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The dream of making money with IoT, AI and Blockchain

Have you ever think about how could you make money with the Internet of Things (IoT) or Artificial Intelligence (AI) and of course with Blockchain?  What would happen if you could use the three of them in a new business model?.  Apparently, Success, Success and Success.

In the next sections I provide information of some business models implemented with these three technologies.

IoT Business Models

As IoT moves past its infancy, certain trends and economic realities are becoming clear. Perhaps the most significant of those is the realisation that traditional hardware business models just don’t work in IoT. Take a look at “The top 5 most successful IoT business models” that have emerged as particularly effective applications for IoT.

If any of you is building an IoT product, this article ” IoT Business Models For Monetizing Your IoT Product”  show how to make money with IoT.

Zack Supalla, the founder and CEO of Particle, an Internet of Things (IoT) startup, suggest “6 ways to make money in IoT”.

Finally, in “How IoT is Spawning Better Business Models” we can read three ways companies like Rolls Royce, Peloton, MTailor or STYR Lab  was rethinking their business model and have created revolution in the marketplace. 

Blockchain Business Models 

It sounds repetitive, but yes "Blockchain technology may disrupt the existing business models”. The authors´ s findings concerning the implications of blockchain technology for business models are summarised in the following picture.

 

Do you think that blockchain will likely to cut into big-players’ revenues? Then, this article: “New Blockchain-Based Business Models Set to Disrupt Facebook and Others”, is for you.

If you are ambitious and you are planning to build a viable business on blockchain, then read “Building an International Business Model on Blockchain”.

I am also an advocate of the coming era of decentralization (at least in my most optimistic version) and Blockchain is a step more to create value when the End of All Corporate Business Models will arrive.

AI Business Models 

Companies from all industries, of all shapes and sizes are thus faced with an important set of questions: Which AI business models and applications can I use ? And what technologies and infrastructures are required?.

It seems that we all are convinced that artificial intelligence is now the most important general-purpose technology in the world that can drive changes at existing business models. Not surprised then, that  AI is Revolutionizing Business Models.  The “data trap” strategy, that in venture capitalist Matt Turck’s words consists of offering (often for free) products that can initialize a data network effect. In addition, the user experience and the design are becoming tangibly relevant for AI, and this creates friction in early stage companies with limited resources to be allocated between engineers, business, and design.

This article introduces  some good examples of AI business models :

New Business models with the intersection of IoT, AI and Blockchain

With IoT we are connecting the Digital to the Physical world. Connected objects offers a host of new opportunities for companies, especially in terms of creating new services. The amount of data generated by the billions of connected objects will be the perfect complementary feed to many AI applications. Finally, blockchain technology could be used to secure the ‘internet of things’ and create smart contracts in a decentralized infrastructure that boost the democratization of technology and creation of sustainable communities.

You must remember that new business models that include IoT, AI and blockchain need among other characteristics: Volume and Scalability. Volume of devices, Volume of data, Volume of customers, volume of developers and powerful ecosystems to escalate. 

Good luck in your search and implementation of your new business model.

Thanks for your Likes, Comments and Shares

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With any security system involving a human component, there’s a careful balance between requisite security measures and the user experience. The reason most of us have one or two locks on our front door – instead of twenty – isn’t because we don’t want more security, it’s that the experience would be far too much of a daily hassle.

When it comes to IoT security, the balance is askew in the other direction: the marketplace is glutted with lower end IoT devices that privilege a simplified user experiences over robust security. While this strategy allows consumers relative ease and a frictionless process in activating smart home and other internet-connected products, this devaluing of security leaves a virtual unlocked front door for malicious hackers who have little difficulty in accessing these devices. A largely unsecure IoT industry is proving time and time again to have significant and harmful repercussions, in the form of the mayhem that hackers can inflict on vulnerable users, and for the internet at-large as devices are corrupted for use in devastating IoT botnet-based DDoS attacks that continue to make headlines.

