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7 things that are getting smarter in IoT era

Internet of Things is surrounded with a lot of buzz, which is there for a reason. It is one of the most revolutionary technologies and it is the closest we’ve come to predicting our future. Of course, the IoT is not based on spells and witchcraft (it’s way scarier than that), but on machine-to-machine communication, cloud computing and networks of small sensors, which collect and analyze data. In this article we’ll share some of things and processes that will change in the IoT Era.

Home security systems

Today you can monitor home security cameras from your smartphone screen. More advanced home security systems go even further. They come with different types of sensors that control air quality, motion, sound, vibration and temperature. These systems use machine learning to determine the normal activity in your home and they send alerts to your smartphone, when something out of the ordinary occurs. Because of their smart machine learning approach, home security systems that are based on IoT concept drastically reduce the incidence of false alarms.

Bed

Even our beds will become smart. At the moment you can buy several types of sleep trackers from the ones that come in the form of bracelet and measure your heart rate and blood pressure to smart mattresses that can connect to home automation systems, prepare your bed temperature, track your heart and breathing rate and wake you up in the morning. These special mattresses also collect information about your sleep and give you recommendations for improving your bed rest.

Energy use

Recently several companies released Wi-Fi enabled sensors that can connect to the home electrical panel and control and track your energy use. These small sensors recognize all appliances and gadgets by their “power signatures” and can monitor the energy use and brake it down to every single device. They will allow you to have a deep look into your monthly energy use, to recognize and deal with critical points and to save money on utility bills. Same as many other home security and home automation systems, these sensors learn to interpret the activity of your home devices and send warnings when incidents happen.

All home appliances and systems

All-in-one smart home automation systems can control several home appliances at once. People can use them to turn their porch lights on and off when they are on vacation and to preheat their home or their oven before they arrive home from work. These systems also control various conditions in your home and use smart sensors and machine learning to create the perfect comfort. Some home automation systems also come with a Bluetooth speaker and a microphone and they can work as voice assistants.

Self-storage monitoring

Self-storage monitoring protects stored goods from climate changes, theft and other unforeseen incidents. New storage monitoring systems based on the IoT concept control storage lighting, air-conditioning and security. They also use sensors to track variables that are critical for perishable goods like temperature and humidity. You can find these smart storages in many different cities around the world. 

Construction sites

Construction site managers can use IoT solutions to monitor the work of heavy machinery and the movement of construction employees. This basically means that they don’t need to leave their trailer office. Sensors track the movement of supply and dumping trucks through geo-location technology and insure that everything works as scheduled. If there’re any irregularities in the work of heavy machinery, supply trucks or employees, the site manager will be instantly notified by smartphone push-notification.  

Emergency vehicles

In many cities the only connection between emergency vehicles and their headquarters is established through old-fashion radios. This offers a limited control in emergency situations. Advanced telematics already appeared in many emergency vehicles around the world. This technology allows lone drivers to receive updates in real time from the environment they are entering, including: over speeding, harsh events or the incidents of other team members. Employees at the headquarters also receive the information about emergency vehicle’s hours of service, speed, siren state and location. This way, they can easily schedule vehicle’s regular maintenance and minimize its downtime.

Internet of Things is the biggest tech trend that is happening at the moment. It will completely rock our world and bring a lot of positive disruption to every segment of our lives. Soon, we’ll be able to control all of our possessions through one smart app, which will leave us more time to focus on ourselves and our friends and family.

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New IoT App Makes Drivers Safer

Transportation has become one of the most frequently highlighted areas where the internet of things can improve our lives. Specifically, a lot of people are excited about the IoT's potential to further the progress toward entire networks of self-driving cars. We hear a lot about the tech companies that are involved in building self-driving cars, but it's the IoT that will actually allow these vehicles to operate. In fact, CNET quoted one IoT expert just last year as saying that because of the expanding IoT, self-driving cars will rule the roads by 2030.

On a much smaller scale, there are also some niche applications of the IoT that are designed to fix specific problems on the road. For instance, many companies have looked to combat distracted driving by teenagers through IoT-related tools. As noted by PC World, one device called the Smartwheel monitors teens' driving activity by sensing when they're keeping both hands on the wheel. The device sounds an alert when a hand comes off the wheel and communicates to a companion app that compiles reports on driver performance. This is a subtle way in which the IoT helps young drivers develop better habits.

In a way, these examples cover both extremes of the effect the IoT is having on drivers. One is a futuristic idea that's being slowly implemented to alter the very nature of road transportation. The other is an application for individuals meant to make drivers safer one by one. But there are also some IoT-related tools that fall somewhere in the middle of the spectrum. One is an exciting new app that seeks to make the roads safer for the thousands of shipping fleet drivers operating on a daily basis.

At first this might sound like a niche category. However, the reality is that the innumerable companies and agencies relying shipping and transportation fleets have a ton of drivers to take care of. That means supervising vehicle performance, safety, and more for each and every one of them. That process comprises a significant portion of road activity, particularly in cities and on highways. These operations are able to be simplified and streamlined through Networkfleet Driver, which Verizon describes as a tool to help employees manage routes, maintenance, communication, and driving habits all in one place.

The app can communicate up-to-date routing changes or required stops, inform drivers of necessary vehicle repairs or upkeep, and handle communication from management. It can also make note of dangerous habits (like a tendency to speed or make frequent sudden stops), helping the driver to identify bad habits and helping managers to recommend safer performance. All of this is accomplished through various IoT sensors on vehicles interacting automatically with the app, and with systems that can be monitored by management.

The positive effect, while difficult to quantify, is substantial. Fleet drivers make up a significant portion of road activity, and through the use of the IoT we can make sure that the roads are safer for everyone.

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Almost three years ago, I wrote in my IoT blog  the posts “Are you prepared to answer M2M/IoT security questions of your customers ?. and “There is no consensus how best to implement security in IoT” given the importance that Security has to fulfil the promise of the Internet of Things (IoT).

And during this time I have been sharing my opinion about the key role of IoT Security with other international experts in articles “What is the danger of taking M2M communications to the Internet of Things?, and events (Cycon , IoT Global Innovation Forum 2016).

