Internet of Things (IoT) has generated a ton of excitement and furious activity. However, I sense some discomfort and even dread in the IoT ecosystem about the future – typical when a field is not growing at a hockey-stick pace . . .
“History may not repeat itself but it rhymes”, Mark Twain may have said. What history does IoT rhyme with?
I have often used this diagram to crisply define IoT.
Even 10 years ago, the first two blocks in the diagram were major challenges; in 2017, sensors, connectivity, cloud and Big Data are entirely manageable. But extracting insights and more importantly, applying the insights in, say an industrial environment, is still a challenge. While there are examples of business value generated by IoT, the larger value proposition beyond these islands of successes is still speculative. How do you make it real in the fastest possible manner?
In a slogan form, the value proposition of IoT is ”Do more at higher quality with better user experience”. Let us consider a generic application scenario in industrial IoT.
IoT Data Science prescribes actions (“prescriptive analytics”) which are implemented, outcomes of which are monitored and improved over time. Today, humans are involved in this chain, either as observers or as actors (picking a tool from the shelf and attaching it to the machine).
BTW, when I mentioned “Better UX” in the slogan, I was referring to this human interaction elements improved by “Artificial Intelligence” via natural language or visual processing.
Today and for the foreseeable future, IoT Data Science is achieved through Machine Learning which I think of as “competence without comprehension” (Dennett, 2017). We cannot even agree on what human intelligence or comprehension is and I want to distance myself from such speculative (but entertaining) parlor games!
Given such a description of the state of IoT art in 2017, it appears to me that what is preventing us from hockey-stick growth is the state of IoT Data Science. The output of IoT Data Science has to serve two purposes: (1) insights for the humans in the loop and (2) lead us to closed-loop automation, BOTH with the business objective of “Do More at Higher Quality” (or increased throughput and continuous improvement).
Machine Learning has to evolve and evolve quickly to meet these two purposes. One, IoT Data Science has to be more “democratized” so that it is easy to deploy for the humans in the loop – this work is underway by many startups and some larger incumbents. Two, Machine Learning has to become *continuous* learning for continuous improvement which is also at hand (NEXT Machine Learning Paradigm: “DYNAMICAL" ML).
With IoT defined as above, when it comes to “rhyming with history”, I make the point (in Neural Plasticity & Machine Learning blog) that the current Machine Learning revolution is NOT like the Industrial Revolution (of steam engine and electrical machines) which caused productivity to soar between 1920 and 1970; it is more like the Printing Press revolution of the 1400s!
Printing press and movable type played a key role in the development of Renaissance, Reformation and the Age of Enlightenment. Printing press created a disruptive change in “information spread” via augmentation of “memory”. Oral tradition depended on how much one can hold in one’s memory; on the printed page, memories last forever (well, almost) and travel anywhere.
Similarly, IoT Data Science is in the early stages of creating disruptive change in “competence spread” via Machine Learning (which is *competence without comprehension*) based on Big Data analysis. Humans can process only a very limited portion of Big Data in their heads; Data Science can make sense of Big Data and provide competence for skilled actions.
To make the correspondence explicit, "information spread" in the present case is "competence spread"; "memory" analog is "learning" and "printed page" is "machine learning".
Just like Information Spread was enhanced by “augmented memory” (via printed page), Competence Spread will be enhanced by Machine Learning. Information Spread and the Printing Press “revolution” resulted in Michelangelo paintings, fractured religions and a new Scientific method. What will Competence Spread and IoT Data Science “revolution” lead to?!
From an abstract point of view, Memory involves more organization in the brain and hence a reduction in entropy. Printed page can hold a lot more “memories” and hence the Printing Press revolution gave us an external way to reduce entropy of “the human system”. Competence is also an exercise in entropy reduction; data get analyzed and organized; insights are drawn. IoT Data Science is very adept at handling tons of Big Data and extracting insights to increase competence; thus, IoT Data Science gives us an external way to reduce entropy.
What does such reduction in entropy mean in practical terms? Recognizing that entropy reduction happens for Human+IoT as a *system*, the immediate opportunity will be in empowering the human element with competence augmentation. What I see emerging quickly is, instead of a “personal” assistant, a Work Assistant which is an individualized “machine learner” enhancing our *work* competence which no doubt, will lead each of us to “do more at higher quality”. Beyond that, there is no telling what amazing things “competence-empowered human comprehension” will create . . .
I am no Industrial IoT futurist; in the Year 1440, Gutenberg could not have foreseen Michelangelo paintings, fractured religions or a new Scientific method! Similarly, standing here in 2017, it is not apparent what new disruptions IoT revolution will spawn that drop entropy precipitously. I for one am excited about the possibilities and surprises in store in the next few decades.
PG Madhavan, Ph.D. - “LEADER . . . of a life in pursuit of excellence . . . in IoT Data Science”
This post original appeared here.
Ever wonder what is the real cost of IOT insecurity?
Well reseachers at the University of California, Berkeley, School of Information recently published a report that attempts to lay out the costs to consumers in the context of DDoS attacks. The report focuses on exploiting vulnerable devices for their computing power and ability to use their network’s bandwidth for cyberattacks—specifically DDoS attacks on Internet domains and servers.
Researchers infected several consumer IoT devices with the Mirai malware and measured how the devices used electricity and bandwidth resources in non-infected and infected state. Their hypothesis: compromised IoT devices participating in a DDoS attack will use more resources (energy and bandwidth) and degrade the performance of a user’s network more than uninfected devices in normal daily operation.
Based on energy and bandwidth consumption they developed calculator to estimate the costs incurred by consumers when their devices are used in DDoS attacks. Two recent and well publicized attacks, and one hypothetical, were calculated:
- Krebs On Security Attack: According to their cost calculator, the total electricity and bandwidth consumption costs borne by consumers in this attack was $323,973.75.
- Dyn, Inc. Attack: They calculate the total cost borne by consumers as $115,307.91.