The need for security is, of course, a major issue that the IoT industry must overcome. Even as Gartner foresees the IoT rapidly expanding to 20.4 billion devices by 2020, a recent market survey finds that 90% of consumers do not have confidence in the security of IoT devices. In the same way, IoT security – and customer confidence in it – is just as important to the enterprise, as commercial IoT applications may provide personalized services that utilize sensitive data, involve monetary transactions, or offer other features requiring authentication that is unquestionably safe and frictionless for customers. Altogether, this makes IoT security a key concern that absolutely must be resolved for the IoT industry to have longer term staying power and to reach its full potential.

Passwords are (rightfully) going extinct

Passwords continue to be the default option for account security across all industries. While common, they’re also an overly complex user authentication method that are becoming less effective in securing access, while also becoming more frustrating and challenging from a UX perspective.

Forgetting your password requires ones to waste time with reset emails and security questions – if we can remember them -  a cumbersome process equivalent to fumbling with twenty door looks.  And beyond delivering a subpar UX, most IoT devices are manufactured without a traditional security interface (no screen, no keypad), leaving passwords a poor candidate for IoT security and leading enterprises across industries seek alternative and more secure ways for authenticating users.

Biometrics are the answer to the IoT’s present – and long term – security needs

Biometric security measures are growing in popularity and in widespread use.  Smart phone users are deploying fingerprint identification or facial recognition to unlock screens. Alexa, Siri, and other voice-activated tools have made talking to your technology commonplace, increasing demand for voice-based authentication as a common security solution.

The biometric approach to security is particularly well-suited to the IoT, though, and offers a compelling synergy with the desires of today’s businesses to establish more personalized interactions and relationships with customers. As demonstrated by the rise of chatbots, brands are evolving to include personalities that go beyond mascots and logos. Businesses want the customer’s brand experience to feel familiar – acquaintances and friends don’t require identification when they see you.  Biometric authentication enables a more natural and passive experience, whether that’s opening the smart home lock on your front door, activating IoT devices inside, or interacting with brands and their products by other means.

In addition to the stylistic advantages, several technical advances have enhanced the current viability of biometric security for the IoT. The memory footprint of biometric security algorithms are getting smaller while also getting more efficient.  Algorithms as small as 2MB now have the capability to fully secure IoT devices. And these algorithms are also becoming smarter and can thwart spy movie-esque attempts at trickery; for example, biometrics can now distinguish between your voice and a recording of it. Backed by AI and machine learning that studies individual user behavior, biometrics can also now authenticate users by their gait, how they type, how they apply pressure to a touchscreen, and plenty more of the things that make you, you.

Secure authentication is the only way to commercialize IoT in the enterprise. When this happens, there will be proper verification of monetary transactions and sensitive personal data can be shared. The challenge for the industry is to provide a secure, frictionless (passive) authentication that fully takes advantage of the IoT without compromising the UX.

With the death of passwords accelerating and the stakes of security for IoT industry health so high, the arrival and incorporation of highly capable biometric security measures within IoT devices is certainly a welcome one.

 

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Top IoT Startups to Invest in 2018-19

With the IoT surfacing as the next great destination for investment, many industry titans are scrambling and fiercely competing to seize their share of the revenues in the IoT market. Currently, IoT is at the core stage of industries like energy management, healthcare, logistics, manufacturing, and transportation. In convergence with various technologies like blockchain, AI, and edge, IoT has the potential to disrupt all the aforementioned verticals. Companies like IBM, Intel and Cisco are swiftly investing in the IoT technologies to take a lead in next era of technology. The adoption of IoT in all industries is becoming so vital that tech giant, Microsoft has announced to invest $5 billion in IoT over next four years globally.