The Security has been always a tradeoff between cost and benefit

I am honest when I say that I do not known how McKinsey gets calculate the total impact that IoT will have on the world economy in 2025, even on one of the specific sectors, and if they had taking into account the challenge of the Security, but it hardly matters: “The opportunities generated by IoT far outweigh the risks”.

With increased IoT opportunity comes increased security risks and a flourishing IoT Security Market (According with Zion Research the IoT Security Market will growth to USD 464 million in 2020).

A decade of breaches and the biggest attack target yet is looming

We all know the negative impact that news about cyber-attacks has in the society and enterprises. In less than a decade and according to Data Source: ICS- CERT (US) have gone from 39 incidents in 2010 to 295 incidents in 2015.

In a survey published by ATT, the company has logged a 458% increase in vulnerability scans of IoT devices in the last 2 years.

It is a temptation for hackers to test their skills in connected objects, whether connected cars or smart homes appliances. But I'm afraid they will go far beyond attacking smart factories, or smart transportation infrastructure or smart grids.

With the millions of unprotected devices out there, the multitude of IoT networks, IoT Platforms, and developers with lack of security I am one more that believes the biggest attack target yet is looming.

 New Threats

With the Internet of Things, we should be prepared for new attacks and we must design new essential defences.

The complex IoT Security Threat Map from Beecham Research provides an overlayed summary of the full set of threat and vulnerability analyses that is used to help clients shape their strategies. This Threat Map “summary” many of the top 5 features from each of those analyses.

1.       external threats and the top internal vulnerabilities of IoT applications

2.       the needs for robust authentication & authorisation & confidentiality

3.       the features and interactions between multiple networks used together in IoT;

4.       the complexities of combining Service Sector optimised capabilities of differing Service Enablement Platforms;

5.       the implementation and defences of edge device operating systems, chip integration and the associated Root of Trust.

 New Vulnerabilities

The OWASP Internet of Things Project is designed to help manufacturers, developers, and consumers better understand the security issues associated with the Internet of Things, and to enable users in any context to make better security decisions when building, deploying, or assessing IoT technologies.

The project looks to define a structure for various IoT sub-projects such as Attack Surface Areas, Testing Guides and Top Vulnerabilities. Bellow the top IoT Vulnerabilities.

 Subex white paper presenting their IoT solution add some real examples of  these vulnerabilities.

Insecure Web Interface: To exploit this vulnerability, attacker uses weak credentials or captures plain text credentials to access web interface. The impact results in data loss, denial of service and can lead to complete device take over. An insecure web interface was exploited by hackers to compromise Asus routers in 2014 that were shipped with default admin user name and password.

Insufficient Authentication/Authorization: Exploitation of this vulnerability involves attacker brute forcing weak passwords or poorly protected credentials to access a particular interface. The impact from this kind of attack is usually denial of service and can also lead to compromise of device. This vulnerability was exploited by ethical hackers to access head unit of Jeep Cherokee2 via WiFi-connectivity. The WiFi password for Jeep Cherokee unit is generated automatically based upon the time when car and head unit is started up. By guessing the time and using brute force techniques, the hackers were able to gain access to head unit.

Insecure Network Services: Attacker uses vulnerable network services to attack the device itself or bounce attacks off the device. Attackers can then use the compromised devices to facilitate attacks on other devices. This vulnerability was exploited by hackers that used 900 CCTV cameras3 globally to DoS attack a cloud platform service.

Lack of Transport Encryption: A lack of transport encryption allows 3rd parties to view data transmitted over the network. The impact of this kind of attack can lead to compromise of device or user accounts depending upon the data exposed. This weakness was exhibited by Toy Talk’s server domain which was susceptible to POODLE attack. Toy Talk helps Hello Barbie doll4 to talk to a child by uploading the words of a child to server and provide appropriate response after processing it. Though there was no reported hack on this, such a vulnerability could easily lead to one.

Privacy Concerns: Hackers use different vectors to view and/or collect personal data which is not properly protected. The impact of this attack is collection of personal user data. This vulnerability was exemplified by the VTech hack5 wherein in hackers were able to steal personal data of parents as well as children using VTech’s tablet.

Who owns the problem?

With the IoT we are creating a very complicated supply chain with lots of stakeholders so it's not always clear 'who owns the problem'. By way of an example with a simple home application and not Super Installers around; if you buy a central heating system and controller which requires you to push a button to increase the temperature then if it stops working you contact the company who supplied it. But if you buy a central heating boiler from one company, a wireless temperature controller from another, download a mobile App from another and have a weather station from another supplier then whose job is it to make sure it's secure and reliable? The simple cop-out is to say 'the homeowner bought the bits and connected them together therefore it's their responsibility' – well I'm sorry but that isn't good enough! 

Manufacturers can't simply divest themselves of responsibility simply because the home owner bought several component parts from different retailers. As a manufacturer you have a responsibility to ensure that your product is secure and reliable when used in any of the possible scenarios and use cases which means that manufacturers need to work together to ensure interoperability – we all own the problem!

This might come as a shock to some companies/industries but at some level even competitors have to work together to agree and implement architectures and connectivity that is secure and reliable. Standardization is a good example of this, if you look at the companies actively working together in ISO, ETSI, Bluetooth SIG etc. then they are often fierce competitors but they all recognize the need to work together to define common, secure and reliable platforms around which they can build interoperable products.  

If Cybersecurity is already top of mind for many organizations, is justified the alarm of lack of security in IoT?

In this three last years of evangelization of IoT, it has been no event or article not collect questions or comments on IoT Security and Privacy.

The good news is that according with the ATT State of IoT Security survey 2015, 85% of global organizations are considering exploring or implementing an IoT strategy but the bad news is that only 10% are fully confident that their connected devices are secure.

Source: ATT State of IoT Security survey 2015

And if we consider the report of Auth0, it scares me that only 10% of developers believe that most IoT devices on the market right now have the necessary security in place.

 

Source: Auth0

In a publication from EY titled “Cybersecurity and the IoT”, the company define three Stages to classify the current status of organizations in the implementation of IoT Security.

