Subscribe to our Newsletter | To Post On IoT Central, Click here


Apps and Tools (144)

Guest post by Preston Tesvich. This article originally appeared here.

Let’s say you’re in the planning phase of an IoT project. You have a lot of decisions to make, and maybe you're not sure where to start:

 

In this article, we focus on a framework of how you can think about this problem of standards, protocols, and radios. 

The framework of course depends on if your deployment is going to be internal, such as in a factory, or external, such as a consumer product. In this conversation we’ll focus on products that are launching externally to a wider audience of customers, and for that we have a lot to consider.

Let’s look at the state of the IoT right now— bottom line, there’s not a standard that’s so prolific or significant that you’re making a mistake by not using it. What we want to do, then, is pick the thing that solves the problem that we have as closely as possible and has acceptable costs to implement and scale, and not worry too much about fortune telling the future popularity of that standard.  

So, it first comes down to technical constraints:
    - What are the range and bandwidth requirements? 
    - How many nodes are going to be supported in the network?
    - What is the cost for the radio? 


That radio choice has big impacts—not only is it a hefty line item on your BOM on its own, it’s also going to determine the resources that the device needs as well. For example, if you have a WiFi radio at the end, there’s considerable CPU and memory expectations, whereas if we have BLE or some mesh network, it’ll need a lot less. There’s infrastructure scaling costs to consider as well. If we go WiFi: is there WiFi infrastructure already in place where this is being deployed? How reliable is it? If we’re starting from scratch, what’s the plan for covering a large area? That can become very costly, especially if you’re using industrial grade access points, so it’s important to consider these effects that are downstream of your decision.

Zooming in on specific standards

In our opinion, the biggest misconception we find: “Isn’t there going to be one standard to rule them all?” There’s no future of that, and it’s not just because we’re never going to all agree on stuff as an industry, it’s because in many cases different standards aren’t solving the same problems differently, they are solving different problems. So understanding that, we can now look at what each protocol attempts to solve and where they live on the OSI model, or "the stack."

 

MQTT

Some would suggest that it is a full protocol to do communication from a device to a server, but it’s not quite that. MQTT is used as a data format to communicate to something, and that payload can be sent over any transport, be it WiFi, mesh, or some socket protocol. What it tries to solve is to define a way to manipulate attributes of some thing. It centers around reading and writing properties, which lends itself very well to an IoT problem. It certainly saves development time in some regards, but depending on how strictly you’re trying to implement it, it may cost you more development time. As soon as you one-off any part of it, you have to document it really well, and at some point you approach a time and cost factor where implementing your own payload scheme may be a better option.

Is it prolific enough to where you should absolutely use it? No, it hasn't reached that level, and it won’t likely reach that level. What it is right now is a convenient standard for device-direct-to-cloud where we don’t control both ends because it gives some measure of a common language that we can agree on; however, the thing to keep in mind is that most of the time it does in fact need additional documentation—what properties are being read/written and what the exact implementation looks like—ultimately, you’re not getting out of a lot of work using MQTT.

Zigbee and Z-wave

Also starting at the network layer, Zigbee and Z-wave are the big incumbents everyone likes for mesh networking. They attempt to solve two problems: provide a reasonable specification to move packets from one place to another on a mesh network, and actually suggest how those packets should be structured; so, they both reach up higher in the stack. And that's the part hinders their futures. For example, Zigbee uses a system called profiles, which are collections of capabilities, such as the smart energy profile or the home automation profile. When a protocol gets so specific as to say ‘this is what a light bulb does’ it’s pretty difficult to implement devices that aren’t included in the profile. While there are provisions for custom data, you’re not really using a cross-compatible spec at that point—you’re basically off the standard as soon as you’re working with a device not defined in the profile.  

The other consideration with these two is that they are both routed mesh networks. We use one node to communicate with another node using intervening nodes. In other words, we can send a message from A to B to C to D, but in practice we’ve sent a message from A to D. As routed meshes, each node understands the path the message needs to take, and that has an in-memory cost associated with it. While Z-wave and Zigbee have a theoretical limit of 65,535 nodes on a network (the address space is a 16-bit integer), the practical limit is closer to few hundred nodes, because these devices are usually low power, low memory devices. The routing also has a time cost, so a large mesh network may manifest unacceptable latency for your use case. Another consideration, especially if you’re launching a cloud controlled consumer product, is that these mesh networks can’t directly connect to the internet—they require an intervening bridge (a.k.a gateway, hub, edge server) to communicate to the cloud.   

A final caveat is that Z-wave is a single source supplier—the radios are made and sold by Zensys, so you have to buy it from them. Zigbee has a certification process, and there are multiple suppliers of the radio, from Atmel to TI.

Bluetooth

You really just can’t compete with the amount of silicon being shipped based on Bluetooth. 10,000 unique SKUs were launched in Bluetooth in 2014. Other than WiFi, there’s nothing that compares in terms of adoption. Bluetooth was originally designed for  ‘personal area networks,’  with the original standard supporting 7 concurrent devices. And now we have Bluetooth low energy (BLE) which has a theoretically infinite limit. BLE did a ton to optimize around IoT challenges. They looked heavily at the amount of energy required to support a communication. They considered every facet of "low energy," not just the radio-- they looked at data format, packet size, how long the radio needed to be on to transmit those packets, how much memory was required to support it, what the power cost was for that memory, and what the protocol expects of the CPU, all while keeping overall BOM costs in mind. For example, they figured out that the radio should only be on for 1.5ms at a time. That’s a sweet spot—if you transmit for longer, the components heat up and thus require more power. They also figured out that button cell batteries are better at delivering power in short bursts as opposed to continuously. Further, they optimized it to be really durable against WiFi interference because the protocols share the same radio space (2.4GHz).

And then CSR came along and implemented a mesh standard over Bluetooth. Take all the advantages afforded with BLE, and then get all the benefits of a mesh network. The Bluetooth Mesh is flood mesh, meaning instead of specific routing to nodes, a message is sent indiscriminately across all nodes. This scales better than routed mesh because there’s no memory constraints. It’s a good solution for many problems in the IoT and at scale is probably going to be the lowest cost to implement. 

Thread

An up and coming standard that’s built on top of the same silicon that powers the Zigbee radio. It solves the problem of mesh nodes not being able to communicate directly to the cloud by adding IPv6 support, meaning that nodes on the network can make fully qualified internet requests. There’s a lot of weight behind this standard. Google seems to think it’s interesting enough to make their own protocol (known as “Weave”) on top of it. And then there’s Nest Weave which is some other version of Google Weave. As it stands, it takes a long time for a standard to really take hold-- you can immediately see how the story with Thread is a little muddier, which will not help its adoption. It’s also solving a problem that it just doesn’t seem that many devices have. Let’s take sensors as an example. Do these low power, lightweight, low cost, low memory, low processing, fairly dumb devices NEED to make internet requests directly? With Thread, each node now knows a lot more about the world—where your servers are for example, and maybe they shouldn’t be concerned with those things, because not only do the requirements of the device increase, but now the probability and frequency of having to update them in the field goes way up. When it comes to the actual sensors and other endpoints, philosophically you want minimize those responsibilities, except in special cases where offline durability, local processing and decision making is required (this is called fog computing).

When Thread announced their product certification last year, only 30 products submitted. Another thing to note about Thread's adoption is that the mesh-IPv6 problem has been solved before-- there’s actually a spec in Bluetooth 4.2 that adds IPv6 routing to Bluetooth, but very few people are using it. Although Nordic Semiconductor thought it was going to be a big deal and went ahead and implemented it first, it just hasn’t come up much in the industry—that happened Q4 2014 and no one’s talking about it.

One thing Thread does have going for it is that it steps out of defining how devices talk to each other, and how devices format their data—doing this makes it more future proof. This is where Weave comes in, because it does suppose how the data should be structured. So basically a way to look at it is that Weave + Thread = direct Zigbee/Z-wave competitor. We haven’t seen anyone outside of Google really take an initiative on Weave, other than Nest who have put a good marketing effort into making it look like they are getting traction with it.

AllJoyn

Other protocols live higher in the stack and remain agnostic at the network layer. The most well known of these is probably Qualcomm’s Alljoyn effort. They have the Allseen Alliance, although their branding is a bit murky—Allplay, AllShare, etc. We’ve seen some traction with it, but not a ton-- the biggest concern that it’s fighting is that it’s a really open ended protocol, loosely defined enough that you’re really not going to build something totally interoperable with everything else. That’s a big risk for product teams. If there aren’t enough devices in the world that speak that language, then why do I need to speak it? That said, LIFX implemented it, and it worked really well for them, especially since Windows implemented it as well. Now it’s part of Windows 10—there’s a layer specifically for AllJoyn stuff and it seems to do well. There's evidence with AllJoyn that you can bring devices to the table that don’t know anything about each other and get some kind of durable interoperability. However, at a glance, it seems complicated—the way authorization is dealt with and the way devices need to negotiate with each other. There really isn't runaway adoption

IEEE’s WiFi

They’ve ruled the roost with their 802.11 series. B then G then A, and now we have AC. 802.11 has been really good at being simple to set up and being high bandwidth. It doesn’t care about power consumption, it’s more concerned with performance because it’s meant to be a replacement for wires. Almost 2 years ago, they announced 802.11 AH which they’ve branded as HaLow, which attempts to address power, range, and pairing concerns of classic WiFi. Most WiFi devices are not headless ("headless" - no display or other input), they have a rich user interface—meaning we can login and configure them to connect to WiFi. Pairing headless devices has been a very tedious process. With HaLow, they’re solving two problems—how do we get things on easier, and how do we decrease the expectations (particularly power) of the device running the radio. It’s too early to know what type of traction this will get, but IEEE has a great track record at standards adoption.

LoRa and SIGFOX

More like: LoRa vs. SIGFOX. With these protocols we’re looking at how to connect things over fairly long distances, such as in smart city applications. LoRaWAN is an open protocol that's following a bottoms-up adoption strategy. SIGFOX is building out the infrastructure from the top down, and handing APIs to their customers. In that way, SIGFOX is more like a service. It'll be interesting to see the dance-off between these two as the IoT is adopted in these more public-type applications. 

That’s the body of standards that need to be addressed. There’s a ton more, but we don’t see them as exciting for the IoT today.