 

A report from IDC states that the worldwide Internet of Things market will grow from $656 billion in 2014 to $1.7 trillion in 2020 with a compound annual growth rate (CAGR) of 16.9%. As per the report, connectivity, devices and IT services will be responsible for the majority of the IoT market in 2020. IDC estimates that all the three services will account for over two-thirds of the worldwide IoT market in 2020, with devices (modules/sensors) alone representing 31.8% of the total. With the increase in market size, the investments in IoT globally shall rise from over $800 billion in 2017 to nearly $1.4 trillion in 2021 indicating a worthy investment with quick ROI. Current investment in IoT holds a promising return as the adoption of IoT increases with market size and spendings. As investment is worth in IoT, it is now important for investors to know which startup having innovative technology would be ideal for them to invest in. The number of startups in the IoT rose rapidly from just 13 in 2013 to 189 in 2014. Following is the list of top startup companies using innovative technologies like blockchain, AI and edge which will aid an investor in selecting an ideal company.

 

Discovery IoT

Discovery IoT is a revolutionary solution that enables brands to track their products through their supply chains, accurately on a real-time basis. They are developing a tag, Cliot, which will hold the ability to track products with embedded sensors and is built at the cost of $0.10. With IoT in convergence, Discovery is using the latest technologies like blockchain, AI and edge computing (mesh network) to solve current problems including stock-outs/empty shelves, product obsolescence/expiry etc., faced by the supply chain industry. Participation in Discovery’s sale will be the next best destination for investors as their solution will soon be adopted by a massive audience. The pre-sale of DIS tokens will be made available for a limited period starting from June 15, 2018 and ICO will be made available for 6 weeks starting from August 1, 2018. They will abate bonuses as per rounds, to attract more investors, keeping in mind that the early investor gains handsome return. Discovery has a strong team lead by Selvam VMS, Co-Founder & CEO, a veteran in the field of supply chain management with more than 10 years in the domain. He is accompanied by Kumar T, Co-Founder & CTO, a techie with more than 15 years of knowledge and experience in the areas of IoT and AI. Also, they are supported by various professionals, experts and senior advisors like Aly Madhavji and Nandakumar Balanujan with 36+ years of experience in supply chain; and incubated by the Blockchain Founders Fund.

 

IOTium

 

IoTium is a startup based in California with an aim to advance secure network infrastructure for the industrial Internet of Things. Their Network as a Service (NaaS) solution is designed for the building automation, industrial automation, oil and gas, manufacturing, transportation and smart city industries, empowering them to securely connect legacy onsite systems to cloud-based applications to leverage new analytics, machine learning, and predictive analytics applications. Till date, IoTium has secured $8.4 million in Series A funding and is backed by investors including GE Ventures, March Capital, and Juniper Networks, as well as Pankaj Patel, former Executive Vice President and Chief Development Officer at Cisco. The funds have been used to expand its trail in the oil and gas, transportation and smart city industries with the launch of the IoTium NaaS. This investment has helped them in a recent distribution partnership with The Panel Shoppe and a building automation firm, Relevant Solutions.

 

 

Evrythng

Evrythng is a startup based in London, New York, and San Francisco which creates IoT and smart solutions to make products more intelligent and interactive. They collect, manage and apply real-time data from smart products and smart packaging to drive IoT applications. The company aims to ensure that connected devices can be managed and enhanced through real-time data and analytics throughout the full product lifecycle. This includes assigning digital identities to devices which allows them to be tracked and thus driving IoT a step further. This provides businesses with insights into their supply chains and consumers with awareness of the counterfeit product. The startup has secured $42.3 million in four funding rounds with lead investors being-  Sway Ventures, Atomico, and BHLP. The company's clients include Coca-Cola, Avery Dennison RBIS, Crown Holdings and West Rock.

 

Notion

Notion, an IoT startup based in Denver, Colorado, provides home security and monitoring through adaptable sensors. The low-cost sensors can be used to monitor unauthorized entry and take temperature readings of a user's home. Notion’s small sensor can be placed near doors and other locations around the house to monitor motion, temperature, water leaks etc. The startup has secured $16 million in funding by following a solid crowdfunding campaign on Kickstarter through six investment rounds and has used the funds to expand the development of home sensor products and to tap into the insurance market. It has attracted audience and investors including Draper Nexus, Translink Capital, Mesh Ventures and XL Innovate.