Stage 1: Activate

Organizations need to have a solid foundation of cybersecurity. This comprises a comprehensive set of information security measures, which will provide basic (but not good) defense against cyber-attacks. At this stage, organizations establish their fundamentals — i.e., they “activate” their cybersecurity.

Stage 2: Adapt

Organizations change — whether for survival or for growth. Threats also change. Therefore, the foundation of information security measures must adapt to keep pace and match the changing business requirements and dynamics otherwise they will become less and less effective over time. At this stage, organizations work to keep their cybersecurity up-to-date; i.e., they “adapt” to changing requirements.

Stage 3: Anticipate

Organizations need to develop tactics to detect and detract potential cyber-attacks. They must know exactly what they need to protect their most valuable assets, and rehearse appropriate responses to likely attack/incident scenarios: this requires a mature cyber threat intelligence capability, a robust risk assessment methodology, an experienced incident response mechanism and an informed organization. At this stage, organizations are more confident about their ability to handle more predictable threats and unexpected attacks; i.e., they anticipate cyber-attacks.

 

What enterprises needs to do

If you are thinking only in the benefits of IoT without consider the Security as a key component in your strategy you will probably regret very soon. Here below some recommendations either before start your IoT journey or if you are already started. Hope is not too late for wise advices.

Key Takeaways

With the proliferation and variety of IoT Devices, IoT Networks, IoT Platforms, Clouds, and applications, during the next few years we will see new vulnerabilities and a variety of new attacks. The progress in the security technologies and processes that prevent them will be key for the adoption of IoT in enterprises and consumers.

In the future Internet of Things world an end to end security approach to protect physical and digital assets. The ecosystems of this fragmented market must understand the need of Security by Design and avoid the temptation to reduce cost at the expense of the security.

Do not stop asking for security when you buy a connected product or use an IoT Service, the temptation of time to market, competitive prices and the lack of resources must not be an excuse to offer secure IoT solutions to enterprises, consumers and citizens.

 

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As if the Internet of Things (IoT) was not complicated enough, the Marketing team at Cisco introduced its Fog Computing vision in January 2014, also known as Edge Computing  for other more purist vendors.

Given Cisco´s frantic activity in their Internet of Everything (IoE) marketing campaigns, it is not surprising that many bloggers have abused of shocking headlines around this subject taking advantage of the Hype of the IoT.

I hope this post help you better understand what is  the role of Fog Computing  in the IoT Reference Model and how companies are using IoT Intelligent gateways in the Fog to connect the "Things" to the Cloud through some applications areas and examples of Fog Computing.

The problem with the cloud

As the Internet of Things proliferates, businesses face a growing need to analyze data from sources at the edge of a network, whether mobile phones, gateways, or IoT sensors. Cloud computing has a disadvantage: It can’t process data quickly enough for modern business applications.

The IoT owes its explosive growth to the connection of physical things and operation technologies (OT) to analytics and machine learning applications, which can help glean insights from device-generated data and enable devices to make “smart” decisions without human intervention. Currently, such resources are mostly being provided by cloud service providers, where the computation and storage capacity exists.

However, despite its power, the cloud model is not applicable to environments where operations are time-critical or internet connectivity is poor. This is especially true in scenarios such as telemedicine and patient care, where milliseconds can have fatal consequences. The same can be said about vehicle to vehicle communications, where the prevention of collisions and accidents can’t afford the latency caused by the roundtrip to the cloud server.

“The cloud paradigm is like having your brain command your limbs from miles away — it won’t help you where you need quick reflexes.”

Moreover, having every device connected to the cloud and sending raw data over the internet can have privacy, security and legal implications, especially when dealing with sensitive data that is subject to separate regulations in different countries.

IoT nodes are closer to the action, but for the moment, they do not have the computing and storage resources to perform analytics and machine learning tasks. Cloud servers, on the other hand, have the horsepower, but are too far away to process data and respond in time.

The fog layer is the perfect junction where there are enough compute, storage and networking resources to mimic cloud capabilities at the edge and support the local ingestion of data and the quick turnaround of results.

The variety of IoT systems and the need for flexible solutions that respond to real-time events quickly make Fog Computing a compelling option.

The Fog Computing, Oh my good another layer in IoT!

A study by IDC estimates that by 2020, 10 percent of the world’s data will be produced by edge devices. This will further drive the need for more efficient fog computing solutions that provide low latency and holistic intelligence simultaneously.

“Computing at the edge of the network is, of course, not new -- we've been doing it for years to solve the same issue with other kinds of computing.”

The Fog Computing or Edge Computing  is a paradigm championed by some of the biggest IoT technology players, including Cisco, IBM, and Dell and represents a shift in architecture in which intelligence is pushed from the cloud to the edge, localizing certain kinds of analysis and decision-making.

Fog Computing enables quicker response times, unencumbered by network latency, as well as reduced traffic, selectively relaying the appropriate data to the cloud.

The concept of Fog Computing attempts to transcend some of these physical limitations. With Fog Computing processing happens on nodes physically closer to where the data is originally collected instead of sending vast amounts of IoT data to the cloud.

Photo Source: http://electronicdesign.com/site-files/electronicdesign.com/files/uploads/2014/06/113191_fig4sm-cisco-fog-computing.jpg

The OpenFog Consortium

The OpenFog Consortium, was founded on the premise based on open architectures and standards that are essential for the success of a ubiquitous Fog Computing ecosystem.

The collaboration among tech giants such as ARM, Cisco, Dell, GE, Intel, Microsoft and Schneider Electric defining an Open, Interoperable Fog Computing Architecture is without any doubt good news for a vibrant supplier ecosystem.

The OpenFog Reference Architecture is an architectural evolution from traditional closed systems and the burgeoning cloud-only models to an approach that emphasizes computation nearest the edge of the network when dictated by business concerns or critical application the functional requirements of the system.

The OpenFog Reference Architecture consists of putting micro data centers or even small, purpose-built high-performance data analytics machines in remote offices and locations in order to gain real-time insights from the data collected, or to promote data thinning at the edge, by dramatically reducing the amount of data that needs to be transmitted to a central data center. Without having to move unnecessary data to a central data center, analytics at the edge can simplify and drastically speed analysis while also cutting costs.