- "Worst-Case" Attack: This hypothetical “Worst-Case” scenario approximates the costs that could result if the Mirai botnet operated at its peak power using a UDP DDoS attack. The projected cost to consumers of this attack is $68,146,558.13.
Commenting on the study, Bob Noel, Director of Strategic Relationships and Marketing for Plixer said, “Organizations with enslaved IoT devices on their network do not experience a high enough direct cost ($13.50 per device) to force them to worry about this problem. Where awareness and concern may gain traction is through class action lawsuits filed by DDoS victims. DDoS victims can suffer financial losses running into the millions of dollars, and legal action taken against corporations that took part in the distributed attack could be mechanism to recuperate losses. Companies can reduce their risk of participating in DDoS attacks in a number of ways. They must stop deploying IoT as trusted devices, with unfettered access. IoT devices are purposed-built with a very narrow set of communication patterns. Organizations should take advantage of this and operate under a least privilege approach. Network traffic analytics should be used to baseline normal IoT device behavior and alarm on a single packet of data that deviates. In this manner it is easy to identify when an IoT device is participating as a botnet zombie, and organizations can remediate the problem and eliminate their risk of being sued.”
Or as we've argued before, regulation is key. And now that we have an economic cost on IoT insecurity, we have better information for regulators to pursue strategies and legislation for enforcing workable security standards to reduce the negative impacts of IoT devices on society.
With the exponential increase in the IoT and connected devices, it is difficult to ensure scalability, security, and robustness of these devices. Cloud computing platforms like AWS help enterprises accelerate their development to deployment cycles, enhancing robustness and scalability of the entire IoT solution.
People perceive cloud as a platform only for storage and computing. However, there are many other capabilities that cloud offers with cloud computing, such as application deployment, data transfer, database management, etc. Moreover, with the onset of IoT and connected technologies, the role of cloud computing has expanded even more in terms of enabling communication between devices and providing scalability to applications.
How Cloud Computing Helps in IoT Deployment
In today’s time, deploying an IoT solution takes a lot of effort and time, due to the increased number of software applications and hardware integration it requires. Also, when it comes to deploying a new, robust and scalable IoT platform for any industry vertical, it can be very tedious and costly to set up the infrastructure. For example, in a smart factory model, there are many machines and devices to be connected to the cloud. Developing a whole new infrastructure for those Internet of Things applications from the scratch can take up to five to six months’ time in development, deployment, and testing. This prolonged time delay is not appropriate since enterprises need to respond to the market demands quickly, especially when the market competition is too high and when the connected devices and technologies are increasing exponentially. This is where cloud computing plays a crucial role in IoT deployment.
There are several cloud platforms and service providers such as AWS (Amazon Web Services), Azure, and Google Cloud for deploying IoT solutions. Of these, we will focus on the integrating AWS cloud platform in this blog.
Why AWS Cloud Platform
Cloud service platforms like AWS help enterprises accelerate their development cycle from months to a few days and hours, allowing them to build a robust and scalable IoT solution. AWS platform also allows easy and secure on-boarding of billions of devices according to the enterprise’s needs. It is one of the robust platforms for accelerated development, which enables the developers to connect the device to cloud quickly. AWS has recently launched AWS IoT 1-Click that easily triggers the Lambda function for any device to perform a specific action.
AWS is offering various services like cloud computing, machine learning, analytics, storage, IoT platform, security, AR & VR, etc. With AWS, organizations are just paying for the services that they utilize, which provides the benefits of cost reduction and better asset management.
Let us see how an enterprise IoT solution can be leveraged with the AWS IoT platform.
Sensor and Device Connectivity with Edge Analytics
The most important and basic aspect of an IoT solution is to connect all the devices and sensors to the cloud for management and control. Since the development of software and services to connect the devices to the cloud is tedious and time-consuming, AWS IoT Core helps IoT developers with AWS IoT SDK, which allows them to choose SDKs according to their choice of hardware for applications development. These applications help users in managing their IoT devices on air.
- Device gateway also consists of the AWS Greengrass a software agent that runs the computing on the edge for the connected devices. Greengrass consists of the Lambda Function, which allows users to run the rule engines, which are coded for particular events like temperature rise, light intensity, etc. AWS Greengrass also brings the AWS to the devices so that they can perform the local compute on the data when they are already using the cloud for other processes like management and storage. It can also be programmed for transferring only necessary information to the cloud after the local compute has been executed.
- Greengrass enables the device to cloud data security by encrypting the data. This data can be secured for both local and cloud communications. So, no one can access this data without any authentication. It uses the same security model as AWS IoT Core, which contains the mutual device authentication and authorization and secured cloud connectivity.
- Organizations can also create the digital twins, also known as Device Shadowing, for their IoT devices in the AWS cloud. In device shadowing, the current state of IoT devices gets replicated in the cloud virtually and this virtual image can be accessed at the time of no internet. This helps in the prediction of the desired future state of a device. IoT Core then compares this desired state with the previously accounted state and can send the command to the device for making up this difference.
Cloud Computing and Storage
The Internet of Things generates a huge data at every moment. The storage and management of this data require a lot of infrastructure deployments and maintenance efforts. AWS provides storage and computing services, which help enterprises in reducing the infrastructure development cost. These services also provide real-time analytics and accessibility of the data at any moment. Also, the developers can access the required data from the cloud without any delay.
- When we talk about the data management, AWS Kinesis can be considered as a great example of the real-time data streaming and analytics. It continuously analyzes, captures, and stores the huge heterogeneous data (terabytes per hour) that gets generated from the IoT devices or any other resources.
- After the data has been stored, Amazon EC2 (Elastic Compute Cloud) provides a secure, resizable, compute capacity in the cloud. Its web service interface allows developers to scale their computing requirement with minimal efforts. Users can scale up and down their computing resources according to the requirement and they just have to pay for the resources utilized. Apart from that, AWS also provides data storage services as AWS S3 and Glacier. They both provide 99% durability, comprehensive security and compliance capabilities that can help meet even the most stringent regulatory requirements. Amazon S3 and Glacier both allow running powerful analytics on the data on the rest.