 

Conclusion

 

Starting from home devices to industrial machines and automobiles, it is widely speculated that the next tech revolution is likely to be spurred by the ability to connect things. Therefore, the tremendous interest generated in IoT evident from the growing number of startups and mounting investments is a testament to IoTs potential to create enormous business opportunities around the globe. As the technology is yet to mature and the market for it is far from being saturated, the time is ripe for investing in IoT based solution providers.

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Although it took some time to manifest, nation-states have realized the potential for cyber espionage and sabotage on IoT devices.

The latest news

On April 16, 2018, the US authorities issued a warning that government-backed Russian hackers are using compromised routers and other network infrastructure to conduct espionage and potentially lay the groundwork for future offensive cyber operations.

In a joint statement, the US Department of Homeland Security (DHS) and Federal Bureau of Investigation (FBI), along with the UK's National Cyber Security Centre (NCSC) - the cyber arm of Government Communications Headquarters (GCHQ) - said that Kremlin-backed hackers are using exploits to carry out malicious attacks. The hackers are using compromised routers to conduct man-in-the-middle attacks to support cyber espionage, steal intellectual property, and maintain persistent access in victim networks for use in additional campaigns.

U.S. CERT noted that cyber actors are exploiting large numbers of enterprise-class and residential routers and switches worldwide to enable espionage and intellectual property theft.

 

A growing concern

This is just the most recent of several incidents wherein nation-states have used connected devices for their goals.

A spying campaign called “Slingshot” targeted at least 100 victims in the Middle East and Africa from at least 2012 until February 2018, hacking MikroTik routers and placing a malicious dynamic link library inside to infect target computers with spyware components.

In another incident, nation-state actors left political messages on 168,000 unpatched IoT devices. The attackers used a bot to search the Shodan search engine for vulnerable Cisco switches and were easily able to exploit a vulnerability in Cisco Smart Install Client software to infect and “deface” thousands of connected devices with propaganda massages.

 

The west is also toying with IoT devices

Russia and China are not alone in investigating the potential of exploiting IoT devices. In 2016, US intelligence chief James Clapper acknowledged that the US would consider using the Internet of Things to spy on adversaries. More recently, the Dutch Joint Cyber SIGINT Unit hacked a CCTV camera to spy on a Russian cyber group called ‘Cozy Bear.’ As a result, they were able to identify many of the members as employees of the Russian Foreign Intelligence Service.

As western countries become more aware of espionage efforts by foreign governments, it is not surprising that they are fighting back by trying to reduce the attack surface. Several Chinese CCTV manufacturers were recently flagged for having built-in backdoors that could allow intelligence services to syphon information. Dahua, a maker of CCTV cameras, DVRs and other devices was forced to issue an emergency patch to its connected devices. Camera models from Shenzhen Neo Electronics were also exposed to have a severe security flaw. Finally, the largest maker of surveillance equipment in the world, HIKvision, was accused of having a backdoor and banned by certain US bodies.

 

What’s next?

While the potential for information collection through IoT devices is enormous, we shouldn’t forget that these are physical devices deployed in the real world, so hacking them can have real consequences.

 

Doomsday scenarios

Here are just four of many potential “doomsday scenarios” that could result from IoT device hacking:

Grid manipulation attacks

Power grid security has received the appropriate attention in recent years, due in part to large scale cyber-attacks on power grids around the world. But what if, instead of hacking secured power plants, a nation-state was to hack millions of smart devices connected to the power supply, so that it could turn them on and off at will? That would create spikes in local and national power consumption, which could damage power transformers and carrying infrastructure, or at the very least, have substantial economic impact.

Power companies try to balance consumption loads by forecasting peak consumption times. For example, in the UK, demand spikes are as predictable as half-time breaks in football matches or the conclusion of an Eastenders episode, both of which require an additional three gigawatts of power for the roughly 3-5 minutes it takes each kettle to boil. The surge is so large that backup power stations must go on standby across the country, and there is even additional power made available in France just in case the UK grid can’t cope. 

But since no one could anticipate an IoT “on-off” attack, nobody could prepare standby power, and outages would be unavoidable. In addition, power production, transportation and storage costs would be enormous.