Benefits of Fog Computing

  • ·         Frees up network capacity - Fog computing uses much less bandwidth, which means it doesn't cause bottlenecks and other similar occupancies. Less data movement on the network frees up network capacity, which then can be used for other things.
  • ·         It is truly real-time - Fog computing has much higher expedience than any other cloud computing architecture we know today. Since all data analysis are being done at the spot it represents a true real time concept, which means it is a perfect match for the needs of Internet of Things concept.
  • ·         Boosts data security - Collected data is more secure when it doesn't travel. Also makes data storing much simpler, because it stays in its country of origin. Sending data abroad might violate certain laws.
  • ·         Analytics is done locally- Fog computing concept enables developers to access most important IoT data from other locations, but it still keeps piles of less important information in local storages;
  • ·         Some companies don't like their data being out of their premises- with Fog Computing lots of data is stored on the devices themselves (which are often located outside of company offices), this is perceived as a risk by part of developers' community.
  • ·         Whole system sounds a little bit confusing- Concept that includes huge number of devices that store, analyze and send their own data, located all around the world sounds utterly confusing.

Disadvantages of Fog Computing

Read more: http://bigdata.sys-con.com/node/3809885

Examples of Fog Computing

The applications of fog computing are many, and it is powering crucial parts of IoT ecosystems, especially in industrial environments. See below some use cases and examples.

  • Thanks to the power of fog computing, New York-based renewable energy company Envision has been able to obtain a 15 percent productivity improvement from the vast network of wind turbines it operates. The company is processing as much as 20 terabytes of data at a time, generated by 3 million sensors installed on the 20,000 turbines it manages. Moving computation to the edge has enabled Envision to cut down data analysis time from 10 minutes to mere seconds, providing them with actionable insights and significant business benefits.
  • Plat One is another firm using fog computing to improve data processing for the more than 1 million sensors it manages. The company uses the Cisco-ParStream platform to publish real-time sensor measurements for hundreds of thousands of devices, including smart lighting and parking, port and transportation management and a network of 50,000 coffee machines.
  • In Palo Alto, California, a $3 million project will enable traffic lights to integrate with connected vehicles, hopefully creating a future in which people won’t be waiting in their cars at empty intersections for no reason.
  • In transportation, it’s helping semi-autonomous cars assist drivers in avoiding distraction and veering off the road by providing real-time analytics and decisions on driving patterns.
  • It also can help reduce the transfer of gigantic volumes of audio and video recordings generated by police dashboard and video cameras. Cameras equipped with edge computing capabilities could analyze video feeds in real time and only send relevant data to the cloud when necessary.

See more at: Why Edge Computing Is Here to Stay: Five Use Cases By Patrick McGarry  

What is the future of fog computing?

The current trend shows that fog computing will continue to grow in usage and importance as the Internet of Things expands and conquers new grounds. With inexpensive, low-power processing and storage becoming more available, we can expect computation to move even closer to the edge and become ingrained in the same devices that are generating the data, creating even greater possibilities for inter-device intelligence and interactions. Sensors that only log data might one day become a thing of the past.

Janakiram MSV  wondered if Fog Computing  will be the Next Big Thing In Internet of Things? . It seems obvious that while cloud is a perfect match for the Internet of Things, we have other scenarios and IoT solutions that demand low-latency ingestion and immediate processing of data where Fog Computing is the answer.

Does the fog eliminate the cloud?

Fog computing improves efficiency and reduces the amount of data that needs to be sent to the cloud for processing. But it’s here to complement the cloud, not replace it.

The cloud will continue to have a pertinent role in the IoT cycle. In fact, with fog computing shouldering the burden of short-term analytics at the edge, cloud resources will be freed to take on the heavier tasks, especially where the analysis of historical data and large datasets is concerned. Insights obtained by the cloud can help update and tweak policies and functionality at the fog layer.

And there are still many cases where the centralized, highly efficient computing infrastructure of the cloud will outperform decentralized systems in performance, scalability and costs. This includes environments where data needs to be analyzed from largely dispersed sources.

“It is the combination of fog and cloud computing that will accelerate the adoption of IoT, especially for the enterprise.”

In essence, Fog Computing allows for big data to be processed locally, or at least in closer proximity to the systems that rely on it. Newer machines could incorporate more powerful microprocessors, and interact more fluidly with other machines on the edge of the network. While fog isn’t a replacement for cloud architecture, it is a necessary step forward that will facilitate the advancement of IoT, as more industries and businesses adopt emerging technologies.

'The Cloud' is not Over

Fog computing is far from a panacea. One of the immediate costs associated with this method pertains to equipping end devices with the necessary hardware to perform calculations remotely and independent of centralized data centers. Some vendors, however, are in the process of perfecting technologies for that purpose. The tradeoff is that by investing in such solutions immediately, organizations will avoid frequently updating their infrastructure and networks to deal with ever increasing data amounts as the IoT expands.

There are certain data types and use cases that actually benefit from centralized models. Data that carries the utmost security concerns, for example, will require the secure advantages of a centralized approach or one that continues to rely solely on physical infrastructure.

Though the benefits of Fog Computing are undeniable, the Cloud has a secure future in IoT for most companies with less time-sensitive computing needs and for analysing all the data gathered by IoT sensors.