- For Database management, AWS provides its service called AWS DynamoDB as NoSQL database that can support both key document-based database. Due to the NoSQL database, it enables benefits like ease of development, scalable performance, high availability, and resilience.
- For data and asset security, AWS has features and services like AWS Identity and Access Management, AWS Key Management Services, and AWS Shield along with the AWS Cloud HSM to enhance the security.
eInfochips (an Arrow company) is an Advanced Consulting Partner for AWS services. We help clients in implementing a highly scalable, reliable, and cost-efficient infrastructure with custom solutions for IoT on the AWS platform. Know more about our AWS services.
Surprise! Operations and IT aren't getting along when it comes to IoT.
451 Research announced new survey results that show operational technology (OT) and IT stakeholders are not aligned on IoT projects. Sure will be harder to drive business results if this doesn't get fixed. Here are some key findings:
Research shows that IT and OT personnel are not well aligned on IoT initiatives, and they need to cross that divide for those enterprise IoT projects to prove viable.
- Only one-third of OT respondents (34%) said they ‘cooperate closely with IT’ on IoT projects from conception to operations.
- A relatively small group of respondents said they were in ‘active conflict’ with IT over IoT, OT professionals are four times more likely to characterize their relationship with IT that way.
- More than half (55%) of the OT survey respondents currently deploy IoT within their organization, and 44% have successfully moved those projects from proof of concept to full-scale deployment.
- New operational efficiencies and data-analytics capabilities are driving successful projects; however, many IoT projects face roadblocks in the trial stage due to the IT and OT divide and budget, staff, and ROI concerns.
Additional details in the graphic below. Want the full findings? 451 Research will happily sell it to you.
You being short-sighted
I do not mean the ophthalmological condition but an approach of believing in things which are close to you only. Often big multinational companies pursuing IoT projects interview customers situated in the same region or country of their location neglecting the fact that the customer located some 5000 km away might have a different opinion. Developing IoT products or services considering only these interviews and later trying to roll them out across the world is like what Henry Ford once said: “Any customer can have a car painted any colour that he wants as long as it is black”.
A more practical approach would be to ask the sales offices worldwide to get these interviews in their region and then consolidate the ideas. This will later result in a new product portfolio which will have at least “something” for everybody.
You are creating IoT-dictatorships within your organization
Having a CDO or IoT Lead at a place is a must if you want to ensure the harmonized implementation of IoT initiatives. However, giving him or her, the complete responsibility of rolling out these initiatives within the organization isn’t a clever move. In a worse case, you will see only those topics being implemented which suit his/her opinion. Better would be to have steering community in a place where experts from different work division or function come together and discuss different IoT projects. Later they decide on which ones to implement and which ones to leave out.
With the support of steering community, the CDO then orchestrates the complete development and harmonized implementation of these projects. Leave some space for feedback from various levels of the organization. This way you can make sure that your IoT portfolio has topics that represent each division or function of your company.
Remember, IoT is what is going to revolutionize your company vertically and horizontally with every employee contributing to it. Hence it cannot be a “one man show”.
You are not communicating your IoT initiative appropriately within the organization
Do your colleagues in far east know about your IoT initiatives? Or do you know what IoT topics they are discussing or projects they are working on? If “No” is the answer to both, then there is something wrong with the organizational communication and hence company’s central IoT strategy. This will result in organization-wide “silos” with each region taking their IoT initiatives.
One may argue that different regions have different needs and hence there is nothing wrong with having customized IoT solutions. But what about organizational and legal aspects of these solutions? Are the local sales and support solely responsible for the projects they sell? Who will take responsibility if issues like data safety, data ownership, system availability come up? These or similar questions can be quickly addressed if there is an IoT steering committee in place which not only defines the roles and responsibilities but ensures the proper (as well as regular) communication of organizational IoT initiatives in the all the regions of their operation.
The jobs within your company are getting redundant.
You run a service company and offer service, maintenance and inspection of some specialized machines. For this, you have a team of competent mechanical and electric engineers who inspect these machines and interact with customer/person responsible on a regular basis. This gives your customer a sense of security regarding service reliability and availability of his devices.
One day you decide to IoT-fy the whole service setup by installing a smart sensor on these machines. Now there is no need of sending your guys to the site since they can monitor the machine running status or any abnormal behaviour from their respective offices. You later realize that there is no need of having 20-30 individuals to carry out monitoring since this can be done by fewer experts. Moreover, you suddenly feel a need of hiring more IT manpower since you cannot expect the middle-aged service engineer to learn IT skills overnight. As a consequence, you decide to replace mechanical engineers with a few IT experts. Your customers might not be much happier with this move since they stop seeing the service engineer who used to visit them regularly.
Creating a change management plan could be a wise move before implementing any IoT project rather than creating redundancies and not knowing how to deal with it. New technology should create more jobs and no layoffs.
Your IoT team is not diverse enough.
New technologies need new ideas. That can only be possible if your steering committee consists of a team of experts with different functional/sectoral as well as cultural expertise. The topic of IoT has many facets which can be addressed well by a team capable of seeing the things from different perspectives.
Having a diverse team for such strategically important projects shouldn’t be an issue, especially if the organization has a significant setup and is present globally. It is however still a rare phenomenon for many companies in Europe. Entirely different than what one usually sees in the US.
The list of factors hampering a successful rollout of an IoT strategy within an organization could be longer. The likelihood of occurrence of the above-mentioned factors is, however, higher. Therefore you need to plan and act strategically, keeping an eye on the long-term perspective and benefits.