Smart utilities

By attacking Internet-facing utility devices such as sewage and water flow sensors and actuators, attackers could create significant damage without having to penetrate robust IT or OT networks.

 

Smart city mayhem

Having a connected urban infrastructure is a terrific thing. The problem is that once you rely on it, there is no turning back. If the connected traffic lights, traffic monitoring cameras and parking sensors are taken offline or manipulated, cities could suffer with large scale interferences to their inhabitants’ daily lives. For example, shutting down connected street lighting could impact millions.

Simple terror

Since we are all aware of the potential impact of a devastating cyber-attack, it would not take much to invoke large-scale hysteria. Just imagine someone hacking a street sign and altering it to display messages from the country’s enemies.

 

Summary

Nation-states have long targeted IT infrastructure to gather intelligence and intellectual property, but their focus has shifted to OT/industrial networks with the aim of facilitating disturbances and physical sabotage. IoT seems to be the new domain in which proficient bad actors can collect information, create disturbances, cause large-scale damage, and inflict terror and panic. The IoT is both insecure and increasingly ubiquitous, and these characteristics make it attractive for hackers and guarantee continued exploitation.

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We often don’t compare technology to fable stories, but when it comes to the internet of things (IoT), the story of Pandora’s Box comes to mind. It’s a technology that has great potential, but where the weakness and possibilities lie are in it’s lack of basic security measures. We might even go as far as to say, what security? These are the concerns we’re thinking about at IT Security Central.

As a completely remote company, we’re taking measures to understand how the internet of things can impact our company data security. Hackers look to exploit technology vulnerabilities to access valuable information. Hacking an IoT connected fish tank, smart fridge - these aren’t far-fetched stories. These are stories that are happening now. 

The lack of secured IoT devices starts in the development phase. These devices are developed on a basic linux operating system with default codes that buyers rarely change. When these devices are developed, security isn’t on the agenda; rather, developers are looking at human behaviors and outside threats. When they should be looking inwards.

An unsecured IoT device is the weak link in the connection. As one of the fundamental purposes of the technology is to provide connection and accessibility, this one weak link can bring down the entire network. And if your remote worker’s BYOD devices are in someway connected to that network, your company just became vulnerable.

Remote workers or ‘the gig economy’ is expected to increase in frequency. According to the Global Mobile Workforce Forecast Update, employees working remotely is suppose to increase to 42.5% of the working population by 2022. At that time, the world is projected to see half of its population working outside the office either full-time, or part-time. 

Security vulnerabilities, remote workers and IoT - where is the connection? The scary thing, remote workers are likely to already have IoT devices in their work environment, and most likely, they are not protected. These devices can mostly be smart home devices that workers have acquired to make their daily lives easier. Common devices include Amazon Echo, Neo and GeniCan.

The first step in active prevention is to make your employees aware of the importance of data security and then aid them with the tools for success.

Best Practices for Protecting Your Network (from Remote Workers)

With the wealth of internet-based security technologies, the idea of protecting your network with in-house servers and the traditional firewall is (well) old school. With cloud-based companies, you can now access and protect data in easy step-by-step processes, and the best news, most of these companies do the data management for you.

One of the most progressive approaches to remote worker security would be to adopt a monitoring service to collect data and actively look for anomalies in the network. Through data collection and analysis, a monitoring software creates a user profile of normal, everyday behavior. The administrator can set ‘alerts’ for when certain data repositories and files are accessed, or when sensitive data is moved. The longer a data breach goes undetected, the larger financial implication for the company. Requiring remote workers to download and use a remote monitoring software is one of the highest levels of protect against data loss.

But if monitoring isn’t on your agenda, these are a few basic tactics that employers can encourage remote workers to undertake.

Permissions Management

Though the workers are remote, administration can set limits to data access. This process starts by undergoing a through analysis and understanding of each position. It’s important to understand who needs access to what information, and who doesn’t need access to information. Once this is understood, administrators can restrict information, and they can also set ‘alerts’ when information is accessed without prior approval.