 

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Customer 360º view in Digital age

In today’s  digital age of customer  hyper-personalization, organizations identify opportunities for real time engagement based on data-driven understanding of customer behavior.
Customers have taken control of their purchase process. With websites, blogs, Facebook updates, online reviews and more, they use multiple sources of information to make decisions and often engage with a brand dozens of times between inspiration and purchase.
It’s important that organizations collect every customer interaction in order to identify sentiments of happy & unhappy customers.
Companies can get a complete 360º view of customers by aggregating data from the various touch points that a customer may use, to contact a company to purchase products and receive service/support.
This Customer 360º snapshot should include:
  • Identity: name, location, gender, age and other demographic data
  • Relationships: their influence, connections, associations with others
  • Current activity: orders, complaints, deliveries, returns
  • History: contacts, campaigns, processes, cases across all lines of business and channels
  • Value: which products or services they are associated with, including history
  • Flags: prompts to give context, e.g. churn propensity, up-sell options, fraud risk, mood of last interactions, complaint record, frequency of contact
  • Actions: expected, likely or essential steps based on who they are and the fact they are calling now

The 360º view of customers, also often requires a  big data analytics strategy to marry structured data (data that can reside in the rows and columns of a database), with unstructured data (data like audio files, video files, social media data). 
Many companies like Nestle, Toyota are using social media listening tools to gather what customers are saying on sites like Facebook and Twitter, predictive analytics tools to determine what customers may research or purchase next.
What are the returns of Customer 360º:
  • All customer touch point data in a single repository for fast queries
  • Next best actions or recommendations for customers
  • All key metrics in a single location for business users to know and advise customers
  • Intuitive and customizable dashboards for quick insights
  • Real time hyper personalized customer interaction
  • Enhanced customer loyalty

Customer 360º helps achieve Single View of Customer across Channels – online, stores, marketplaces, Devices – wearables, mobile, tablets, laptops & Interactions – purchase, posts, likes, feedback, service.

This is further used for customer analytics – predict  churn, retention, next best action, cross-sell & up-sell opportunities, profitability, life time value.
Global leaders in customer experience are Apple, Disney, Emirates.
A word of caution though - Focus & collect only that customer data, which can help to improve the  customer journey.
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EDITOR'S NOTE: This story originally appeared on the A10 Networks blog.

A pair of distributed denial-of-service (DDoS) attacks against high-profile targets last week rank among the largest DDoS attacks on record. And a common thread has emerged: these attacks are leveraging botnets comprising hundreds of thousands of unsecured Internet of Things (IoT) devices.

OVH attack reaches 1 Tbps

European Web hosting company OVH confirmed last week that it suffered a string of DDoS attacks that neared the 1 Tbps mark. On Twitter, OVH CTO Octave Klaba said the attacks OVH suffered were “close to 1 Tbps” and noted that the flood of traffic was fueled by a botnet made up of nearly 150,000 digital video recorders and IP cameras capable of sending 1.5 Tbps in DDoS traffic. Klaba said OVH servers were hit by multiple simultaneous attacks exceeding 100 Gbps each, totaling more than 1 Tbps. The most severe single attacks that was documented by OVH reached 93 million packets-per-second (mpps) and 799 Gbps.

SC Magazine UK quoted security researcher Mustafa Al-Bassam as saying the DDoS attack against OVH is “the largest DDoS attack ever recorded.”

Krebs gets slammed

The OVH attack came on the heels of another gargantuan DDoS incident, this one targeting respected cybersecurity blog Krebsonsecurity.com, which knocked the site offline for several hours.

“The outage came in the wake of a historically large distributed denial-of-service (DDoS) attack which hurled so much junk traffic at Krebsonsecurity.com that my DDoS protection provider Akamai chose to unmoor my site from its protective harbor,” Brian Krebs wrote, adding that he has since implemented DDoS protection from Google’s Project Shield.

The attack on Krebs clocked in at a massive 620 Gbps in size, which is several orders of magnitude more traffic than is typically necessary to knock most websites offline.

SecurityWeek reported that Krebs believes the botnet used to target his blog mostly consists of IoT devices — perhaps millions of them — such as webcams and routers that have default or weak credentials.

“There is every indication that this attack was launched with the help of a botnet that has enslaved a large number of hacked so-called ‘Internet of Things,’ (IoT) devices — mainly routers, IP cameras and digital video recorders (DVRs) that are exposed to the Internet and protected with weak or hard-coded passwords,” Krebs wrote.

Reports indicate that the attack was in response to Krebs reporting on and exposing vDOS, a service run by two Israelis who were offering a DDoS-as-a-Service play and were arrested after Krebs’ story was published.

IoT insecurity

Security researchers have warned that improperly secured IoT devices are more frequently being used to launch DDoS attacks. Symantec last week noted that hackers can easily hijack unsecured IoT devices due to lack of basic security controls and add them to a botnet, which they then use to launch a DDoS attack.

“Poor security on many IoT devices makes them soft targets and often victims may not even know they have been infected,” Symantec wrote. “Attackers are now highly aware of lax IoT security and many pre-program their malware with commonly used and default passwords.”

And while DDoS attacks remain the main purpose of IoT malware, Symantec warned that the proliferation of devices and their increased processing power may create new ways for threat actors to leverage IoT, such as cryptocurrency mining, information stealing and network reconnaissance.

 

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Digital Transformation has become a burning question for all the businesses and the foundation to ride on the wave is being data driven.
DJ Patil & Thomas Davenport mentioned in 2012 HBR article, that Data Scientist is the sexiest job of the century, and how true!  Even the latest Glassdoor ranked Data Scientist at 1 st in top 25 best jobs in America.
Over the last decade there’s been a massive explosion in both the data generated and retained by companies. Uber, Airbnb, Netflix, Wallmart, Amazon, LinkedIn, Twitter all process tons of data every minute and use that for revenue growth, cost reductions and increase in customer satisfaction.
Most industries such as Retail, Banking, Travel, Financial Sector, Healthcare, and Manufacturing want to be able to make better decisions. With speed of change and profitability pressures on the businesses, the ability to take decisions had gone down to real time. Data has become an asset for every company, hence they need someone who can comb through these data sets and apply their logic and use tools to find some patterns and provide insights for future.
Think about Facebook, Twitter and other  social media platforms, smartphone apps, in-store purchase behavior data, online website analytics, and now all connected devices with  internet of things are generating tsunami of new data streams.
All this data is useless if not analyzed for actions or new insights.
The importance of Data Scientists has rose to top due to two key issues:
  • Increased need & desire among businesses to gain greater value from their data
  • Over 80% of data/information that businesses generate and collect is unstructured or semi-structured data that need special treatment 

Data Scientists:

  • Typically requires mix of skills - mathematics, statistics, computer science, machine learning and most importantly business knowledge
  • They need to employ the R or Python programming language to clean and remove irrelevant data
  • Create algorithms to solve the business problems
  • Finally effectively communicate the findings to management

Any company, in any industry, that crunches large volumes of numbers, possesses lots of operational and customer data, or can benefit from social media streams, credit data, consumer research or third-party data sets can benefit from having a data scientist or a data science team.