After years of evangelization waiting for the promises of the Internet of Things (IoT) to come true it seems that we are finally close to reaching the trough of disillusionment phase, we begin to forget all the hype generated so far and focus on reality. A harsh reality that involves selling IoT and not continue selling smoke anymore
THE TIME TO SELL IoT IS NOW
The sale of IoT is perhaps more complex than the sale of other disruptive technologies such as Big Data, Cloud or AI and maybe as complex as Blockchain today. In the article “ Welcome to the first “Selling IoT” Master Class!” I commented how it should be the evolution of M2M Vendors for sell IoT and how should be the evolution of IT Technology Vendors for sell IoT. However, many of these companies still have difficulty in forming and finding good sellers of IoT
The truth is that nowadays it does not make any sense to sell IoT as a technology. Enterprise buyers only want to buy solutions that provide measurable business outcomes while, in the other side, many IoT Vendors only want to sell their portfolio of products and services that have been categorized under the umbrella of IoT, either as quickly as possible or at the lowest possible cost.
During last 5 years, I have been analysing how IoT companies sell their products and services. Some of my customers (Start-ups, Device vendors, Telco Operators, Platform vendors, Distributors, Industry Applications, System Integrators) requested me to create IoT sales material to train their sales team about how to sell their IoT solutions and services. And sometimes I also helped Head Hunters or customers searching for IoT sales experts
Based on this varied experience I have launched this year a new service: “IoT Sales Workshops” to help companies train their internal teams in how to sell IoT. Here are some of the lessons I learned
- There is a time for act as an IoT Sales generalist and a time for act as an IoT Specialist.
- You need to adapt the IoT storytelling based on your audience.
- Being an IoT expert is not synonymous with being successful in selling IoT.
- You need to show how companies can get more out of IoT by solving a specific business problem.
- Make it easy for the customer to see the benefits of your IoT product or IoT service and what is the value you are adding.
- Given the complexity and specialization of IoT by vertical, explain companies the need to focus more closely at business cases, on their IoT business model as well as the ROI over three to four years before jumping into technology.
- You need to be patient because IoT selling is not easy and takes time align strategy and business needs with the IoT products and services you are selling.
- Build a strong ecosystem and make easy the customer the adoption of end to end IoT solution collaborating with your partners.
- Train your IoT Business and Technical experts to get better at telling stories. Design a new marketing and sales communications playbook. Keep it simple. Build your narrative from the foundation up – one idea at a time.
- If you want an IoT sales expert you need to pay for it (not expect miracles from external sales agents working on commission base).
- IoT Sales is a full-time job. You will not have time to other enterprise activities.
- Selling IoT to large enterprises is a teamwork process.
- Be Persistent. Do not expect big deals soon.
- Be Passionate, Be Ambitious, Be Disruptive to sell IoT.
I do not consider myself an IoT sales expert. And of course, neither a superman of sales. In fact, I have shied away from classifying myself in the role of a pure salesperson even though over time I have given a weight and value to this work that once seemed derogatory to me.
Sell IoT is not easy. In a few years we will have forgotten of the word IoT and we will be selling new hypes, but in the mean time you need to be prepared for disillusionment moments, long sales cycles and a lot of work with sometimes poor results. However, I do not know if will be 2020, suddenly if you persevere you probably will be awarded as the best IoT sales expert and you finally will earn a lot of money.
Be Persistent, Be Passionate, Be Ambitious, Be Disruptive to sell IoT
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I've seen a lot of different thoughts about "original equipment manufacturers" and "original design manufacturers" recently, so I figured I'd offer my observations from my time working in Shenzhen for my IoT company.
Backstory: we’re partnered with Qualcomm to cloud enable bluetooth mesh technology across myriad US, Asian, and European based companies, primarily for lighting and smart home products in consumer/commercial markets. I spent about 6 months in Shenzhen and Hong Kong during 2017 putting together the supply chain partnerships.
From what I’ve experienced, “brand,” i.e. the companies we’re familiar with as consumers, and Original Equipment Manufacturer “OEM” are used interchangeably, while Original Design Manufacturer “ODM” refers to the “factory.”
In most of my interactions, there is a tight albeit painful relationship between the OEM and ODM in consumer electronics because cooperation between multiple vendors is often required to get a product to market, especially in IoT. Typically, the most differentiated intellectual property (IP) is in the hands of the OEM (brand)— industrial design, software, firmware, and it’s in their best interests to obfuscate as much as possible throughout the supply chain to make it harder to replicate the technology, which everyone assumes will happen. And it does. This is especially true during the rise of the IoT, where connectivity challenges plague both sides of the pond, and clever solutions are the 11th hour superpower everyone is fighting to find first to use as leverage in the supply chain.
There is another class of manufacturers— not sure the technical name, but we call them “module makers” — companies that specialize in the design and production of drop-in PCB modules for various connectivity chipsets to make them easier to productize. An example would be ITON, who provides chips for several of GE’s products to the prime ODM (such as Leedarson or Eastfield) who is responsible for final assembly (note: many ODMs are also module makers— they keep chips in house to maximize control and profits).
Both ODMs and module makers participate in a process of product innovation that presupposes the market. Chipmakers (and other tech vendors) like Qualcomm send their reps out to the factories to demo new silicon technology in the form of a “reference design” in a bid to get the ODM to create a module or product based on that chipset that answers to a trend they’ve noticed from their OEM/brand customers. In this way, the ODM bears the R&D cost as a bet for business, but doing so gives them a chance to retain the right to get a royalty on every module sold. Ask an ODM to hand over any firmware they've made and they’ll tell you with their sweet puppy dog eyes “eat my shorts” because it’s how they keep you from just taking everything to another vendor.
For brands like Home Depot (or more generally companies less interested in designing hardware) these ODMs are essential because they are flexible enough to develop a catalog of partially developed products on speculation— whatever successfully sells up the food chain at Home Depot, they make real (note: the “make real” part is where a lot hits the fan because this stuff is hard to scale).
The OEM-ODM-module maker ecosystem creates a sort of “it takes a village to make a product” atmosphere, but with grumpy uncles, annoying neighbors, and meddling kids abounding. There's a constant sense of quiet espionage on both sides, although that tends to get better if you develop a direct relationship with your mfg partners. Western business has evolved to sustain trust with purely transactional relationships-- this is way less true in places like China. Go to lunch with them and take them to dinner a few times, invite them to Macau, get them drunk and having fun with you. These relationships are insurance policies on getting screwed. Further, having boots on the ground near your manufacturing is practically a requirement nowadays if you want to have any hope of your supply chain operating smoothly.