Home Network Policy

Once employees leave the brick & mortar walls, the manager has little access where and on what internet network they’re accessing information. But don’t fret, this freedom and flexibility is part of what make remote work appealing. Where privacy might be a factor, we don’t suggest to go as far as asking remote workers to eliminate IoT devices on their network. Rather, we encourage to create a policy that specifically states the security requirements that the IoT must have in order for the work network to be accessed. By educating your employees, you can save them and data loss heartbreak.

Encryption

Encryption, encryption, encryption. You’ve heard the importance of encryption. For remote workers, the company can never be too safe, so they should go the extra mile and set remote workers up on an encrypted network. A VPN ensures all connections and communications are encrypted when the network is accessed. Don’t worry about IoT connectivity in their home, or when remote employees connect to an unsecured public wi-fi connection. A VPN provides the next level of security through encryption, and a hacker won’t be able to access communication or data without alerting administrators to a potential breach. 

IoT devices are already integrating into our at-home lives, and when remote workers access their at-home networks, suddenly the topics collide. As more workers go remote, it’s important to look inwards towards security to see how everyday IoT devices impact company data. Take the time to ensure that remote workers are protecting the network effectively.

Guest post by Isaac Kohen. Isaac Kohen is the founder and CEO of Teramind (https://www.teramind.co/), an employee monitoring and insider threat prevention platform that detects, records, and prevents, malicious user behavior in addition to helping teams to drive productivity and efficiency. Isaac can be reached at [email protected]. Connect with Isaac on social media: LinkedIn, IT Security Central and Twitter @TeramindCo.

 

 

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Guest post by Romain Wurtz, Chief Technology Officer, NarrativeWave

As companies engage in the implementation of analytics and data science applications, many challenges lie ahead. According to the Harvard Business Review, many data science applications fail due to poor goal definition, a lack of understanding of the key data, or a lack of focus on business value.

We believe the best route to data analytics and particularly analytics for the Industrial Internet of Things, must have several key elements:

Key Elements of Effective Analytics:

Builds upon your Subject Matter Experts’ existing knowledge. Allows engineers to use the platform and be part of the analytics process.

Enables automation of key processes.  Builds a solid foundation for more complex analytics (e.g. predictive).

This article takes a look at each of these elements in further detail and explores why they are important to driving value for your organization.

Having a platform built on your subject matter experts’ knowledge is the best starting point.

Your Subject Matter Experts (SMEs) and engineers have been building and maintaining your equipment for decades. Their expertise and knowledge is the best available expertise on how your equipment should be operated, maintained, and evaluated. Incorporating their knowledge to best evaluate data from the equipment and what that data means, is the ideal starting point for the application of analytics.

Analytics platforms using purely Machine Learning or Artificial Intelligence may lack insight on what the data means and the meaning of events within the data. Without human interaction or interpretation, more advanced analytics, such as predictions, have a difficult time achieving the desired outcome. Without a determined outcome, the process can take months to evaluate, and even then, the analytic effectiveness and accuracy can remain unknown and unproven.

We believe the best starting point for analytics is one that starts by using your own proven analytic methods as a foundation and then allows for a natural, building blocks approach.

Using a platform that allows engineers to be part of the process helps with the adoption of analytics.

Adopting new analytics and data driven business models is fundamentally about changing the way business has been done for many years. In an effort to make this transition, gaining adoption and trust of key players within your organization will significantly impact the success of a new program. Having a platform where SMEs can interact and engage, without having to be a data scientist or a developer, results in higher adoption and more impactful business outcomes for the organization.

Implementing a platform that automates current processes creates short-term and significant value.

In order to gain value from large data sets and sensor data, only a platform that starts to automate part of the process can create scalable value. Meaning, the platform must be able to interpret data, generate insights, and provide recommended outcomes for end users. Otherwise, it becomes just another way to visualize and explore data. This can add value on its own, but doesn’t reach the impact that automation provides. As noted earlier, building a system on your proven analytic methods, and then adding a layer of more advanced analytics, such as machine learning based predictions, is the best route to a highly accurate, automated platform.