Top data scientists in the world today are:
  • Kirk D Borne of BoozAllen
  • D J Patil Chief Data Scientist at White House
  • Gregory Piatetsky of kdnuggets
  • Vincent Granville of Analyticsbridge
  • Jonathan Goldman of LinkedIn
  • Ronald Van Loon

Data science will involve all the aspects of statistics, machine leaning, and artificial intelligence, deep learning & cognitive computing with addition of storage from big data.

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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. 

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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

 

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Using Mattermarks’s list of the Top 100 IoT startups in 2015 (ranked by funding, published in Forbes Oct 25, 2015) Ipqwery has looked behind the analytics to reveal the nature of the intellectual property (IP) behind these innovative companies. Our infographic presents a general summary of the IP within the group as a whole, and illustrates the trailing 5-year trends related to IP filing activity.

The vast majority of these companies have both patents (84%) and trademarks (85%) in their IP portfolio. There was a sharp and mostly linear increase in filings for both patents and trademarks, from 2011 through to 2014, with a slight decrease showing in 2015. 2016 looks to be on pace to meet or exceed last year’s filing activity as well. All this is consistent with the ever-expanding number of companies operating within the IoT ecosystem.

A closer look at the top 5 patent class descriptions amongst all patents granted or published yields close results between these classes. This is not surprising given the similar technologies behind many IoT products, such that their patents will incorporate the same or similar descriptions within their claims. Comparatively, there is a wider variance in the Top 5 Trademark classes used, but this speaks more to the wider variety of marketing and branding potential than to the underlying IoT technologies. 

What’s striking in Mattermark’s original analysis of the Top 100 IoT Startups is that 30% of all funding raised by this group as a whole has been concentrated in only the top 5 companies; Jawbone, Genband, Silver Spring Networks, View Glass and Jasper Technologies. Ipqwery’s analysis further reveals that only two of these companies (Silver Spring and Jasper) have Top 5 inventors within the group. In fact, Jasper actually has 2 of the Top 5 inventors. The other top inventors come from Hello and Kineto Wireless.=

The broad-strokes approach of IPqwery’s infographic doesn’t directly illustrate the IP held by any one company, but certainly hints at where exactly this type of analysis could be very useful indeed. For where Mattermark sought to pinpoint where the greatest growth potential (momentum) was within the group of companies by looking at the overall IoT funding environment, IPqwery’s analysis of the general IP trends within this group sheds additional light on the matter, and perhaps raises some additional issues. Wouldn’t potential correlations between IP and funding also be a useful measure of momentum across metrics, and thus shouldn’t IP data be generally more integrated into business growth analytics, from the get go?

Here's a link to a new infographic by IPqwery summarizing the intellectual property held by the Top 100 IoT Startups (2015). 

 

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A smart, highly optimized distributed neural network, based on Intel Edison "Receptive" Nodes

Training ‘complex multi-layer’ neural networks is referred to as deep-learning as these multi-layer neural architectures interpose many neural processing layers between the input data and the predicted output results – hence the use of the word deep in the deep-learning catchphrase.

While the training procedure of large scale network is computationally expensive, evaluating the resulting trained neural network is not, which explains why trained networks can be extremely valuable as they have the ability to very quickly perform complex, real-world pattern recognition tasks on a variety of low-power devices.

These trained networks can perform complex pattern recognition tasks for real-world applications ranging from real-time anomaly detection in Industrial IoT to energy performance optimization in complex industrial systems. The high-value, high accuracy recognition (sometimes better than human) trained models have the ability to be deployed nearly everywhere, which explains the recent resurgence in machine-learning, in particular in deep-learning neural networks.

These architectures can be efficiently implemented on Intel Edison modules to process information quickly and economically, especially in Industrial IoT application.

Our architectural model is based on a proprietary algorithm, called Hierarchical LSTM, able to capture and learn the internal dynamics of physical systems, simply observing the evolution of related time series.

To train efficiently the system, we implemented a greedy, layer based parameter optimization approach, so each device can train one layer at a time, and send the encoded feature to the upper level device, to learn higher levels of abstraction on signal dinamic.

Using Intel Edison as layers "core computing units", we can perform higher sampling rates and frequent retraining, near the system we are observing without the need of a complex cloud architecture, sending just a small amount of encoded data to the cloud.

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5 Innovative Ways IoT Can Help Farms

With the global population expected to reach 9.7 billion people by 2050, innovation in the agricultural industry is more important than ever. This growth brings with it a need for increased food production and a dwindling availability of arable land. Projections show that feeding a population near 9 billion people would require raising global food production by some 70%. To provide for such steep demands, old farming techniques are simply no longer adequate. Thankfully, the agriculture industry is a burgeoning sector within the Internet of Things, and farmers globally are ready to reap the benefits.

Let’s look at a few ways IoT is helping the agriculture industry around the world.

 

  1. Smart Ag Is Environmentally Friendly

Agriculture is responsible for a significant environmental footprint, accounting for 80% to 90% of US water consumption, releasing massive quantities of chemical pesticides, and producing more greenhouse gas emissions with animal agriculture alone than all combined transportation. IoT technology can maximize production capabilities and minimize required resources in order to reduce the industry’s environmental harm. Sensors can be implemented to test agricultural factors such as soil for moisture and nutrient levels to ensure resources are being used as efficiently as possible. This way water and pesticides can be reserved from unnecessary use and alternates can be implemented.

  1. IoT Provides Precision Control for Farmers

The agriculture industry reaches far and wide, and each sector involves too much labor for one person to accomplish alone. As a result, much of the agriculture industry relies heavily on trusting intuition and human judgment. This also means that workers are nearly irreplaceable during illness or absence. Implementation of IoT technology can allow for real-time access to information that otherwise would take too much time or effort to obtain. Managers can have remotely controlled decision-making capabilities at their fingertips rather than having to wait for reports and then send out orders to workers. For example, rather than using valuable workers, drones are now available to monitor crops or apply treatment to specific areas. Also, companies like Lecia have developed GPS-guided combines and other agricultural IoT technologies. Aeris is helping to make solutions like these possible through cellular connectivity, perfect for remote real-time access to critical data.