- P

Read more…

internet of things

By Ben Dickson. This article originally appeared here

The Internet of Things is the connection of things beyond your computer and laptop – physical things – to the internet. It has enormous potential for both customers and manufacturers. It’s today’s buzzword. And it’s everywhere. It will soon invade our lives in ways that were unimaginable before, and there’s no stopping it. If you’re a consumer, IoT might have become part of your life without you knowing it. And if you’re a manufacturer, you should start thinking about making your products “smart,” lest you lose the competitive edge against your rivals.

That’s the basic mindset that drives manufacturers in virtually every industry toward integrating internet connectivity into their newest products without thinking about the requirements, implications, challenges and pitfalls. And that’s where they stop: connectivity.

I would call it “barely scratching the surface,” but I think even that would be an overstatement. In reality, it’s worse than that. A recent Forrester research commissioned by Xively showed that 62 percent of companies are just looking to differentiate their brand through adding connectivity to their products. But with more and more companies creating connected devices, connectivity per se is no longer a unique differentiator.

No wonder we’re seeing vulgar references being made to the IoT since a lot of new IoT devices end up creating more trouble and headaches than utility and efficiency. And this is the phenomenon that is supposed to trigger the next digital revolution.

Creating a successful IoT project is much more than just linking your next product to the internet. Here is what you should know before getting engaged in the manufacturing of your next smart appliance.

Security and privacy

One of the main failings of IoT manufacturers is to take security and privacy issues into account before developing and shipping their products. The result is fridges that leak Gmail credentialslight bulbs that leak Wi-Fi networkstoys that spy on kidsTVs that spy on viewers, and the list goes on.

As long as security comes as an afterthought and not as a main area of focus, we’ll be seeing IoT being referred to as one of the most insecure sectors of the tech industry.

Aside from security, privacy is another serious topic of content in IoT. With so much personal data being collected by IoT devices, manufacturers must – and unfortunately don’t – consider the privacy implications before shipping products. Much of this data is subject to regulations such as HIPAA.

So sensitive data must be encrypted whether it’s on the device or in the cloud or while it’s being transferred. Sensitive data shouldn’t be stored at all. Data that is being shared with third parties must be vetted and anonymized.

Users should be able to opt out of data collection programs and should be fully informed about the type of data that is being collected.

Long story short, there are a lot of security and privacy complexities that you need to consider and plan for before diving into the project.

User experience and compatibility

What kind of technologies will this device of yours be using? Is it compatible with other appliances or gadgets that potential consumers will have installed in their home? Do they need to purchase and install a new router just because of your product? Is it really necessary that they install a new mobile app for your device only?

What are the possible scenarios where users would want to connect their devices through platforms such as IFTTT? Does your IoT platform support that?

These are all important questions that you need to answer in regard to your IoT product.

It is imperative that your product seamlessly blend into the connected life of your clients without adding complexities, frustration and extra steps. Also, it is important that your technology be able to work in a legacy environment, so it should be able to continue functioning disconnected. It would be very embarrassing if your customers wouldn’t be able to turn on the lights because they’ve lost internet connectivity (I’ve discussed some potential solutions to this problem here and here).

The point is, if your device ends up being a disconnect island in the IoT ecosystem of your consumers that has to be managed separately, there’s a likely chance that the consumers will abandon it and take their chances with some other brand.

So you should think out of the box and in the broader scope when designing your IoT product. Also plan for the future, and if you’ll be manufacturing other IoT products in the same line in the future, consider how these devices will correlate and how you can standardize your IoT product line to improve compatibility.

Data management

The true potential of the IoT lies in its ability to gather data, glean insights and make smart decisions which lead to improved user experience, better efficiency, costs savings, etc. But unfortunately, most companies stop at the gathering phase, piling up reams of data in their cloud servers and making minimal use of it. According to the Xively report, only about one third of firms are leveraging captured connected device data to provide insight to internal stakeholders and partners, personalize interactions with customers, or profile and segment customers.

This is a missed opportunity for leveraging customer data, as most companies focus their time on just connecting products rather than creating actionable insights from the captured data. Companies should leverage third-party analytics and machine learning services to do a host of activities such as integrating data gathered from IoT devices with previous data they have about their customers. This can enable them to better segment their customers and categorize them based on their preferences and device usage.

Also, data gathered from devices can provide the best feedback to improve existing products. By examining how devices are being used, manufacturers can find the strengths and failings of their products and make software and hardware design decisions to improve their current and future products. Naturally, your first IoT device won’t contain all the relevant features and characteristics that end users will expect form a smart appliance. Device data can help you correct your development path in the future.

There’s much more

These are just some of the considerations that can help you get your feet wet with IoT design and development challenges. The full list can be much more comprehensive. For instance, I didn’t even touch upon the issue of support and management, which deals with updating mechanisms and customer support.

What challenges do you face when designing your IoT products? How do you deal with them? Please share with us in the comments section.

Read more…

The power of big data, analytics and machine learning have created unique opportunities in the e-commerce industry. Thanks to data-driven enhancements to ads, upselling and cross-selling, online shoppers are able to get “what they want, when they want it.”

This transformation has had a direct and positive impact on business efficiency, driving more sales and improving customer satisfaction. But it has also had the adverse effect of widening the gap between online and brick-and- mortar businesses, and has faced the retail industry with higher shopper expectations and unprecedented challenges.

However, the advent and development of the Internet of Things (IoT) and the widespread use of mobile devices and mobile apps can help overcome these challenges. Thanks to microprocessors and ubiquitous internet connectivity, smart devices can be deployed everywhere and on everything, from point of sales systems to dressing rooms.

Thanks to microprocessors and ubiquitous internet connectivity, smart devices can be deployed everywhere and on everything, from point of sales systems to dressing rooms.

This enables retailers to gather and analyze data like never before, and to interact with each shopper in a unique and personalized way. Here’s how every aspect of a retail business can benefit from IoT technology and mobile apps, effectively improving sales, cutting costs and drawing customers back to the store.

Supply chain and inventory management

Inventory management problems account for some of the biggest expenditures and losses in retail stores. According to a report by McKinsey, inventory distortion, including overstock, stockouts, and shrinkage, cost retailers a yearly $1.1 trillion worldwide. In the U.S., shrinkage alone is hitting retailers with $42 billion in losses every year, 1.5 percent of total retail sales.

Thanks to IoT, retailers will be able to not only improve inventory control within the store but also expand it to the supply chain. Tracking of goods no longer starts at the store’s receiving dock – it begins at the point of manufacturing.

Better handling of the supply chain

With RFID tags placed on goods and environmental sensors in transportation vehicles, retailers will be able to trace the goods they purchase and their treatment and conditions throughout the supply chain. Information gathered from devices will be analyzed in the cloud and rule-based notifications and alerts can be sent to desktop and mobile apps in order to inform employees and staff members of events that must be acted upon.

The enhanced control will enable suppliers to reduce product damage throughout the journey to retail outlets. This will prove especially useful for the shipping of perishable and temperature sensitive inventory.

Retailers can also leverage IoT technologies such as RFID to track products through the extended supply chain, i.e. after the product has been sold. Having data and improved visibility will streamline otherwise-difficult tasks such as critical product recalls.

Improving in-store inventory tracking

One of the perennial problems retailers are faced with is the lack of accurate inventory tracking. Store shelves aren’t replenished on time; items are misplaced in shelves; sales associates aren’t able to locate items customers are looking for; order management is abysmal, leading to excessive purchase orders to avoid stock-outs. The results are higher inventory costs, lost worker productivity, mishandled stocking, potentially empty shelves and missed sales opportunities.

IoT technology can tackle these problems by bringing more visibility into the location of inventory items and offering more control. By deploying an inventory management system that is based on RFID chips, sensors and beacons, physical assets can be directly synced with database servers. Additional technologies such as store shelf sensors, digital price tags, smart displays and high-resolution cameras combined with image analysis capabilities can further help enhance the control of retailers on goods located at store shelves and in the back storage.

Subsequently retailers can better ensure inventory is adequately stocked, and when stock levels become low, reorder quantities can be suggested based on analytics made from POS data. According to the McKinsey report, reducing stock-outs and overstocks can help lower inventory costs by as much as 10 percent.

The use of IoT can also reduce missed sales opportunities attributed to poorly stocked shelves. When customers are unable to find what they’re looking for, they’ll take their business elsewhere. This can happen while the desired item is actually available in the backroom or displaced to some other shelf. Sales associates can quickly track items by their RFIDs using their mobile devices and beacons installed across the store. They can also receive timely alerts for misplaced items and emptied shelves in order to minimize customer mishaps. Improved on-shelf availability can improve sales by as much as 11 percent, the McKinsey report states.

Improved on-shelf availability can improve sales by as much as 11 percent

Reducing shrinkage and fraud

Shrinkage and fraud is an ever-present challenge in retail stores, whether from customers or employees. IoT can help curb the theft of items by adding a layer of visibility and traceability to inventory items. RFIDs, smart-shelves and camera feeds combined with sophisticated machine learning technology can paint a clearer picture of what takes place in-store, detect suspicious movement and determine whether items have been obtained through legal means.

Also, knowing that items are being tracked will discourage patrons and employees from resorting to the pilfering of goods. This is a huge improvement from traditional systems which rely on human monitoring, point-of- sale data and receipts to validate the sale of goods.

Customer experience

One of the benefits of online shopping is being able to push products and offers to customers instead of waiting for them to find them on their own. This helps to catch the attention of customers at the right moment and improve sales dramatically.

IoT will help enhance the brick-and- mortar experience to this level by helping gather data, perform analysis and make the best decisions for retail stores.

Optimizing product placement

Trying to figure out how customers navigate store isles is valuable information. Retailers always try to lay out their stores in order to maximize exposure to customers and improve sales. In the pre-IoT days, this has been done through human observation, educated guesses, random experimentation and manual sales correlation.

But now, thanks to data gathered from RFID chips, IoT motion detection sensors, beacons and video analytics, retailers can gather precise data from customer movement patterns and identify premium traffic areas. IoT makes is possible to learn how customers interact with specific items and discover which items are abandoned. Changes to store layouts can be automatically correlated to customer behavior changes and sales figures in order to perform precise A/B testing on tweaks and modifications.

Optimized use of in-store staff

Being able to identify customers that need help, and tending to their needs in time is an important factor in closing sales and improving conversion rates. But in-store staff can only watch so many customers at once, and in many cases the presence of a salesperson can be misinterpreted and considered offensive by customers.