In the case of a brand like Apple, who meticulously defines and controls every little detail of their product and supply chain works with an Electronic Manufacturing Services company “EMS” like Foxconn who primarily invest only in building other designs precisely to specification.
So OEM v. EMS: OEM: “build this for me, exactly like this, and don’t ask too many questions, or I’ll eat your children.”
The ODM/OEM relationship is a bit shakier:
OEM: “build this for me, and pretty please do your best not to use lead paint or explode my users.”
All that said, many companies I’ve encountered are chimeric— companies that usually do business as an EMS could also be caught as an ODM if the opportunity is right. I’ve wracked my brain over how to approach meetings with ODMs that also have an OEM/brand side to the company. The ODM side is a potential partner while the OEM side is a potential customer— in the already confusing world of IoT this can be quite the rollercoaster.
I could be off, but the cash value of the above has navigated me through hella lots of conversations from ivory tower to where the dog food gets made. It is a truly global and complex web of associations, across cultural, language, political, and social boundaries. Read “Poorly Made in China” and “Barbarians at the Gate” to see the differences in East vs. West strategies for business success, which I see as orthogonal values of Replication and Dominance.
If you’re interested, here’s a great article by a Shenzhen based supply chain expert: https://www.linkedin.com/pulse/3-types-partners-product-managers-can-use-development-changtsong-lin/
Thanks for reading! Our company is expert at IoT integrations, and we thrive on building ecosystems of partners with positive feedback loops on new services and revenue streams. Kindred spririts, please reach out to me at [email protected]
COO @ Droplit
The Internet of Things is revolutionizing the retail industry, coming in it with the improved shopping experience, automated business processes, enhanced digital marketing, and optimized inventory and supply chain management.
Providing retailers with various advantages, IoT technology also enables them to boost sales and increase customer loyalty. Oracle discovered that when applying RFID tags, retail companies can achieve 99% inventory accuracy, a 50% reduction in out-of-stocks, and a 70% reduction in shrinkage.
The global IoT retail market is predicted to grow from $14 billion in 2015 to $36 billion by 2020, at a CAGR of 20%. In its report about the Internet of Things, Verizon found that retailers believe in the IoT potential and have a positive attitude towards adopting IoT in their work:
- 77% of retailers said that IoT solutions help improve the customer experience;
- 89% of companies said they got the understanding of customer shopping habits, needs, and preferences thanks to using IoT solutions.
Promising to innovate and transform the retail industry, IoT solutions are becoming widely introduced for solving a wide range of issues. Here I’ve listed the main use cases describing how IoT is applied by retailers and what benefits they do receive.
IoT applications in Retail
Beacon alerts & in-store navigation
Customer interactions are a key success factor in all business. Through beacons, retailers can easily reach the user audience, increase customer loyalty, and raise profit. Beacons are IoT Bluetooth-enabled devices that use low-energy Bluetooth connections to automatically send push notifications directly to user smartphones once they appear in the operating area.
As beacons are small, they can be attached to almost any place, for instance, walls and counters. In the retail industry, beacons are mainly used for customer in-store navigation, sending push notifications, and collecting customer data.
In connection with mobile applications, retailers can motivate customers to make more purchases by notifying them about discounts and special offerings when they enter the coverage zone, generally near a certain shop. Also, in large shopping centers, beacons are irreplaceable for navigating customer and showing them the best routes to the place they need.
Customer data plays a key role in any business dealing with customers. Retail companies do need to know their audience in order to make them make purchases and increase profit while delivering an amazing personalized experience. Satisfied customers are returning clients.)
IoT solutions suit great for collecting customer data, including the determination of customer buying habits, needs, preferences, favorite routes in the shopping center, and the most popular goods as well.
By sending all these data to the analytical system, where it’s processed and analyzed, retailers can find out what they should improve. In some cases, for example, it will be better to change the placement of shelves or clothes. Also, with the audience understanding retailers can launch successful marketing campaigns and provide personalization.
Personalized shopping experience
Traditionally, customer relationships were built on the basis of face-to-face communications. For now, personalized experience takes the center stage and significantly impacts consumer purchasing decisions.
To boost sales and retain customers, retail companies are adopting IoT solutions to deliver the best shopping experience possible. By using beacons, mobile apps, push notifications, and customer analytics, retailers get the ability to understand the needs and preferences of their customers and ensure successful targeting when creating advertising campaigns.
Supply chain management
Like in many other sectors, supply chain management takes an important part in the retail too. Retailers integrate IoT solutions for load tracking, driver activity monitoring, tracing the delivery process, transportation management, as well as viewing load/driver location in real time. This way, the Internet of Things can enable a transparent supply chain management and help achieve “just-in-time” delivery much easier.
Optimized asset management
IoT applications are widely used for asset tracking and management. Using RFID tags, mobile apps, and other technologies for inventory tracking, retail companies can accomplish up to 100% inventory accuracy, minimize unexpected out-of-stocks, enable end-to-end store inventory management, and increase sales margins by up to 10% as a result.
What’s more, IoT solutions provide retailers with the ability to track the assortment of goods, analyze product popularity, and check out the information about goods any time they need, including their availability in the store, brand name, price, and description.
As you see, there are many useful IoT applications in the retail industry. With the use of additional devices and technologies, improvements in sensors, enhanced connectivity and machine learning tools, retailers automate operations, optimize various processes, reduce costs, and deliver the personalized experience.
As demand for location services in all areas of the Internet of Things (IoT) grows, so too has the requirement for precision location. For many applications, especially those that need to scale to cover large areas, providing ”proximity zone” types of location is simply not accurate enough. That means the old way of determining location—primarily using Bluetooth beacons—is no longer sufficient.