  1. Farms Are More Productive With IoT

As agricultural businesses gain more insight and control over their operations, they are able to make better business decisions and thus increase productivity. If a farm can use drones or sensors to monitor fields or cattle, for example, then the experience of the farmer can be utilized to make decisions while the manual labor previously needed to monitor these areas can be better repurposed elsewhere. IoT technology can also be applied to agricultural machinery, allowing for preventative maintenance and more accurate reports in the case of a malfunction, saving time and money. As smarter decisions are made regarding resources, productivity will improve.

  1. Smart Farming Saves Money

According to the USDA, some of the top expenses of agricultural businesses include feed, labor, fuel, chemicals, and farm supplies and repairs, all expenditures that can be reduced with the help of IoT. Implementing IoT technology can allow businesses to make better decisions about efficiently using resources including assets, labor, and capital, significantly reducing operational costs. Replacing and improving past techniques will be the only way to maintain a competitive advantage with rising demands and ever-improving technology.

  1. IoT Provides Transparency for Consumers

As more data is made available to the public, consumers demand high quality products more emphatically than ever. Concerns continue to emerge regarding the environmental footprint, personal health effects, and other details surrounding food production. Utilizing IoT technology in production is the only way to provide consumers with the data and transparency that is now the standard expectation, and thus maintain a competitive advantage in the industry.

When you’re ready to connect your agriculture devices to the Internet of Things, contact Aeris for a customized IoT solution.

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Originally Posted by: Mimi Spier

The Internet of Things (IoT) is here to stay—and rapidly evolving. As we try to make sense of IoT’s impact on our lives and businesses, we also continue grappling with the security challenges.

As the IoT security landscape evolves, here are five key insights for designing and implementing IoT deployments for your enterprise.

5 IoT insights vmware airwatch

1. Protect Your People

IoT has opened up a world of possibilities in business, but it has also opened up a host of ways to potentially harm employees and customers. A security breach is not limited to stealing credit card data, anymore. Anyone with the right access could breach firewalls or steal health records. A key challenge of the IoT world is providing the right access to the right people at the right time.

[Related: 5 Real Ways to Enable IoT Success in Your Enterprise]

2. Watch Your Things

As millions of “things” start joining the enterprise network, it also expands the surface area for hackers to breach your system. All these devices will be leveraging public Wi-Fi, cloud, Bluetooth networks, etc., which will create multiple points of vulnerabilities. Your system needs to be designed for security from the bottom up to account for:

A) Device level: better quality devices

B) Data level: encryption and cryptology

C) Network level: certificates and firewalls

D) Application level: login/authorized access

3. Poor Quality of Things

The standards for IoT hardware and software are still evolving, which means until we have any established guidelines, we need to account for a vast range in the quality of “things.” Some of these may be very sophisticated and hardy, while others may be of the cheap disposable variety. Which devices you pick may depend upon factors like cost, usage and the use case itself. However, be warned that lower-quality devices have been used to gain entry to a secure network.

“By 2020, more than 25% of identified attacks in enterprises will involve the Internet of Things (IoT), although the IoT will account for less than 10% of the IT security budget.” Gartner

4. Is Your Network Ready?

One of the biggest challenge for any IT department implementing company-wide IoT projects will be assessing and managing bandwidth. As millions of devices join your network at increasing rates, scaling your network’s bandwidth will be an ongoing struggle. Your bandwidth needs must remain elastic, so you can support your enterprise needs, while minimizing costs. It is critical to minimize exposure of your networks by using, for example, micro-segmentation.

5. Data Is Your Friend

As with protecting any system, predictive maintenance is the way to stay a step ahead of breaches. The usual ways of pushing out timely security patches and software upgrades will continue to be helpful. However, one big advantage of IoT is the sheer amount of data it generates. You can track operational data to create alerts based on anomalies in the system. For example, if someone logs into the system from Atlanta and then, 30 minutes later, logs in again from Palo Alto, the system should raise a red flag.

You can view the original post by clicking Here.

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A Different Kind of IoT Competition

Two months ago, we made the claim that IoT needs a new programming language. That was not a light statement. It was backed by 3 years of heads-down innovation on a fundamental technology: TQL (Thing Query Language). Next month, we will see TQLers’ submitting IoT projects from all over the globe for the TQLOne Competition. Imagine learning a foreign language. Before you become proficient, you are invited to join a poetry contest, against the native speakers. What would happen? In fact, your poem would win for its creativity, albeit with a few spelling errors!!
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Machine to Machine (M2M) and Internet of Things (IoT) realities mean that more and more devices are being deployed and connected to each other. This connectivity is both the promise of IoT (data gathering, intelligent control, analytics, etc.) and its Achilles’ heel. With ubiquitous connectivity comes security threats -- the reason security has received such a high profile in recent discussions of IoT.
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Original article is published at Forbes: link

Have heard about the magic pill? Not sure how it works, but it helps you lose 20 pounds in a week while consuming the same calories as before. And you’ve probably also heard about the scary side effects of that pill. The need for magic pills is appearing in the IoT market as well. Thanks to the explosion of sensors to measure everything imaginable within the Internet of Things, enterprises are confronted with a never-ending buffet of tempting data.

Typically data has been consumed like food: first it is grown, harvested, and prepared. Then this enjoyable meal is ingested into a data warehouse and digested through analytics. Finally we extract the nutritional value and put it to work to improve some part of our operations. Enterprises have evolved to consume data from CRM, ERP, and even the Web that is high in signal nutrition in this genteel, managed manner from which they can project trends or derive useful BI.

sensory

The IoT and its superabundance of sensors completely changes that paradigm and we need to give serious consideration to our data dietary habits if we want to succeed in this new data food chain. Rather than being served nicely prepared data meals, sensor data is the equivalent of opening your mouth in front of some kind of cartoon food fire hose. Data comes in real-time, completely raw, and in such sustained volume that all you can do is keep stuffing it down.