IoT helps deal with this problem without disrupting the customer experience. Motion detection sensors, cameras and facial expression recognition algorithms can help identify customer who have been standing too long in one location and are manifesting confusion and ambivalence. The IoT ecosystem can then notify a nearby sales associate through a mobile or smart watch app. This way, shoppers get a better experience because they aren’t kept waiting, and retailers optimize their in-store staff.

Personalized offers and promotions

Banner ads and product suggestions that are customized based on browsing and purchasing history are one of the features that give online shopping channels the edge over brick-and- mortar retail. Cross-selling and upselling have become an important source of revenue for online sellers.

IoT can help retailers collect data and make offers to customers that will put them on par with their online counterparts. RFID chips, sensors and beacons can gather data about customer interactions with store items. The data can be analyzed by machine learning solutions and used to push extra information, customer reviews, recommendations and special offers on smart displays that are installed in stores.

Mobile apps can help move the experience to the next level. While customers interact with in-store items, the IoT ecosystem can merge the collected insights with their online product browsing history in order to provide useful information, offer loyalty programs and offer smarter suggestions for upsells.

Mobile apps in retail

IoT devices and sensors help collect data and glean insights from virtually every physical object and event that takes place in retail stores. But it is with mobile apps that IoT becomes a hands-on experience, especially in retail where most of the tasks are performed in field rather than behind a desk.

it is with mobile apps that IoT becomes a hands-on experience, especially in retail where most of the tasks are performed in field rather than behind a desk.

With a fully featured mobile app (or a suite of app for mobile devices and wearables) retailers can make sure that everyone within the retail chain has access to the data they need anytime, anywhere, in order to become more efficient at their jobs. This includes salespersons, inventory managers, suppliers and everyone else.

Mobile apps will also improve the customer experience as it will drive loyalty and enable customers to engage in a more personalized experience with retail stores and the smart gadgets that are installed in them.

Conclusion

With actionable insights offered by IoT-powered solutions, retailers will be able to offer customers what they actually want through a digital, connected and personalized experience. The gamut of data-driven and cloud-powered technology that is available for the retail sector to take advantage of can help merge the benefits of online and brick-and- mortar shopping experience. Eventually IoT will become the de facto standard and reinvent retail as we know it today.

Read about how Mokriya develops solutions for IoT problems

Read more…

Does IoT Need Wireless?

By Wade Sarver. This article originally appeared here

Hell yeah! Don’t get me wrong, you could use CAT 5 to connect most of this stuff, but the idea is to have the equipment everywhere and talking all the time, or at least when we need to. They need to be wireless controlled for it to work properly and to be autonomous. What fun would a drone be if you needed to have a copper line connected to it. The FCC laid out their plan to sunset copper lines. I did a lot of work on them but I won’t miss them because wireless is so cool! If you like copper so much, then put that smartphone down and use a landline, if you can find one.

So, back to IOT, (Internet of Things), they rely on wireless connections for more than convenience. This is how the machine to machine, M2M, really take off. Whether it’s to control valves for a water company or to read your electric meter or to control natural gas flow, you need to have connectivity everywhere. We just need to define what that connectivity will be. It could be the standard carrier networks, LTE really. That is going to be key for so much of this. But most of these systems will need much less bandwidth.

Small data networks, that sounds crazy, right? NOT! You see the new networks are built for larger packets, so they are so inefficient, and too expensive, for a simple command to open or close a valve. LTE and Wi-Fi seem like overkill for these applications, although they are everywhere and the most convenient to work with, especially Wi-Fi, it’s in your house and would be a great way for your smart home full of IOT devices to talk to your smartphone and the real world.

That is why the LTE format may not be the best for IOT, although it would be everywhere so by default it may be the technology of choice.

So how will wireless IOT work?

They need something for outdoor communication like LoRa, the low-bandwidth system. There is a LoRa Alliance, if you want to read more about what they are up to. Another good article on LoRa is here where they go into detail about how it works. What they explain is that they are planning to use the spectrum that is left behind, with smaller bandwidth. They way the Semtech chip works is that they utilize spectrum that is sub giga-hertz, like 109MHz, 433MHz, 866MHz, and 915MHz where they have smaller amounts of spectrum. They need to stay away from the license free spectrum because it might interfere.

There is another format called SigFox for outdoor communication. Again, made for very small packets of data. I found information at here if you want more information but here is what I got out of it. They are using the 915MHz spectrum (ISM band license free), using 2 types of Phase Shift Keying, PSK. This supposedly will help get the data through the noise. I am not sure what the coverage would be for something like this but I would bet its very limited. This is a low power, wide area, (LPWA) network. A good article on SigFox is here if you want to learn how they plan to deploy. I am told that they already have several deployments in the USA, although I don’t know of any personally.

Now, for the smart home, inside a building, or the smart office, you could use Wi-Fi, ZigBee, Z-Wave, Bluetooth, or something proprietary. We all know Wi-Fi and Bluetooth, right? It’s on your smartphones and in your homes. What we don’t know if ZigBee and Z-Wave.

What is ZigBee for IOT? Well, according to the ZigBee Alliance it is a wireless language that is used to connect devices, which is such a generic explanation that I could use for any wireless protocol. Come on!

So I went into Wikipedia at https://en.wikipedia.org/wiki/ZigBee where they give a much better explanation. It is line of site, LOS, and very short-range. It works in the ISM band, just like Wi-Fi, (2.4GHz in most countries but also in 915MHz in USA and Australia, 784MHz in China, 868MHz in Europe). The data rate is very small, remember I said smaller packets are all you need? This is made for very small and efficient bursts of data. They also support mesh networking. Mesh means that the devices not only connect to the hub but they can repeat the signal to each other forming a mesh. This is a great way to extend coverage if you don’t need massive bandwidth.

What is Z-Wave for IOT? Z-Wave takes ZigBee and makes some enhancements. It specifically works in the 908.42GHz range in the USA and 868.42MHz band in Europe. For a great explanation go here but its made for very small networks in the home. Find more at http://www.z-wave.com/ but I haven’t heard much more on this except that they have a version that will work with the Apple iWatch.

As you can see there are many technologies to roll out the IOT format. I don’t really know if there is a clear winner but I think it depends on the need. The wireless backhaul will come down to a chip they add to the device based on need, coverage, and cost. I could see someone using all of the technologies in a device to get the coverage they need, like maybe utility meters. That would make sense because it would be a one-time up front cost. However, for the in home stuff, cheap is what they need. I seriously don’t see people putting in a new network in their homes if they don’t have to but many companies will say you need a “hub” which will be the special format switch that their devices will, in theory, talk to the Wi-Fi in their homes. I already see it but it looks like they want to sell more devices in the home. So maybe high-end stuff will need the hub. I could see the hub as another line of defense in security, where if someone hacks your Wi-Fi and/or cable router then they would need to get by another device to get to your thermostat or light switches.

However, for an outdoor network I could see a dedicated network taking off for several reasons, cost reliability, and security. It costs money to pay the carrier a fee every month when you have a small low data device on it when you could put one of the cheaper hotspots in a space to connect your devices. Again, it really comes down to cost and reliability. Many will say they want security, but how secure can they really be?

A few more articles that may interest you:

http://pages.silabs.com/rs/silabs/images/Wireless-Connectivity-for-IoT.pdf?mkt_tok=3RkMMJWWfF9wsRoguKjNZKXonjHpfsX86%2B4rWKK3lMI%2F0ER3fOvrPUfGjI4DSsJkI%2BSLDwEYGJlv6SgFTLPBMbNsz7gOXBg%3D

http://postscapes.com/internet-of-things-protocols/

https://en.wikipedia.org/wiki/LPWAN

http://www.semtech.com/wireless-rf/internet-of-things/

https://www.micrium.com/iot/devices/

http://www.networkcomputing.com/internet-things/10-leaders-internet-things-infrastructure/1612927605

https://www.thethingsnetwork.org/

So let me know what you think, email [email protected] when you think of something to say!

Photo Credit here.

Read more…

For IoT and M2M device security assurance, it's critical to introduce automated software development tools into the development lifecycle. Although software tools' roles in quality assurance is important, it becomes even more so when security becomes part of a new or existing product's requirements.

Automated Software Development Tools

There are three broad categories of automated software development tools that are important for improving quality and security in embedded IoT products:

  • Application lifecycle management (ALM): Although not specific to security, these tools cover requirements analysis, design, coding, testing and integration, configuration management, and many other aspects of software development. However, with a security-first embedded development approach, these tools can help automate security engineering as well. For example, requirements analysis tools (in conjunction with vulnerability management tools) can ensure that security requirements and known vulnerabilities are tracked throughout the lifecycle.  Design automation tools can incorporate secure design patterns and then generate code that avoids known security flaws (e.g. avoiding buffer overflows or checking input data for errors). Configuration management tools can insist on code inspection or static analysis reports before checking in code. Test automation tools can be used to test for "abuse" cases against the system. In general, there is a role for ALM tools in the secure development just as there is for the entire project.
  • Dynamic Application Security Testing (DAST): Dynamic testing tools all require program execution in order to generate useful results. Examples include unit testing tools, test coverage, memory analyzers, and penetration test tools. Test automation tools are important for reducing the testing load on the development team and, more importantly, detecting vulnerabilities that manual testing may miss.
  • Static Application Security Testing (SAST): Static analysis tools work by analyzing source code, bytecode (e,g, compiled Java), and binary executable code. No code is executed in static analysis, but rather the analysis is done by reasoning about the potential behavior of the code. Static analysis is relatively efficient at analyzing a codebase compared to dynamic tools. Static analysis tools also analyze code paths that are untested by other methods and can trace execution and data paths through the code. Static analysis can be incorporated early during the development phase for analyzing existing, legacy, and third-party source and binaries before incorporating them into your product. As new source is added, incremental analysis can be used in conjunction with configuration management to ensure quality and security throughout. 

Figure 1: The application of various tool classes in the context of the software development lifecycle.

Although adopting any class of tools helps productivity, security, and quality, using a combination of these is recommended. No single class of tools is the silver bullet[1]. The best approach is one that automates the use of a combination of tools from all categories, and that is based on a risk-based rationale for achieving high security within budget.