Bluetooth beacons have been the go-to solution for determining location for years, but they have three limiting factors:
- Beacons only work with smartphones, not tags, which limits how they can be used
- They are able to locate objects in best case within 3-4 meters, which is fine for determining a general location, but is not refined enough to meet the requirements for many of today’s applications
- Beacons are battery-operated, which impacts their ability to deliver real-time location; frequent transmissions drain the device’s battery, meaning frequent replacements are necessary
The shortcoming of beacons and other location technologies that rely on smartphones has spawned an industry shift to a more network-centric approach, with the intelligence moving to the receiver antenna and a centralized software application, rather than the intelligence residing in a smartphone app. That, in turn, has launched the development of a wide range of active, low-cost Bluetooth Low Energy (BLE) tags with long battery life and possible on-board sensors.
Another shift occurring is a change in how signals from these tags are measured to determine location. The traditional method—using signal strength to estimate location—does not take into consideration how the signal will be impacted by its environment. While a weak signal could indicate an object is far away from a beacon, it’s also possible a physical object, such as a concrete pillar or wall, is impacting the signal.
Two new approaches are emerging for BLE angle estimation. The first is based on the signal’s Angle of Arrival (AoA)—the precise direction the device is from the receiver antenna arrays. With AoA, multiple antennas are used within the same devices to measure the signal. This allows the antenna to locate a tag or smartphone with accuracy of 10 to 20 centimeters, not meters.
The second approach considers the signal’s Angle of Departure (AoD). In this approach, the location intelligence is moved back to the mobile devices. The AoD approach works like "indoor GPS," where the fixed infrastructure devices (aka Locators) are only broadcasting and are not aware of the receiving devices, similarly to how a GPS Satellite works. This means the capability to locate an unlimited amount of devices, and no privacy issues.
As the use cases for indoor location services continue to grow, with every industry from manufacturing and logistics to healthcare and retail, to law enforcement and beyond clamoring for more precision, new approaches beyond Bluetooth beacons need to be considered. The AoA and AoD methodologies are quickly gaining momentum as the next generation of location technology.
Guest post by Antti Kainulainen is CTO & cofounder of Quuppa. Before Quuppa, he was with Nokia Research Center (NRC) during 2005-2012, where he was the lead engineer in several projects related to indoor positioning. He also took part in the standardization work of the Bluetooth Wireless technology. Antti received his M.Sc. degree in technology from Helsinki University of Technology in 2007. He has 16 granted patents and 22 pending patent applications. More at www.quuppa.com
"One day I'm in my cubicle, Steve shows up with someone I've never met before. He asks me, 'Guy, what do you think of this company Knoware?'. I said, 'Well Steve, it is a mediocre company, mediocre product, lot of drilling practises, doesn't make full use of graphics, just basic mediocrity, nothing that strategic for us.' He says to me, 'I want you to meet the CEO of Knoware.' So that's what was like working for Steve Jobs. ‘You always have to be on the ball.
A lot of water has flowed under the bridge since then. The flow of information has also changed the way we live in today’s world.
Your mark on the world begins…
Every morning when we read a newspaper having out so much information we came to know the latest happening in the world (of course in details), yeah you are right even the internet edition also. This is just a very basic example of IoT. All our Railways, Air and even sea networks are connected with the help of IoT. We can take the example of banking. It is very easy to transact any amount of money from part of the world to other with help of e-commerce. We can purchase anything online with help of debit and credit cards. This has made our lives more and more simple. People are working on the internet without really having to go outside to their workplace. IoT has changed the whole scenario. Companies can share technologies online. Even the doctors can guide the other doctors while operating on a patient with the help of Information Technology. A whole new world is coming our way. Technology is allowing us to reimagine our future transportation system. Advances in connected automation, navigation, communication, robotics, and smart cities—coupled with a surge in transportation-related data—will dramatically change how we travel and deliver goods and services. Automation in the field of transportation is everywhere. Have we as humans become an afterthought? We order service on our smartphones, we manoeuvre around in increasingly automated vehicles, we ride in driverless transport, and we will increasingly find ourselves sharing our highways and byways with drones and other unmanned craft.
1) SaaS & Bring Your Own Device
Global movements such as BYOD and SaaS, where consumerisation of IT and mobility are drastically changing the capabilities of employees and their expectations of a workspace. Building your own apps is the ideal way to mitigate the risk of BYOD and SaaS. An organisation can provide those that only allow the user to access what they need. The enter-prise’s concern is the data; the employee’s concern is the device. In the IT security world, we care about both. Now that most of the organizations started adopting BYOD in some form, it is not just their personal iPads and laptops that users are bringing into the office, they are also using the consumer apps available in their personal device for work purpose which leads to the next wave in mobility. In the very near future BYOD won’t be a ‘trend’ but a norm no one would think twice about.
2) The Emergence of Big Data
"Big data" alluringly holds out the promise of competitive advantages to companies that can use it to unlock secrets about customers, website usage and other key elements of their business operations. Big Data now stream from daily life: from phones and credit cards and televisions and computers; from the infrastructure of cities; from sensor-equipped buildings, trains, buses, planes, bridges, and factories. It's estimated that 43 trillion gigabytes of new data will be created by the year 2020.
3) Cloud computing: How it's transforming the role of IT
Market conditions require significant change and many organizations are using this driver as an opportunity to simplify their applications and data through rationalization and technology innovations such as Cloud Computing. Cloud is defined as any cloud service where consumers are able to access software applications over the internet. The applications are hosted in “the cloud” and can be used for a wide range of tasks for both individuals and organisations. Google, Twitter, Facebook and Flickr are all examples of SaaS, with users able to access the services via any internet enabled device. Cloud is also the fastest growing because it keeps pace with emerging and future business models than on-premise systems, the majority of which were designed for business models of the past.
The next step, moving towards virtual workspaces, can make organisations far more agile but only if those responsible for the IT (and in effect, the productivity) of the employees understand the relationship employees have with their devices and how these change throughout the day based on their personal preference – be it a smartphone for the train, a tablet device for a client meeting or a laptop for remote working at home.