Building a platform with a solid foundation of your experts’ knowledge is the best way to approach implementing an entire suite of analytics.

Building a platform configured by your own SMEs creates the optimal foundation for an entire range of analytics. Your experts can provide knowledge about significant areas such as:

The meaning of key data. How sensors are related to each other.

What constitutes an actionable event?  What constitutes a false alarm?

Exceptions to the rule.

Once this knowledge is part of an automated platform, adding a full range of analytics becomes more impactful. For example, knowledge of what constitutes a false alarm can lead to an insight describing what turned a false alarm into a valid alarm and what indicators are worth automatically tracking. By contrast, an approach that solely tries to use machine learning or AI techniques without these key understandings, can struggle with the “right” business outcome, accuracy, dealing with exceptions, and delivering significant value to the business.

Business Cases & Outcomes

These business case examples show how we at NarrativeWave impact customer’s operations, profitability, unplanned downtime, and workforce efficiency.

Improved Accuracy of Event & Alarm Analysis.

Challenge: The traditional workflow of diagnosing events or alarms on large industrial assets is a manual process for engineers. A manufacturer was looking for a solution that would increase accuracy and reduce the risk of costly human errors. 

Solution: NarrativeWave’s platform allowed the customer’s engineers to create detection models and equations through the SaaS platform. Currently, this manufacturer receives accurate and automated root-cause analysis of events in near real-time.

Impact: The software provided a 25% increase in accuracy of diagnosing events, which means a more consistent, predictable solution for this manufacturer’s engineers and clients.

Reduced Time Spent Diagnosing Alerts & Alarms

Challenge: Sensors on large industrial assets generate millions of data points per second. When an alert was triggered, engineers spent hours conducting redundant, manual research to diagnose the problem and produce an actionable report for clients. The diagnostic process can take up to 16 hours and technicians were struggling to keep up with the expanding service requirements. 

Solution: The NarrativeWave platform automated their manual processes, delivering an analysis, actionable insights, recommendations, and a report to their engineers in less than 3 minutes. This allowed their engineers to make near real-time decisions on what happened, why it happened, and what to do next.

Impact: The outcome resulted in a 95% time savings in diagnosing alerts and alarms, which reduced unplanned equipment downtime, improved workforce efficiency, and enhanced service contract profitability. This proved the opportunity for a multi-million dollar savings per year for this OEM, and better supported real-time service contracts.

Optimized Productivity of Skilled Engineering Labor

Challenge: More than 50% of all industry alarms are false positives, which still have to be diagnosed and solved. A customer was looking for a solution that would allow their engineers to optimize their workflow and spend less time servicing invalid alarms. 

Solution: The NarrativeWave platform automated the root cause analysis of events to produce actionable insights based on the manufacturer’s data. The outcome was an explanation of the event that occurred and guidance on what to do next, which was provided to the engineers within a few minutes.

Impact: The platform accurately and quickly invalidated false alarms, allowing engineers to focus more time on resolving valid alarms and serving their clients. For the first time, engineers were being leveraged in the best way to impact this manufacturer’s operations.

Increased Efficiency in Creating Detection Models

Challenge: A large enterprise client had a robust analysis setup with 3 detection models and 150 threshold variants. The client’s process for iterating detection models originally took 3–4 months and required engineers to rely on development from either a software engineer, data scientist, or an outside software vendor. 

Solution: NarrativeWave’s platform provided an intuitive pipeline, enabling their business users to quickly create, manage, and iterate their own detection models. The platform is user-directed, managed and utilized by the customer’s internal engineers, without the ongoing need of developers or data scientists.

Impact: The iteration timeframe has been dramatically reduced since using NarrativeWave. More importantly, this customer’s engineers can setup iterations on their own, allowing for immediate impact on the business operations and for their clients.

Enhanced Next Generation Knowledge Base

Challenge: Engineers have been detecting alarms individually for 30 or more years. While working with a major engine manufacturer, NarrativeWave found the detection process was not recorded, standardized, or made available to other engineers and management within the organization. 