The role of static analysis tools in a security-first approach

Static analysis tools provide critical support in the coding and integration phases of development. Ensuring continuous code quality, both in the development and maintenance phases, greatly reduces the costs and risks of security and quality issues in software. In particular, it provides some of the following benefits:

  • Continuous source code quality and security assurance: Static analysis is often applied initially to a large codebase as part of its initial integration as discussed below. However, where it really shines is after an initial code quality and security baseline is established. As each new code block is written (file or function), it can be scanned by the static analysis tools, and developers can deal with the errors and warnings quickly and efficiently before checking code into the build system. Detecting errors and vulnerabilities (and maintaining secure coding standards, discussed below) in the source at the source (developers themselves) yields the biggest impact from the tools.
  • Tainted data detection and analysis: Analysis of the data flows from sources (i.e. interfaces) to sinks (where data gets used in a program) is critical in detecting potential vulnerabilities from tainted data. Any input, whether from a user interface or network connection, if used unchecked, is a potential security vulnerability.  Many attacks are mounted by feeding specially-crafted data into inputs, designed to subvert the behavior of the target system. Unless data is verified to be acceptable both in length and content, it can be used to trigger error conditions or worse. Code injection and data leakage are possible outcomes of these attacks, which can have serious consequences.
  • Third-party code assessment: Most projects are not greenfield development and require the use of existing code within a company or from a third party. Performing testing and dynamic analysis on a large existing codebase is hugely time consuming and may exceed the limits on the budget and schedule. Static analysis is particularly suited to analyzing large code bases and providing meaningful errors and warnings that indicate both security and quality issues. GrammaTech CodeSonar binary analysis can analyze binary-only libraries and provide similar reports as source analysis when source is not available. In addition, CodeSonar binary analysis can work in a mixed source and binary mode to detect errors in the usage of external binary libraries from the source code. 
  • Secure coding standard enforcement: Static analysis tools analyze source syntax and can be used to enforce coding standards. Various code security guidelines are available such as SEI CERT C [2] and Microsoft's Secure Coding Guidelines [3]. Coding standards are good practice because they prevent risky code from becoming future vulnerabilities. As mentioned above, integrating these checks into the build and configuration management system improves the quality and security of code in the product.

As part of a complete tools suite, static analysis provides key capabilities that other tools cannot. The payback for adopting static analysis is the early detection of errors and vulnerabilities that traditional testing tools may miss. This helps ensure a high level of quality and security on an on-going basis.

Conclusion

Machine to machine and IoT device manufacturers incorporating a security-first design philosophy with formal threat assessments, leveraging automated tools, produce devices better secured against the accelerating threats on the Internet. Modifying an existing successful software development process that includes security at the early stages of product development is key. Smart use of automated tools to develop new code and analyze existing and third party code allows development teams to meet strict budget and schedule constraints. Static analysis of both source and binaries plays a key role in a security-first development toolset. 

References

  1. No Silver Bullet – Essence and Accident in Software Engineering, Fred Brooks, 1986
  2. SEI CERT C Coding Standard,
  3. Outsource Code Development Driving Automated Test Tool Market, VDC Research, IoT & Embedded Blog, October 22, 2013

 

Read more…

Originally Posted by:  

With the announcement of the Cisco Solution for LoRAWAN™, Service Providers have an integrated solution that enables them to extend their network reach to where they’ve never gone before – i.e., offering IoT services for devices and sensors that are battery powered, have low data rates and long distance communications requirements. The solution opens new markets and new revenue streams for Service Providers, and can be deployed in a wide range of use cases in Industrial IoT and Smart City applications such as:

  • Asset Tracking and Management
  • Logistics
  • Smart Cities (e.g., smart parking, street lighting, waste management, etc.)
  • Intelligent buildings
  • Utilities (e.g., water and gas metering)
  • Agriculture (e.g., soil, irrigation management)

AU43170

Our Cisco Mobile Visual Networking Index estimates that while LoRa is in its early stages now, these types of Low Power Wide Area connectivity means will quickly gain traction and that by 2020, there will be more than 860 million devices using it to connect.  One of the reasons for such forecasted aggressive adoption, especially in North America and Western Europe, is that LoRa® works over readily available unlicensed spectrum. Cisco is a founding Board member of the LoRa® Allianceformed in January, 2015, with a goal to standardize LPWA Networks in order to stimulate the growth of Internet of Things (IoT) applications.

Cisco has been working with a number of Mobile Operators who are trialing and deploying LoRa® networks to target new low-power consumption IoT services such as metering, location tracking and monitoring services. Many Mobile Operators are looking at LoRa® as complementary to NarrowBand IOT (NB-IOT), an upgrade to current mobile networks that drops the transmit power and data rates of the LTE standard to increase battery life. As NB-IOT networks, devices, and ecosystems will not be commercialized until 2017, LoRa® gives Operators (and all SPs, in fact) a way to gain a head-start on offering new IoT services based on various new low cost business models.

Cisco’s approach to IoT is to deliver integrated solutions that enable SPs to support different class of services aligned with specific pricing models across unlicensed (Wi-Fi, LoRa) and licensed (2G/3G/LTE, and soon, NB-IoT) radio spectrum as demanded by the IoT application. Our multi-access network strategy for IoT is complemented by the Cisco Ultra Services Platform (USP) – our comprehensive, virtualized services core, which includes mobile packet core, policy and services functions. Cisco USP delivers the scalability and flexibility that Operators focusing on IoT need as more and varied “things” get connected to their networks.

Cisco continues to integrate and evolve solutions such as LoraWAN™ to help Service Providers of all types capitalize on new IoT opportunities and transform into next-generation IoT Service Providers.

Read more…

Originally Posted and Written by: Michelle Canaan, John Lucker, & Bram Spector

Connectivity is changing the way people engage with their cars, homes, and bodies—and insurers are looking to keep pace. Even at an early stage, IoT technology may reshape the way insurance companies assess, price, and limit risks, with a wide range of potential implications for the industry.

Insurers’ path to growth: Embrace the future

In 1997, Progressive Insurance pioneered the use of the Internet to purchase auto insurance online, in real time.1 In a conservative industry, Progressive’s innovative approach broke several long-established trade-offs, shaking up traditional distribution channels and empowering consumers with price transparency.

This experiment in distribution ended up transforming the industry as a whole. Online sales quickly forced insurers to evolve their customer segmentation capabilities and, eventually, to refine pricing. These modifications propelled growth by allowing insurers to serve previously uninsurable market segments. And as segmentation became table stakes for carriers, a new cottage industry of tools, such as online rate comparison capabilities, emerged to capture customer attention. Insurers fought to maintain their competitive edge through innovation, but widespread transparency in product pricing over time created greater price competition and ultimately led to product commoditization. The tools and techniques that put the insurer in the driver’s seat slowly tipped the balance of power to the customer.

This case study of insurance innovation and its unintended consequences may be a precursor to the next generation of digital connectivity in the industry. Today, the availability of unlimited new sources of data that can be exploited in real time is radically altering how consumers and businesses interact. And the suite of technologies known as the Internet of Things (IoT) is accelerating the experimentation of Progressive and other financial services companies. With the IoT’s exponential growth, the ways in which citizens engage with their cars, homes, and bodies are getting smarter each day, and they expect the businesses they patronize to keep up with this evolution. Insurance, an industry generally recognized for its conservatism, is no exception.

IoT technology may still be in its infancy, but its potential to reshape the way insurers assess, price, and limit risks is already quite promising. Nevertheless, since innovation inevitably generates unintended possibilities and consequences, insurers will need to examine strategies from all angles in the earliest planning stages.

To better understand potential IoT applications in insurance, the Deloitte Center for Financial Services (DCFS), in conjunction with Wikistrat, performed a crowdsourcing simulation to explore the technology’s implications for the future of the financial services industry. Researchers probed participants (13 doctorate holders, 24 cyber and tech experts, 20 finance experts, and 6 entrepreneurs) from 20 countries and asked them to imagine how IoT technology might be applied in a financial services context. The results (figure 1) are not an exhaustive compilation of scenarios already in play or forthcoming but, rather, an illustration of several examples of how these analysts believe the IoT may reshape the industry.2

ER_2824_Fig.1

CONNECTIVITY AND OPPORTUNITY

Even this small sample of possible IoT applications shows how increased connectivity can generate tremendous new opportunities for insurers, beyond personalizing premium rates. Indeed, if harnessed effectively, IoT technology could potentially boost the industry’s traditionally low organic growth rates by creating new types of coverage opportunities. It offers carriers a chance to break free from the product commoditization trend that has left many personal and commercial lines to compete primarily on price rather than coverage differentiation or customer service.

For example, an insurer might use IoT technology to directly augment profitability by transforming the income statement’s loss component. IoT-based data, carefully gathered and analyzed, might help insurers evolve from a defensive posture—spreading risk among policyholders and compensating them for losses—to an offensive posture: helping policyholders prevent losses and insurers avoid claims in the first place. And by avoiding claims, insurers could not only reap the rewards of increased profitability, but also reduce premiums and aim to improve customer retention rates. Several examples, both speculative and real-life, include:

  • Sensors embedded in commercial infrastructure can monitor safety breaches such as smoke, mold, or toxic fumes, allowing for adjustments to the environment to head off or at least mitigate a potentially hazardous event.
  • Wearable sensors could monitor employee movements in high-risk areas and transmit data to employers in real time to warn the wearer of potential danger as well as decrease fraud related to workplace accidents.
  • Smart home sensors could detect moisture in a wall from pipe leakage and alert a homeowner to the issue prior to the pipe bursting. This might save the insurer from a large claim and the homeowner from both considerable inconvenience and losing irreplaceable valuables. The same can be said for placing IoT sensors in business properties and commercial machinery, mitigating property damage and injuries to workers and customers, as well as business interruption losses.
  • Socks and shoes that can alert diabetics early on to potential foot ulcers, odd joint angles, excessive pressure, and how well blood is pumping through capillaries are now entering the market, helping to avoid costly medical and disability claims as well as potentially life-altering amputations.3

Beyond minimizing losses, IoT applications could also potentially help insurers resolve the dilemma with which many have long wrestled: how to improve the customer experience, and therefore loyalty and retention, while still satisfying the unrelenting market demand for lower pricing. Until now, insurers have generally struggled to cultivate strong client relationships, both personal and commercial, given the infrequency of interactions throughout the insurance life cycle from policy sale to renewal—and the fact that most of those interactions entail unpleasant circumstances: either deductible payments or, worse, claims. This dynamic is even more pronounced in the independent agency model, in which the intermediary, not the carrier, usually dominates the relationship with the client.