4) Millions of sensitive IT services exposed to the Internet
All the more the Internet is becoming more and more important for nearly everybody as it is one of the newest and most forward-looking media and surely "the" medium of the future. These advances—in fields such as robotics, A.I., computing, synthetic biology, 3D printing, medicine, and nanomaterials—are making it possible for small teams to do what was once possible only for governments and large corporations: solve the grand challenges in education, water, food, shelter, health, and security. Technology is, today, moving faster than ever. Advances that took decades, sometime centuries, such as the development of telephones, airplanes, and the first computers, now happen in years.
The macro trends that have changed the playing field in the past 10 years have been cloud, mobility, Big Data, and social networking. An even bigger trend ahead will be the Internet of Things that will extend information technology into every aspect of our lives. IT has become more agile and responsive to the needs of the business. While cloud was considered hype just a few years ago, the cloud in its many forms, private, public, hybrid, is transforming IT into a service model. IT leaders who embraced these changes have been able to do more with less and have proven their strategic value.
According to Steve, the iPhone was originally a tablet project. Partway through the R&D process, he said, “Hmm, we can make a phone out of this.” After the launch, many people rewrote history and said that the purpose of the iPhone was to reinvent the future of telephony.
Today, technology is, moving faster than ever. The ubiquity of network connectivity and the proliferation of smart devices (such as sensors, signs, phones, tablets, lights, and drones) have created platforms upon which every enterprise can innovate. Since the past few years we have also seen countless innovations that improve our daily lives. From Internet technology to finance to genetics and beyond - we have seen technologies such as mobile, social media, smartphones, big data, predictive analytics, and cloud, among others are fundamentally different than the preceding IT-based technologies. And advances in science and technology have changed the way we communicate, our thought processes, exchange views, understand the way we relate to one another and think about what it means to be a real Innovative change maker. Perhaps one day you too can be a part of reinventing something which is new, timely, relevant and useful.
Raj Kosaraju specializes on Cloud Computing, Data Warehousing, Business Intelligence, Supply Chain Management, Big Data & IoT.
What is a smart city? The answer depends on who you ask. Solutions providers will tell you it’s smart parking, smart lighting or anything to do with technology. City officials may tell you it’s about conducting city business online, such as searching records or applying for permits. City residents may tell you it’s the ease of getting around, or about crime reduction. Everyone is right. A smart city, built properly, will have different value for different stakeholders. They may not think of their city as a “smart”city. They know it only as a place they want to live in, work in, and be a part of. To build this type of city, you have to first build the smart city ecosystem.
A smart city is built on technology, but focused on outcomes
A scan of the various smart city definitions found that technology is a common element. For example, TechTarget defines a smart city as “a municipality that uses information and communication technologies to increase operational efficiency, share information with the public and improve both the quality of government services and citizen welfare”. The Institute of Electrical and Electronics Engineers (IEEE) envisions a smart city as one that brings together technology, government and society to enable the following characteristics: a smart economy, smart mobility, a smart environment, smart people, smart living, smart governance.
But what does a smart city really do? Our scan of smart city projects worldwide showed that initiatives fell into one or more smart city “outcomes” (Figure One).
As a starting point, we define a smart city is one that uses technology extensively to achieve key outcomes for its various stakeholders, including residents, businesses, municipal organizations and visitors.
The smart city ecosystem framework
Figure Two shows our framework for a smart city ecosystem. A vibrant and sustainable city is an ecosystem comprised of people, organizations and businesses, policies, laws and processes integrated together to create the desired outcomes shown in Figure One. This city is adaptive, responsive and always relevant to all those who live, work in and visit the city. A smart city integrates technology to accelerate, facilitate, and transform this ecosystem.
Four types of value creators
There are four types of value creators in the smart city ecosystem. They create and consume value around one of the outcomes listed in Figure One.
When people think of a smart city, they automatically think of services provided by municipal and quasi-government agencies, such as smart parking, smart water management, smart lighting, and so on. In fact, there are three other value providers and users that co-exist in the smart city – businesses and organizations, communities, and residents.
Businesses and organizations may create services that use and create information to create outcomes for its stakeholders. Some examples of “smart” businesses include Uber and Lyft for personal mobility, NextDoor for information sharing, and Waze/Google for traffic and commute planning.
Communities are miniature smart cities, but with very localized needs. Some examples of potential smart communities include university campuses, office parks, airports, cargo ports, multi-dwelling unit (MDU) or apartment complexes, housing developments/neighborhoods, business districts and even individual “smart” buildings. They have needs for smart services that may be tailored specifically for their stakeholders.
Residents or individual citizens are also smart services providers in the smart city. A resident living near a dangerous street intersection can point a camera at the intersection and stream that information live to traffic planners and police. Residents place air quality measurement sensors on their properties to monitor pollution and pollen levels during certain times of the year, and make that information available to other community members. Residents can choose to make these smart services temporary or permanent, and free or fee based.
The Smart City is built on layers
A smart city is an ecosystem comprised of multiple “capability layers”. While technology is a critical enabler, it is just one of many foundational capabilities that every smart city must have. No one capability is more important than the rest. Each capabilities plays a different role in the smart city. These capabilities must integrate and coordinate with each other to carry out its mission.
Value layer. This is the most visible layer for city residents, businesses, visitors, workers, students, tourists and others. This layer is the catalog of smart city services or “use cases”, centered around the outcomes (Figure One), and offered by value creators and consumed by the city stakeholders.
Innovation layer. To stay relevant, value creators in the smart city must continuously innovate and update its services for its stakeholders. Smart cities proactively facilitate this through a variety of innovation programs, including labs, innovation zones, training, ideation workshops, skills development and partnerships with universities and businesses.
Governance, management and operations layer. The smart city creates disruption and results in digital transformation of existing processes and services. Smart city management models must integrate a new ecosystem of value creators and innovators. They must plan, support and monetize new business models, processes and services. They must upgrade their existing infrastructure and management processes to support “smart” services. Finally, they must measure the performance of the city with a new set of metrics.