And, as you would expect, your digestion will be compromised. You won’t benefit from that overload of raw IoT data. In fact, we’ll need to change our internal plumbing, our data pipelines, to get the full nutritional benefit of IoT sensor data.

That will require work, but if you can process the data and extract the value, that’s where the real power comes in.  In fact, you can attain something like superpowers. You can have the eyesight of eagles (self-driving cars), the sonar wave perception of dolphins (for detecting objects in the water), and the night vision of owls (for surveillance cameras).If we can digest all this sensor data and use it in creative ways, the potential is enormous. But how can we adapt to handle this sort of data? Doing so demands a new infrastructure with massive storage, real-time ingestion, and multi-genre analytics.

If we can digest all this sensor data and use it in creative ways, the potential is enormous. But how can we adapt to handle this sort of data? Doing so demands a new infrastructure with massive storage, real-time ingestion, and multi-genre analytics.

Massive storage. More than five years ago, Stephen Brobst predicted that the volume of sensor data would soon crush the amount of unstructured data generated by social media(remember when that seemed like a lot?). Sensor data demands extreme scalability.

Real-time ingestion. The infrastructure needs to be able to ingest raw data and determine moment by moment where to land it. Some data demands immediate reaction and should move into memory. Other data is needed in the data warehouse for operational reporting and analytics. Still other data will add benefit as part of a greater aggregation using Hadoop. Instant decisions will help parse where cloud resources are appropriate versus other assets.

Multi-genre analytics. When you have data that you’ve never seen before, you need to transform data and apply different types of algorithms. Some may require advanced analytics and some may just require a standard deviation. Multi-genre analytics allows you to apply multiple analytics models in various forms so that you can quickly discern the value of the data.

The self-driving car is a helpful metaphor. I’ve heard estimates that each vehicle has 60,000 sensors generating terabytes of data per hour. Consider the variety of that data. Data for obstacle detection requires millisecond response and must be recognized as such if it is to be useful. A sensor on the battery to predict replacement requires aggregation to predict a trend over time and does not require real-time responsiveness. Nevertheless both types of data are being created constantly and must be directed appropriately based on the use case.

How does this work at scale? Consider video games. Real-time data is critical to everything from in game advertising, which depends on near instant delivery of the right ad at a contextually appropriate moment, to recommendations and game features that are critical to the user experience and which are highly specific to moments within the game. At the same time, analyzing patterns at scale is critical to understanding and controlling churn and appeal. This is a lot of data to parse on the fly in order to operate effectively.

From a data perspective, we’re going to need a new digestive system if we are to make the most of the data coming in from the IoT. We’ll need vision and creativity as well. It’s an exciting time to be in analytics.

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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

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The Internet of Things is changing the world, heralded as one of the most pivotal technology trends of the modern era. We are getting ready to enter a time where everything, quite literally, is connected to the Internet.

For the industrial sector, this is a new area of exploration. Factories have smart infrastructures that use sensors to relay data about machine performance. Cities have smart grids that monitor everything from traffic to the energy used by streetlights. Hospitals can monitor the health of high-risk, at-home patients.

In other words, we are entering a hacker's dream world.

Recent attacks, like the Christmas 2015 attack on the Ukraine power grid, have shown that the Internet of Things possesses severe vulnerabilities. These weak points can be everything from back doors that allow a hacker access to a system to lack of proper use by untrained workers. If your business uses IoT devices, there’s a good chance they are not secure.

Why are so many systems left vulnerable? Weaknesses often come from the same set of five drivers:

Pa1e9cCyWAh6tGKUeQF4-UQgSS_pv-Yr6XRzUL7riY2wtQDkm4jWXT6ryb65N136M3onsWQW2y87NGr2N_Vof6fB1VljWojgrNIgU32gKScfKJceanEpf2x75eX3RaKRsT196PEr 

Source: Allerin

Whether your company is struggling because your devices were deployed too quickly or operational costs constraints got in the way, your team must take measures to fix security risks. Here are four security flaws:

1. Lack of Encryption

Any device that is connected to the Internet to relay data needs encryption. When communication between devices and facility machines are now encrypted, it provides a doorway for hackers to send malicious updates, steal data, and even take control of the system. 

In 2014, an Israeli security firm took control of cars using a specific connected telematics device that failed to use proper encryption.

2. Failing to Install Updates

Once you have a machine-to-machine communication​ system working properly, it can be easy to forget to install the necessary updates to keep the network secure. 

Yet, hackers are constantly updating their strategies and tactics. Failing to install updates and patches leaves your system vulnerable. 

Even if you’re worried about breaking integrations between systems, you should at the least install every security update released by the vendor. These updates are specifically designed to address vulnerabilities discovered in your devices. After all, if your vendor releases a security update, it’s because they found a problem.

You also should know that updates and patches are not always the final solution to security vulnerabilities. Unfortunately, many manufacturers are not able or willing to provide the necessary support to continue updating their devices. 

To avoid this risk, shop carefully for systems that provide updates and are backed by a trusted company.

3. Poorly Built Networks

The modern industrial network is designed to get tasks done. If the design focuses too much on completing that task, it will leave weak points in security. Things that are obvious when building IT networks are sometimes less obvious when creating industrial DNP3 and other network architecture.

The solution to this risk is fairly simple. Those tasked with building industrial networks need to ensure they are partnering with IT professionals to build networks that are safer from attacks. Security features, like deep packet inspection and network segmentation, should be in place from the beginning.

4. Sensors Outside of the Company's Control

Most of the sensors and other connected pieces that make up a network are controlled by the company. But for some companies, that is not the case. For example, power companies have sensors in their customer's homes. 

Sensors outside of the company's immediate control are hard to secure, which gives hackers access. Currently, cloud-based security using public key services to authenticate devices may be the best solution to this problem.

Don't Take The Risk

Industrial security breaches can cause devastating consequences.​ Therefore, the above risks need to be addressed.

As more industrial facilities rely on the Internet of Things, it's important for company teams to be aware of the potential vulnerabilities. Take security into full consideration.

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