The emerging technology intrinsic to the IoT that can potentially monitor and measure each insured’s behavioral and property footprint across an array of activities could turn out to be an insurer’s holy grail, as IoT applications can offer tangible benefits for value-conscious consumers while allowing carriers to remain connected to their policyholders’ everyday lives. While currently, people likely want as few associations with their insurers as possible, the IoT can potentially make insurers a desirable point of contact. The IoT’s true staying power will be manifested in the technology’s ability to create value for both the insurer and the policyholder, thereby strengthening their bond. And while the frequency of engagement shifts to the carrier, the independent agency channel will still likely remain relevant through the traditional client touchpoints.

By harnessing continuously streaming “quantified self” data, using advanced sensor connectivity devices, insurers could theoretically capture a vast variety of personal data and use it to analyze a policyholder’s movement, environment, location, health, and psychological and physical state. This could provide innovative opportunities for insurers to better understand, serve, and connect with policyholders—as well as insulate companies against client attrition to lower-priced competitors. Indeed, if an insurer can demonstrate how repurposing data collected for insurance considerations might help a carrier offer valuable ancillary non-insurance services, customers may be more likely to opt in to share further data, more closely binding insurer and customer.

Leveraging IoT technologies may also have the peripheral advantage of resuscitating the industry’s brand, making insurance more enticing to the relatively small pool of skilled professionals needed to put these strategies in play. And such a shift would be welcome, considering that Deloitte’s Talent in Insurance Survey revealed that the tech-savvy Millennial generation generally considers a career in the insurance industry “boring.”4 Such a reputational challenge clearly creates a daunting obstacle for insurance executives and HR professionals, particularly given the dearth of employees with necessary skill sets to successfully enable and systematize IoT strategies, set against a backdrop of intense competition from many other industries. Implementing cutting-edge IoT strategies could boost the “hip factor” that the industry currently lacks.

With change comes challenges

While most stakeholders might see attractive possibilities in the opportunity for behavior monitoring across the insurance ecosystem, inevitable hurdles stand in the way of wholesale adoption. How insurers surmount each potential barrier is central to successful evolution.

For instance, the industry’s historically conservative approach to innovation may impede the speed and flexibility required for carriers to implement enhanced consumer strategies based on IoT technology. Execution may require more nimble data management and data warehousing than currently in place, as engineers will need to design ways to quickly aggregate, analyze, and act upon disparate data streams. To achieve this speed, executives may need to spearhead adjustments to corporate culture grounded in more centralized location of data control. Capabilities to discern which data are truly predictive versus just noise in the system are also critical. Therefore, along with standardized formats for IoT technology,5 insurers may see an increasing need for data scientists to mine, organize, and make sense of mountains of raw information.

Perhaps most importantly, insurers would need to overcome the privacy concerns that could hinder consumers’ willingness to make available the data on which the IoT runs. Further, increased volume, velocity, and variety of data propagate a heightened need for appropriate security oversight and controls.

For insurers, efforts to capitalize on IoT technology may also require patience and long-term investments. Indeed, while bolstering market share, such efforts could put a short-term squeeze on revenues and profitability. To convince wary customers to opt in to monitoring programs, insurers may need to offer discounted pricing, at least at the start, on top of investments to finance infrastructure and staff supporting the new strategic initiative. This has essentially been the entry strategy for auto carriers in the usage-based insurance market, with discounts provided to convince drivers to allow their performance behind the wheel to be monitored, whether by a device installed in their vehicles or an application on their mobile device.

Results from the Wikistrat crowdsourcing simulation reveal several other IoT-related challenges that respondents put forward. (See figure 2.)6

ER_2824_Fig.2a

Each scenario implies some measure of material impact to the insurance industry. In fact, together they suggest that the same technology that could potentially help improve loss ratios and strengthen policyholder bonds over the long haul may also make some of the most traditionally lucrative insurance lines obsolete.

For example, if embedding sensors in cars and homes to prevent hazardous incidents increasingly becomes the norm, and these sensors are perfected to the point where accidents are drastically reduced, this development may minimize or eliminate the need for personal auto and home liability coverage, given the lower frequency and severity of losses that result from such monitoring. Insurers need to stay ahead of this, perhaps even eventually shifting books of business from personal to product liability as claims evolve from human error to product failure.

Examining the IoT through an insurance lens

Analyzing the intrinsic value of adopting an IoT strategy is fundamental in the development of a business plan, as executives must carefully consider each of the various dimensions to assess the potential value and imminent challenges associated with every stage of operationalization. Using Deloitte’s Information Value Loop can help capture the stages (create, communicate, aggregate, analyze, act) through which information passes in order to create value.7

The value loop framework is designed to evaluate the components of IoT implementation as well as potential bottlenecks in the process, by capturing the series and sequence of activities by which organizations create value from information (figure 3).

ER_2824_Fig.3

To complete the loop and create value, information passes through the value loop’s stages, each enabled by specific technologies. An act is monitored by a sensor that creates information. That information passes through a network so that it can be communicated, and standards—be they technical, legal, regulatory, or social—allow that information to be aggregated across time and space. Augmented intelligence is a generic term meant to capture all manner of analytical support, collectively used to analyze information. The loop is completed via augmented behavior technologies that either enable automated, autonomous action or shape human decisions in a manner leading to improved action.8

For a look at the value loop through an insurance lens, we will examine an IoT capability already at play in the industry: automobile telematics. By circumnavigating the stages of the framework, we can scrutinize the efficacy of how monitoring driving behavior is poised to eventually transform the auto insurance market with a vast infusion of value to both consumers and insurers.

Auto insurance and the value loop

Telematic sensors in the vehicle monitor an individual’s driving to create personalized data collection. The connected car, via in-vehicle telecommunication sensors, has been available in some form for over a decade.9 The key value for insurers is that sensors can closely monitor individual driving behavior, which directly corresponds to risk, for more accuracy in underwriting and pricing.

Originally, sensor manufacturers made devices available to install on vehicles; today, some carmakers are already integrating sensors into showroom models, available to drivers—and, potentially, their insurers—via smartphone apps. The sensors collect data (figure 4) which, if properly analyzed, might more accurately predict the unique level of risk associated with a specific individual’s driving and behavior. Once the data is created, an IoT-based system could quantify and transform it into “personalized” pricing.

ER_2824_Fig.4

Sensors’ increasing availability, affordability, and ease of use break what could potentially be a bottleneck at this stage of the Information Value Loop for other IoT capabilities in their early stages.

IoT technology aggregatesand communicatesinformation to the carrier to be evaluated. To identify potential correlations and create predictive models that produce reliable underwriting and pricing decisions, auto insurers need massive volumes of statistically and actuarially credible telematics data.

In the hierarchy of auto telematics monitoring, large insurers currently lead the pack when it comes to usage-based insurance market share, given the amount of data they have already accumulated or might potentially amass through their substantial client bases. In contrast, small and midsized insurers—with less comprehensive proprietary sources—will likely need more time to collect sufficient data on their own.

To break this bottleneck, smaller players could pool their telematics data with peers either independently or through a third-party vendor to create and share the broad insights necessary to allow a more level playing field throughout the industry.

Insurers analyze data and use it to encourage drivers to act by improving driver behavior/loss costs. By analyzing the collected data, insurers can now replace or augment proxy variables (age, car type, driving violations, education, gender, and credit score) correlated with the likelihood of having a loss with those factors directly contributing to the probability of loss for an individual driver (braking, acceleration, cornering, and average speed, as figure 4 shows). This is an inherently more equitable method to structure premiums: Rather than paying for something that might be true about a risk, a customer pays for what is true based on his own driving performance.

But even armed with all the data necessary to improve underwriting for “personalized” pricing, insurers need a way to convince millions of reluctant customers to opt in. To date, insurers have used the incentive of potential premium discounts to engage consumers in auto telematics monitoring.10 However, this model is not necessarily attractive enough to convince the majority of drivers to relinquish a measure of privacy and agree to usage-based insurance. It is also unsustainable for insurers that will eventually have to charge rates actually based on risk assessment rather than marketing initiatives.

Substantiating the point about consumer adoption is a recent survey by the Deloitte Center for Financial Services of 2,193 respondents representing a wide variety of demographic groups, aiming to understand consumer interest in mobile technology in financial services delivery, including the use of auto telematics monitoring. The survey identified three distinct groups among respondents when asked whether they would agree to allow an insurer to track their driving experience, if it meant they would be eligible for premium discounts based on their performance (figure 5).11 While one-quarter of respondents were amenable to being monitored, just as many said they would require a substantial discount to make it worth their while (figure 5), and nearly half would not consent.

ER_2824_Fig.5

While the Deloitte survey was prospective (asking how many respondents would be willing to have their driving monitored telematically), actual recruits have been proven to be difficult to bring on board. Indeed, a 2015 Lexis-Nexis study on the consumer market for telematics showed that usage-based insurance enrollment has remained at only 5 percent of households from 2014 to 2015 (figure 6).12

ER_2824_Fig.6

Both of these survey results suggest that premium discounts alone have not and likely will not induce many consumers to opt in to telematics monitoring going forward, and would likely be an unsustainable model for insurers to pursue. The good news: Research suggests that, while protective of their personal information, most consumers are willing to trade access to that data for valuable services from a reputable brand.13 Therefore, insurers will likely have to differentiate their telematics-based product offerings beyond any initial early-adopter premium savings by offering value-added services to encourage uptake, as well as to protect market share from other players moving into the telematics space.

In other words, insurers—by offering mutually beneficial, ongoing value-added services—can use IoT-based data to become an integral daily influence for connected policyholders. Companies can incentivize consumers to opt in by offering real-time, behavior-related services, such as individualized marketing and advertising, travel recommendations based on location, alerts about potentially hazardous road conditions or traffic, and even diagnostics and alerts about a vehicle’s potential issues (figure 7).14 More broadly, insurers could aim to serve as trusted advisers to help drivers realize the benefits of tomorrow’s connected car.15

Many IoT applications offer real value to both insurers and policyholders: Consider GPS-enabled geo-fencing, which can monitor and send alerts about driving behavior of teens or elderly parents. For example, Ford’s MyKey technology includes tools such as letting parents limit top speeds, mute the radio until seat belts are buckled, and keep the radio at a certain volume while the vehicle is moving.16 Other customers may be attracted to “green” monitoring, in which they receive feedback on how environmentally friendly their driving behavior is.