Policy, processes, and public-private partnerships, and financing layer. The smart city doesn’t just magically appear one day. An entirely new set of engagement models, rules, financing sources, and partners are required to build, operate and maintain the smart city. Cities must develop a new set of “smart” competencies in order to get and stay in the “smart city game”.
Information and data layer. The lifeblood of the smart city is information. The smart city must facilitate this in several ways, including open data initiatives, data marketplaces, analytics services, and monetization policies. Equally important, they must have programs that encourage data sharing and privacy policies to protect what and how data is gathered.
Connectivity, accessibility and security layer. People, things and systems are interconnected in the smart city. The ability to seamlessly connect all three, manage and verify who and what is connected and shared, while protecting the information and users is crucial. The highest priorities for smart cities are to provide a seamless layer of trusted connections.
Smart city technology infrastructure layer. Most people automatically think of technology when talking about smart cities. The smart city technology infrastructure must scale beyond the traditional municipal users and support a new class of value creators, and city/user stakeholders.
Leveraging the smart city ecosystem framework
The smart city is a complex ecosystem of people, processes, policies, technology and other enablers working together to deliver a set of outcomes. The smart city is not “owned” exclusively by the city. Other value creators are also involved, sometimes working in collaboration and sometimes by themselves. Successful and sustainable smart cities take a programmatic approach to engage its stakeholders across the ecosystem.
Our research has found that many cities are not taking an ecosystem approach to smart city projects. This is due in part to smart city projects being managed by the Information Technology (IT) organization where their charter is on systems development and deployment. In contrast, more experienced smart cities manage their smart city programs through internal cross functional “Transformation” or “Innovation” organizations.
Regardless of where cities are in their smart city journey, they must get ahead of the “curve” with smart city projects. They begin by thinking in terms of building the broader ecosystem in order to create a sustainable and scalable smart city. Key next steps include:
- Understand the smart city ecosystem framework and tailor it to the realities of their specific city. Incorporate this model into the development of their smart city vision, strategy and execution plans.
- Relative to the smart city ecosystem framework, identify current capabilities and gaps across the various layers. Understand what is needed to support the four types of value creators.
- Evaluate existing and new smart city projects and initiatives against the ecosystem framework. Use this framework to identify what is missing from the project plans and what is needed to make the projects fully successful.
- Prioritize and develop competencies across the various ecosystem layers. A smart city requires new skills and competencies. Augment existing capabilities through strategic partnerships and contracting with service providers, as required.
Benson Chan is an innovation catalyst at Strategy of Things, helping companies transform the Internet of Things into the Innovation of Things through its innovation laboratory, research analyst, consulting and acceleration (execution) services. He has over 25 years of scaling innovative businesses and bringing innovations to market for Fortune 500 and start-up companies. Benson shares his deep experiences in strategy, business development, marketing, product management, engineering and operations management to help IoTCentral readers address strategic and practical IoT issues.
This post was co-authored with Renil Paramel, an IoT Innovation Catalyst, Strategist and Senior Partner at Strategy of Things.
The predicted growth of the IoT market in manufacturing is unprecedented. At the moment, Markets and Markets researchers predict it to reach $13.49 billion by 2020. Just to give you some perspective, in 2015 the value of this market was estimated at $4.11 billion. The main IoT technology applications in manufacturing revolve around enhancing connectivity and automation. The main goal of this tech is to maximize the efficiency of the manufacturing process while minimizing its costs. The benefits of utilizing digital solutions in this industry are a great motivation for the developers as seeing what has already been achieved prompts them to see how far they can push these solutions.
The most important benefits, no doubt responsible for such a tremendous growth of the IoT manufacturing industry, include:
Boost in Work Efficiency
Constant improvement of the manufacturing operation is one of the main goals for any industrial business owner. Implementing IoT technology on any level of the manufacturing allows to:
- Automate the production process, or some of its steps
- Pre-test new ideas and designs (using a combination of advanced modeling and testing solutions)
- Analyze the production process and identify its strengths and weaknesses
- Save time and money for the business by increasing the efficiency of both the production line and employees
- Monitor the manufacturing business performance at all times, analyze the data, and use this information for accurate predictions
Steady Improvements in Performance
The most important benefit of the contemporary IoT solutions is their ability to improve constantly by simply ‘doing their job’. The AI that governs them is usually programmed to process data collected during the manufacturing process and optimizing that process based on it.
As the system is regulated by the AI developed specifically for it, the efficiency and accuracy of these changes and advancements are greater than any settings set by man. However, making manual adjustments is possible and this will add another layer to the machine’s betterment. The intuitive operation systems of today will memorize the most effective patterns in the production process and find a multitude of ways to achieve or even improve those results. They will do this with utmost accuracy and speed. Utilizing these particular solutions can make even a small manufacturing business into a big player on its market.
Creating the Perfect Environment for Innovation
Manufacturing facilities reigned by IoT technology are extremely flexible. This means that the business owner is able to integrate new solutions quickly and boost the production process’ efficiency right away.
Most importantly, implementing this technology allows to step away from the traditional linear production process. This, in turn, leads to the creation of more efficient singular production cycles organized into a cohesive system that can adjust to the change in manufacturing demand immediately. Such a scheme allows for the most efficient use of resources.
This kind of ‘cluster’ manufacturing also enables the owner to monitor the entire system more easily. One can determine where an issue occurs and have other sectors pick up the slack if possible. In any case, this scheme allows making quick and more accurate fixes for any problems.
Allowing for Predictive Maintenance
Predictive maintenance is a very efficient method of cutting the manufacturing costs. It is exactly what the name states, a maintenance based on predictions. It’s a step up from preventative maintenance as it’s more effectively targeted.
Predictive analytics drive this solution and allow you to maximize the equipment output while minimizing the costs for its maintenance. Note that using such technology also helps you save money you would have lost due to the manufacturing process stopping.
The IoT for the manufacturing industry develops extremely fast with dozens of solutions released for any kind of business. Embracing this technology now can not only give one an edge over the competition. With the high popularity rate of this tech, not using any of these solutions is sure to marginalize the business.
Adam Flamberg is a consultant at DO Supply.
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