Insurers can also look to offer IoT-related services exclusive of risk transfer—for example, co-marketing location-based services with other providers, such as roadside assistance, auto repairs, and car washes may strengthen loyalty to a carrier. They can also include various nonvehicle-related service options such as alerts about nearby restaurants and shopping, perhaps in conjunction with points earned by good driving behavior in loyalty programs or through gamification, which could be redeemed at participating vendors. Indeed, consumers may be reluctant to switch carriers based solely on pricing, knowing they would be abandoning accumulated loyalty points as well as a host of personalized apps and settings.

For all types of insurance—not just auto—the objective is for insurers to identify the expectations that different types of policyholders may have, and then adapt those insights into practical applications through customized telematic monitoring to elevate the customer experience.

Telematics monitoring has demonstrated benefits even beyond better customer experience for policyholders. Insurers can use telematics tools to expose an individual’s risky driving behavior and encourage adjustments. Indeed, people being monitored by behavior sensors will likely improve their driving habits and reduce crash rates—a result to everyone’s benefit. This “nudge effect” indicates that the motivation to change driving behavior is likely linked to the actual surveillance facilitated by IoT technology.

The power of peer pressure is another galvanizing influence that can provoke beneficial consumer behavior. Take fitness wearables, which incentivize individuals to do as much or more exercise than the peers with whom they compete.17 In fact, research done in several industries points to an individual’s tendency to be influenced by peer behavior above most other factors. For example, researchers asked four separate groups of utility consumers to cut energy consumption: one for the good of the planet, a second for the well-being of future generations, a third for financial savings, and a fourth because their neighbors were doing it. The only group that elicited any drop in consumption (at 10 percent) was the fourth—the peer comparison group.18

Insurers equipped with not only specific policyholder information but aggregated data that puts a user’s experience in a community context have a real opportunity to influence customer behavior. Since people generally resist violating social norms, if a trusted adviser offers data that compares customer behavior to “the ideal driver”—or, better, to a group of friends, family, colleagues, or peers—they will, one hopes, adapt to safer habits.

ER_2824_Fig.7a

The future ain’t what it used to be—what should insurers do?

After decades of adherence to traditional business models, the insurance industry, pushed and guided by connected technology, is taking a road less traveled. Analysts expect some 38.5 billion IoT devices to be deployed globally by 2020, nearly three times as many as today,19 and insurers will no doubt install their fair share of sensors, data banks, and apps. In an otherwise static operating environment, IoT applications present insurers with an opportunity to benefit from technology that aims to improve profits, enable growth, strengthen the consumer experience, build new market relevance, and avoid disruption from more forward-looking traditional and nontraditional competitors.

Incorporating IoT technology into insurer business models will entail transformation to elicit the benefits offered by each strategy.

  • Carriers must confront the barriers associated with conflicting standards—data must be harvested and harnessed in a way that makes the information valid and able to generate valuable insights. This could include making in-house legacy systems more modernized and flexible, building or buying new systems, or collaborating with third-party sources to develop more standardized technology for harmonious connectivity.
  • Corporate culture will need a facelift—or, likely, something more dramatic—to overcome longstanding conventions on how information is managed and consumed across the organization. In line with industry practices around broader data management initiatives,20 successfully implementing IoT technology will require supportive “tone at the top,” change management initiatives, and enterprisewide training.
  • With premium savings already proving insufficient to entice most customers to allow insurers access to their personal usage data, companies will need to strategize how to convince or incentivize customers to opt in—after all, without that data, IoT applications are of limited use. To promote IoT-aided connectivity, insurers should look to market value-added services, loyalty points, and rewards for reducing risk. Insurers need to design these services in conjunction with their insurance offerings, to ensure that both make best use of the data being collected.
  • Insurers will need to carefully consider how an interconnected world might shift products from focusing on cleaning up after disruptions to forestalling those disruptions before they happen. IoT technology will likely upend certain lines of businesses, potentially even making some obsolete. Therefore, companies must consider how to heighten flexibility in their models, systems, and culture to counterbalance changing insurance needs related to greater connectivity.
  • IoT connectivity may also potentially level the playing field among insurers. Since a number of the broad capabilities that technology is introducing do not necessarily require large data sets to participate (such as measuring whether containers in a refrigerated truck are at optimal temperatures to prevent spoilage21 or whether soil has the right mix of nutrients for a particular crop22), small to midsized players or even new entrants may be able to seize competitive advantages from currently dominant players.
  • And finally, to test the efficacy of each IoT-related strategy prior to implementation, a framework such as the Information Value Loop may become an invaluable tool, helping forge a path forward and identify potential bottlenecks or barriers that may need to be resolved to get the greatest value out of investments in connectivity.

The bottom line: IoT is here to stay, and insurers need look beyond business as usual to remain competitive.

The IoT is here to stay, the rate of change is unlikely to slow anytime soon, and the conservative insurance industry is hardly impervious to connectivity-fueled disruption—both positive and negative. The bottom line: Insurers need to look beyond business as usual. In the long term, no company can afford to engage in premium price wars over commoditized products. A business model informed by IoT applications might emphasize differentiating offerings, strengthening customer bonds, energizing the industry brand, and curtailing risk either at or prior to its initiation.

IoT-related disruptors should also be considered through a long-term lens, and responses will likely need to be forward-looking and flexible to incorporate the increasingly connected, constantly evolving environment. With global connectivity reaching a fever pitch amid increasing rates of consumer uptake, embedding these neoteric schemes into the insurance industry’s DNA is no longer a matter of if but, rather, of when and how.

You can view the original post in its entirety Here

Read more…

Guest blog post by Bernard Marr

What does big data know about you?

Quite a lot.

Every time we use a computer, access our phones, or open an app on a tablet, we’re leaving a digital trail. Most people are vaguely aware that Google knows what they’ve searched for, or that Facebook knows who their friends are, but it goes much, much deeper than that.

I’ve compiled a list of 21 things Big Data knows about almost every one of us — right now:

  1. Of course, Google knows what you’ve searched for. So do Bing, Yahoo!, and every other search engine. And your ISP knows every website you’ve ever visited. Ever (even in private browsing).
  2. Google also knows your age and gender — even if you never told them. They make a pretty comprehensive ads profile of you, including a list of your interests (which you can edit) to decide what kinds of ads to show you.
  3. Facebook knows when your relationship is going south. Based on activities and status updates on Facebook, the company can predict (with scary accuracy) whether or not your relationship is going to last.
  4. Google knows where you’ve travelled, especially if you have an Android phone.
  5. And the police know where you’re driving right now — at least in the U.K., where closed circuit televisions (CCTV) are ubiquitous. Police have access to data from thousands of networked cameras across the country, which scan license plates and take photographs of each car and their driver. In the U.S., many cities have traffic cameras that can be used similarly.
  6. Your phone also knows how fast you were going when you were traveling. (Be glad they don’t share that information with the police!)
  7. Your phone has also probably deduced where you live and work.
  8. The Internet knows where your cat lives. Using the hidden meta-data about the geographic location of where the photo was taken which we share when we publish photos of our cats on sites like Instagram and other social media networks.
  9. Your credit card company knows what you buy. Of course your credit card company knows what you buy and where, but this has raised concerns that what you buy and where you shop might impact your credit score. They can use your purchasing data to decide if you’re a credit risk.
  10. Your grocery store knows what brands you like. For every point a grocery store or pharmacy doles out, they’re collecting mountains of data about your purchasing habits and preferences. The chains are using the data to serve up personalized experiences when you visit their websites, personalized coupon offers, and more.
  11. HR knows when you’re going to quit your job. An HR software company called Workday WDAY -1.00% is testing out an algorithm that analyzes text in documents and can predict from that information, which employees are likely to leave the company.
  12. Target knows if you’re pregnant. (Sometimes even before your family does.)
  13. YouTube knows what videos you’ve been watching. And even what you’ve searched for on YouTube.
  14. Amazon knows what you like to read, Netflix NFLX -0.85% knows what you like to watch. Even your public library knows what kinds of media you like to consume.
  15. Apple and Google know what you ask Siri and Cortana.
  16. Your child’s Barbie doll is also telling Mattel what she and your child talk about.
  17. Police departments in some major cities, including Chicago and Kansas City, know you’re going to commit a crime — before you do it.  
  18. Your auto insurance company knows when and where you drive — and they may penalize you for it, even if you’ve never filed a claim.
  19. Data brokers can help unscrupulous companies identify vulnerable consumers. For example, they may identify a population as a “credit-crunched city family” and then direct advertisements at you for payday loans.
  20. Facebook knows how intelligent you are, how satisfied you are with your life, and whether you are emotionally stable or not – simply based on a big data analysis of the ‘likes’ you have clicked.
  21. Your apps may have access to a lot of your personal data. Angry Birds gets access to your contact list in your phone and your physical location. Bejeweled wants to know your phone number. Some apps even access your microphone to record what’s going on around you while you use them.

This is actually just the tip of the iceberg. As we dive deeper into the benefits big data can provide to us, we’ll also be happily coughing up more and more data. The iPhone Health app, for instance, can collect data about all kinds of intimately personal things about your health.

It’s up to us, as consumers, to be aware of what we’re giving away, when, and to whom. I would love to hear your concerns and comments on this topic.

Bernard Marr is a best-selling author & keynote speaker. His new book: 'Big Data in Practice: How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results'

Follow us @IoTCtrl | Join our Community

Read more…

The technology sector is buzzing with predictions and hype about the Internet of Things (IoT), but many people are still confused about what it means, what the real world opportunities are and why businesses should be looking into IoT.

At a fundamental and simplistic level the Internet of Things refers to 'physical objects which linked via wired or wireless networks'

These physical objects could be anything (such as medical machines, vehicles, building systems, signage, toasters, smoke alarms, temperature sensors, weather monitors, intelligent tags or rubbish bins for example). Almost any object, in any sector, in any location could potentially join the Internet of Things, so its no wonder that Gartner predict there will be 50 billion devices connected by 2020 (and other analysts estimate several orders of magnitude more).  

Typically the Internet of Things is used to gather data and insight, find efficiency, automate tasks or improve an experience or service. At Smarter Technology Solutions (STS) we put this down to a simple formula, with greater insight, comes better decisions.

I know what you're thinking, why would you connect an object like a rubbish bin to the Internet?

Well its a simple example but it has tremendous flow on effects. Simply tracking the fill level of a rubbish bin using a smart sensor, councils and waste providers can find out a few important facts such as fill-level trends, how often the bin really needs emptying and when, to better plan waste collection services (eg timing of bin collection near food outlets to avoid lunchtimes) and to identify areas that may need more/less bins (to assist with city/service planning).
By collecting just the fill level data of a waste bin the following benefits could be attained:

  1. Reduction in cost as less bin collections = less waste trucks on the road, no unnecessary collections for a bin that's 20% full, less labour to complete waste collection. This also provides a level of operational efficiency and optimized processes.
  2. Environmental benefit - where waste is not overflowing and truck usage is reduced, flow on environmental impact, pollution and fuel consumption is minimized. By ensuring waste bins are placed in convenient locations, littering and scattered waste is also minimized.
  3. Service improvements - truck collection routes can be optimized, waste bins can be collected at convenient times and planning of future/additional services can be amended as the data to trend and verify assumptions is available. 

More complex examples of IoT include:

  • Intelligent transport systems which update digital signage on the highway and adjusts the traffic lights in real time to divert traffic, optimise traffic flow and reduce congestion;
  • A farm which uses sensors to measure soil moisture, chemical levels and weather patterns, adjusting the watering and treatment schedules accordingly;
  • The building which draws the blinds to block out the afternoon sun, reducing the need to consume more power cooling the building and to keep the environment comfortable;
  • Health-care devices which monitor patients and auto-alert medical practitioners once certain symptoms or attributes are detected; 
  • Trucks which automatically detect mechanical anomalies and auto schedule themselves in for preventative maintenance once they reach certain thresholds; 
  • Asset tracking of fleet vehicles within a services company which provides operations staff with fleet visibility to quickly dispatch the closest resource to a job based on proximity to the next task;
  • Water/gas/electric meters which sends in their own reading in on a monthly basis and trends analysis which can detect potential water/gas leaks; or
  • A retail store which analyses your in-store behavior or purchasing patterns and recommend products to you based on previous choices and your personal preferences.

At Smarter Technology Solutions we specialize in consulting with organizations  to understand the benefits of IoT, design best fit solutions, engineer and implement solutions as well as supporting the ongoing support needs of the organization. This results in 3 key outcomes:

  • Discovery of New Opportunities - With better visibility, trends, opportunities, correlations and inefficiencies can be understood. From this, products, services and business models can be adjusted or changed to achieve competitive advantage.
  • Improved Efficiency - By identifying inefficiencies in existing business practices, work-flows can be improved and more automated services can be provided.
  • Improved Services - With trends and real time data businesses are able make smarter decisions and alter the way you services are delivered.

www.smartertechnologysolutions.com.au

Read more…

The Internet of Things (IoT) concept promises to improve our lives by embedding billions of cheap purpose-built sensors into devices, objects and structures that surround us (appliances, homes, clothing, wearables, vehicles, buildings, healthcare tech, industrial equipment, manufacturing, etc.).

IoT Market Map -- Goldman Sachs

What this means is that billions of sensors, machines and smart devices will simultaneously collect volumes of big data, while processing real-time fast data from almost everything and... almost everyone!!!

IoT vision is not net reality

Simply stated, the Internet of Things is all about the power of connections.

Consumers, for the moment anyway, seem satisfied to have access to gadgets, trendy devices and apps which they believe will make them more efficient (efficient doesn't necessarily mean productive), improve their lives and promote general well-being.

Corporations on the other hand, have a grand vision that convergence of cloud computing, mobility, low-cost sensors, smart devices, ubiquitous networks and fast-data will help them achieve competitive advantages, market dominance, unyielding brand power and shareholder riches.

Global Enterprises (and big venture capital firms) will spend billions on the race for IoT supremacy. These titans of business are chomping at the bit to develop IoT platforms, machine learning algorithms, AI software applications & advanced predictive analytics. The end-game of these initiatives is to deploy IoT platforms on a large scale for;

  • real-time monitoring, control & tracking (retail, autonomous vehicles, digital health, industrial & manufacturing systems, etc.)
  • assessment of consumers, their emotions & buying sentiment,
  • managing smart systems and operational processes,
  • reducing operating costs & increasing efficiencies,
  • predicting outcomes, and equipment failures, and
  • monetization of consumer & commercial big data, etc.

 

IoT reality is still just a vision

No technology vendor (hardware or software), service provider, consulting firm or self-proclaimed expert can fulfill the IoT vision alone.

Recent history with tech hype-cycles has proven time and again that 'industry experts' are not very accurate predicting the future... in life or in business!

Having said this, it only makes sense that fulfilling the promise of IoT demands close collaboration & communication among many stake-holders.

A tech ecosystem is born

IoT & Industrial IoT comprise a rapidly developing tech ecosystem. Momentum is building quickly and will drive sustainable future demand for;

  • low-cost hardware platforms (sensors, smart devices, etc.),
  • a stable base of suppliers, developers, vendors & distribution,
  • interoperability & security (standards, encryption, API's, etc.),
  • local to global telecom & wireless services,
  • edge to cloud networks & data centers,
  • professional services firms (and self-proclaimed experts),
  • global strategic partnerships,
  • education and STEM initiatives, and
  • broad vertical market development.

I'll close with one final thought; "True IoT leaders and visionaries will first ask why, not how..!"

Read more…

Guest blog post by Peter Bruce

When Apple CEO Tim Cook finally unveiled his company’s new Apple Watch in a widely-publicized rollout, most of the press coverage centered on its cost ($349 to start) and whether it would be as popular among consumers as the iPod or iMac.

Nitin Indurkhya saw things differently.

“I think the most significant revelation was that of ResearchKit,” Indurkhya said. “It allows the iWatch to gather huge amounts of health-related data from its sensors that could then be used for medical research, an area that has traditionally been plagued by small samples and inconsistent and costly data collection, and for preventive care.”

Indurkhya is in a perfect position to know. He teaches text mining and other online courses for Statistics.com and the Institute for Statistics Education. And if you’ve ever wondered about the origins of a term we hear everywhere today – Big Data - the mystery is over. Indurkhya, along with Sholom Weiss, first coined "Big Data" in a predictive data mining book in 1998. (I never anticipated Big Data becoming a buzzword,” he said. “although we did expect the concept to take off.”)

The ResearchKit already has five apps that link users to studies on Parkinson's disease, diabetes, asthma, breast cancer and heart disease. Cook has touted other health benefits from Apple Watch, including its ability to tap users with a reminder to get up and move around if they have been sitting for a while. “We've taken (the mobile operating system) iOS and extended it into your car, into your home, into your health. All of these are really critical parts of your life,” Cook told a Goldman Sachs technology and Internet conference recently.

That helps explain the media fascination over another new Apple product. But it also tells us the importance of learning about Big Data. Having access to large amounts of raw numbers alone doesn’t necessarily change our lives. The transformation occurs when we master the skills needed to understand both the potential and the limitations of that information.

The Apple Watch exemplifies this because the ResearchKit essentially recruits test subjects for research studies through iPhone apps and taps into Apple Watch data. The implications for privacy, consent, sharing of data, and other ethical issues, are enormous. The Apple Watch likely won’t be the only device in the near future to prompt these kinds of concerns. It all leads to the realization that we need to be on a far more familiar basis with how data is collected and used than we’ve ever had to be in the past.

“We are increasingly relying on decisions, often from "smart" devices and apps that we accept and even demand,  that arise from data-based analyses,” Indurkhya said. “ So we do need to know when to, for example, manually override them in particular instances.

“Allowing our data to be pooled with others has benefits as well as risks. A person would need to understand these if they are to opt for a disclosure level that they are comfortable with. Otherwise the danger is that one would go to one or the other extreme, full or no participation, and have to deal with unexpected consequences.”

The Big Data questions raised by the Apple Watch are similar to the concerns over access to and disclosure of other reams of personal information. Edward Snowden’s leaks most famously brought these kinds of worries into play, publicizing the spying on ordinary Americans by the National Security Agency. There’s also commonly expressed fear that Big Data is dehumanizing, and that it’s used more for evil than for good.

These fears, Indurkhya noted, have seeped into the popular culture. Consider this list of Big Data movies: War Games, in which a super computer is given control of all United States defense assets.  Live Free or Die Hard, in which a data scientist hacker hopes to eventually bringing down the entire U.S. financial system. Even Batman gets into the act, hacking every cell phone in Gotham.

Little wonder people might shy away from studying big data. But that would be a mistake, said Indurkhya, who has a rebuttal for all the Hollywood hyped-fears.

First, he said, there are strong parallels between the Big Data revolution and the industrial revolution. Look at history. Despite all the dire predictions, machines aren't "taking over the world" and neither will Big Data.

Second, it’s also helpful to appreciate what Big Data gives us. It provides us with better estimates - they are more accurate and our confidence in them is higher. Perhaps more importantly, it provides estimates in situations where, in the absence of Big Data, answers were not obtainable at all, or not readily accessible. Think about searching the web for  "Little Red Riding Hood and Ricky Ricardo."  Even in the early days of the internet, you would have gotten lots of results individually for "Little Red Riding Hood" and "Ricky Ricardo," but it was not until Google had accumulated a massive enough data set, and perfected its Big Data search techniques, that you could reliably get directed to the "I Love Lucy" episode where Ricky dramatically reenacts the story for little Ricky. 

Data specialists can set policies and procedures that protect us from some of the risks of Big Data.  But we also need to become much more familiar with how our data is collected, analyzed, and distributed. If the Apple Watch rollout proves anything, it might be this: Going forward, we’ll all have to be as smart about data as our devices.

Follow us @IoTCtrl | Join our Community

Read more…

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. It will play a big part in the IoT. From our friends at R2D3 is a very interesting visual introduction to machine learning. Check it out here

Read more…

Keeping Up With Tech Trends

With the continuing evolution of technology, it's not surprising how trends are constantly changing as well. A big number of companies try to create new trends or keep up and ride with the current ones as they create new tech startups that will hook the public and keep them wanting for more.

Take Flappy Bird for example. Although the application was released May of 2013, it made huge waves in 2014 and even became the most downloaded free game in the Apple App Store. It even earned $50,000 a day! After feeling guilty about it's addictive nature, the creator of the game removed the game from application stores. But that gave opportunity for other developers to create their own application that was similar to it. So not long after that, hundreds of flappy bird-like applications were created and released in the application stores. But now it seems like the hype has died down and flappy bird will now just become another tech trend faded memory.

If you're one of the companies that are making plans now for a new tech trend or you're simply just making drafts for a new one, make sure you pay attention to what we think are the four rising tech trends this year. Bring out your pads and take note, people!

Think Smart

Nowadays, people are getting smart. Companies are creating better smart phones. Don't know what the time is? Check your smart watch. You can now even wear your computer with smart glasses and smart homes are getting popular too. Yes, developers are finding new ways to bring smarter things in this world. Create your own application or device that will keep the momentum going.

Some Privacy, Please

With plenty of emerging news about invasion of privacy, people are becoming more conscious about keeping things to themselves or just among a few people. Private applications like, Snapchat or ones that secure your pictures and other information have made waves because people favour them more for their privacy-promising quality. Look into creating or capitalizing more applications that will cater to this consumer need.

Drones!

With Amazon launching the video for their Prime Air, you know that they are coming. And with a great feedback of interest from everyone who watched the videos, we can predict that drones will no longer be something that we see on TV but something that we'll be experiencing very soon.

Data Forever

With growing popularity of social media or websites, unlimited data became and still is a huge trend. Look into ways to use that unlimited data as opportunity to use it for either better advertising, create better products or develop the consumer's experience. The possibilities are endless.

 

 

Read more…
The ‘connected’ car, not to be confused with the self-driving, autonomous car, is defined as any vehicle equipped with Internet access that allows data to be sent to and from the vehicle.

Since the automobiles were invented, car makers have been trying to add features which may reduce driver error. Today’s car has the computing power of 20 personal computers, features about 100 million lines of programming code, and processes up to 25 gigabytes of data an hour.

Digital technology is also changing how we use and interact with our cars, and in more ways than you probably realize.

The market for smart vehicles is certainly set for takeoff and many analysts predict they could revolutionize the world of automobiles in much the same way smartphones have changed the face of telecommunications.

Is your car connected to the Internet? Millions of vehicles around the world had embedded Internet access, offering their drivers a multitude of smart options and benefits. These include better engine controls, automatic crash notifications and safety alerts, to name just a few. Owners can also interact with their connected vehicles through apps from any distance.

Vehicle-to-vehicle communications, for example, could help automobiles detect one another's presence and location to avoid accidents. That could be especially useful when it comes to driver-less cars - another advance already very much in development. Similar technology could help ensure that cars and their drivers slow down for school zones or stop at red lights.

Connected vehicle technologies provide the tools to make transformational improvements in safety, to significantly reduce the number of lives lost each year through connected vehicle crash prevention applications.

The Connected Car will be optimized to track and report its own diagnostics, which is part of its appeal for safety conscious drivers.

Connected cars give superior Infotainment services like navigation, traffic, weather, mobile apps, emails and also entertainment.

Auto insurers also have much to gain from the connected car revolution, as personalized, behavior based premiums are already becoming new industry standard.

OEMS and dealers must embrace the  Big Data revolution now, so they’re ready to harness the plethora of data that will become available as more and more connected cars hit the roads.

Cloud computing powers much of the audio streaming capabilities and dashboard app functions that are becoming more commonplace in autos.

In the next 5 years it seems that non-connected cars will become a thing of the past.  Here are some good examples of connected cars:

  • Mercedes-Benz models introduced this year can link directly to Nest, the Internet of Things powered smart home system, to remotely activate a home’s temperature controls prior to arrival.
  • Audi has developed a 12.3 inch, 3d graphics fully digital dashboard in partnership with NVIDIA.
  • Telematics Company OnStar can shut down your stolen car remotely helping police solve the case.
  • ParkMe covers real time dynamic parking information and guide drivers to open parking lots and meters. It if further integrating with mobile payments.

The next wave is driver-less, fully equipped and connected car, where there will be no steering wheels, brakes, gas pedals and other major devices. You just have to sit back, relax and enjoy the ride!!

This article originally appeared here.
Read more…

Node.js and The Internet of Things

Last year, we interviewed Patrick Catanzariti and asked him if Javascript will be the language of IoT. It was one of our most shared Q&As. Charlie Key's talk at the Node Community Conference provides a nice overview of how Node is driving adoption of IoT. In software development, Node.js is an open-source, cross-platform runtime environment for developing server-side Web applications. Although Node.js is not a JavaScript framework, many of its basic modules are written in JavaScript, and developers can write new modules in JavaScript.

Here's his presentation and a look at where the market of the Internet of Things is and how technologies like Node.js (JavaScript) and the Intel Edison are making it easier to create connected solutions. 

The major topics include: 
* What is the Internet of Things 
* Where is IoT Today 
* 4 Parts of IoT (Collect, Communicate, Analyze, Act) 
* Why JavaScript is Good for IoT 
* How Node.js is Making a Dent in the Internet of Things 
* What npm Modules are used for Hardware (Johnny-Five, Cylon.js, MRAA) 
* What is the Intel Edison 
* How to Best Work with the Edison 
* Tips for Edison (MRAA, Grove Kit, UPM) 
* Where the World of JavaScript and IoT is Going Node.js 

Read more…

The IoT Database

Phillip Zito at the highly resourceful blog Building Automation Monthly has consolidated multiple IoT Frameworks and Offerings into the IoT Database. You will see links to the Frameworks and Offerings below. He says over time that he will be working on providing summary articles on each Framework and Offering. He could use your help. If you have an offering/framework you would like added to this list feel free to add it in the comments. You can find the IoT Database here

Read more…

The IoT User Experience Urgency

As we evolve toward a software-defined world, there’s a new user experience urgency emerging.  That’s because the definition of “user” is going to be vastly expanded.  In the Internet of Things (IoT) era, users include machines.

Companies today are generating, collecting and analyzing more data than ever before.  They want to get better insights into their customers and their business operations.  This is driving substantial Investments in new architectures that extend to cloud and mobility.

They’re also yielding to user demands for more and newer sources of big data.  They’re experimenting with data lakes to store this potential trove.  And they’re investing in data blending and visualization technologies to analyze it all.

In the IoT world of the near future, however, much of this analysis is going to be done by machines with deep learning capabilities.  With forecasts for as many as 50 billion connected devices by 2020, the experience of these “users” with the applications they engage with will be no less critical to achieving strategic objectives than customer experience is now – and will remain.

But how are companies going to get smarter if user experience sucks?  Where is this greater insight going to come from if whatever business intelligence software they’ve deployed is not performing to user expectations?

They’re not going to win customer satisfaction and loyalty by frustrating users.  And the risks involved with disappointing machine users could be catastrophic.

It's Time to Get Strategic

More companies have come to realize the strategic value of their data.  As such, they’re seeking ways to get a higher return on those data assets.  The databases – both transactional and analytic – they’ve invested in are critical to corporate strategy.

In order to maximize the performance of business-critical apps companies must get strategic about user experience and application performance.  Monitoring technologies can no longer be implemented as short-term tactical bandages.

They might put out a brush fire temporarily, but they create more complexity and management headaches in the long run.  They often don’t work well together and generate more false positives than a smoke detector with a failing battery.  Annoying right?

IT teams are going to have to get more efficient with their ops data.   They will need a standardized approach to integrating diverse data sets, including those from SaaS applications and IaaS or PaaS clouds.  This is critical to gaining physical and logical knowledge of the computing environment across the entire application delivery chain.

Next-generation data integration technologies can unify ops data from traditional monitoring solutions with real-time streams of machine data and other types of big data.  They automate much of the cleansing, matching, error handling and performance monitoring that IT Ops teams often struggle with manually.

As this ops data grows with IoT, it can be fed into a data lake for analysis.  In fact, IT teams can kill two birds with one stone.  First, IT Ops data is a natural fit as an early test case for a data lake.  And by starting now they can hone skills sets for big data analytics and the coming IoT data deluge.

IT Ops, which are increasingly becoming a part of DevOps teams, can learn from and share their experiences with data management and analytics teams – as well as business teams.  It makes sense to bring application governance and data governance together because they share a common goal: ensuring that users have access to the highest quality data at the point of decision to optimize business outcomes and mitigate risks. 

The Path to ROI and Risk Management Objectives

This environment necessitates communication and collaboration among IT and business teams to proactively anticipate, identify and resolve application performance and user experience problems.  It also facilitates orchestration and management of both internally and externally sourced services efficiently to improve decision-making and business outcomes.

Through a unified approach to performance analytics, IT can help their companies leverage technology investments to discover, interpret and respond to the myriad events that impact their operations, security, compliance and competitiveness.  Ops data efficiency becomes actionable to facilitate strategic initiatives and positively impact financial results.

Successful strategy implementation manifests in return on investment (ROI) and risk management.  Multiple studies, including ours and the annual Puppet Labs State of DevOps report confirm that companies taking a strategic approach to user experience and application performance outperform their respective peer groups in financial metrics and market performance.

Vendors in this space – usually referred to as application performance management ( APM) – need to advance their thinking and technology.  Machine learning and predictive analytics are going to be table stakes in the IoT future.

APM vendors have a choice: they can maintain a focus on human user experience, which will always be essential.  Or they can think more broadly about user experience in the IOT world.  Because some of today’s enterprise customers – that produce everything from home monitoring devices and appliances to turbine engines, agricultural machinery and healthcare equipment – could one day well become competitors.

By capturing data from embedded sensors and applying advanced analytics to provide customers using their equipment with deeper insights, they could close out what will become the lion’s share of the IoT user experience market.  Leading manufacturers are already there.

 Photo: Gorbash Varvara

Originally posted on Big Data News by Gabriel Lowy

Follow us @IoTCtrl | Join our Community

Read more…

IoT Dictionary and M2M Industry Terms

camera-dictionary

Here's a great resource  from Aeris - an IoT Dictionary.

Aeris Communications has been in the machine-to-machine market for some time and are both a technology provider and a cellular network operator delivering comprehensive IoT / M2M services.

This glossary includes key terms of the IoT (Internet of Things) & M2M (machine-to-machine) communications industry, including wireless and cellular technologies spanning many different markets. It is updated to present current terminology and usage. It's a crowd-sourced resource, so feel free to contact Aries with suggestions. 

Also, if you need an IT-related dictionary, I just love WhatIs.com.

Read more…
RSS
Email me when there are new items in this category –

IoT Open Discussion Forums

Upcoming IoT Events

More IoT News

IoT Career Opportunities