Solution: The platform is setup to record the engineers’ knowledge and feedback, resulting in a platform that gets smarter over time. Engineers can customize the business analysis and recommendations to make them as accurate as possible, therefore creating an evolving knowledge base for SMEs. 

Impact: The outcome resulted in the manufacturer, for the first time, being able to capture their engineers’ knowledge. This increased collaboration between engineers, improved standardization, and allowed valuable knowledge to be visible across the organization.

Improved Fleet Health & Management

Challenge: Manufacturers and equipment operators currently lack visibility into assets across their entire fleet, making it difficult to identify poorly performing assets and best performing assets. 

Solution: With NarrativeWave, asset performance can be evaluated near real-time, enabling organizations to better manage critical assets and plan for future actions, all by the click of a mouse.

Impact: The platform-wide view provides significant time-savings of tracking and managing fleet health for equipment manufacturers and operators. Additionally, the platform reduces unplanned downtime and helps organizations prevent critical equipment failures.

Improved Predictive Analytics & Maintenance

Challenge: Manufacturers and equipment operators are interested in deploying predictive models for better asset maintenance and warranty support. Pure machine learning approaches lack a solid foundational basis and can be difficult to implement successfully.

Solution: With the NarrativeWave Knowledge Base, key information such as the meaning of events, the relationship of sensors, and what constitutes a valid alarm are already known. By applying machine learning techniques to a solid NarrativeWave foundation, predictive analytics is more effectively implemented. 

Impact: This approach provides a strategic method of utilizing predictive analytics and improves the outcome of implementing analytics. The result is a highly accurate, auditable platform rather than a pure “black box” approach.

 

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We are in the dawn of a new cyber society. A society where organizations shall design plans to utilize the unique skillsets of both AI Systems and humans. A society where Humans and AI systems shall work and live together and without fear. A society where humans shall use newfound time and freedom to advance strategic skills and individual talents.
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The most irritating problem that car owners in a heavy traffic ridden city face is to find a spot to park their car. Finding a parking spot in a parking lot, airport or near offices many times become a serious problem and cost people a lot of money and other resources like time and fuel.

This problem is growing every day and to bring it down we need to implement some smart parking solutions.  is a revolutionary solution for this matter and its prototype projects are already running in many cities.

Just as your Smartphone or smart refrigerator, these smart parking lots are linked to many internet-enabled devices; in other words, the whole parking lot is made IoT (Internet of Things) enabled. Internet of Things is the technology, using which we can connect our surrounding devices, especially those devices which could never have been internet friendly before IoT.

How Does Smart Parking IoT Work?

IoT enabled smart parking makes use of low-cost sensors and mobile apps that give out real-time information about the availability and location of parking space. Small sensors are installed on the road surface for each individual spot in the parking lot.

Now, these sensors at each parking spot give out an individual real-time status update of its availability to a nearby server. The data could also be transferred to the cloud. From there a base station is set up. Such base stations have the capacity to serve thousands of devices in the vicinity of around 4 sq. Miles for urban cities and 10 sq miles for suburban regions.

For the enterprises who own multiple parking lots across the city, such base stations are very cheap to set up as they cover a long range. So, the number of base stations to be set radically reduces. Also, the sensors mounted in the lots have very low setup and maintenance cost as they are designed to withstand mechanical pressure from the vehicle and also their working is very low-powered which makes their batteries last almost 10 years.

Read Also: 9 Internet of Things Trends for 2018

 

Benefits Of Smart Parking IoT:

  • Real-Time Info:

With real-time updates, the user doesn’t remain unknown about available parking spot and gets a precise list of empty spots. This real-time data gives information about user preferences to the parking lot owner to help decide what type of modifications are needed according to their user behavior.

  • Location Info:

The sensors won’t just inform about the availability of parking space but will also give its exact location in the lot. Due to this, much of the time is saved as users know exactly which floor and which aisle to go to.

  • Integrated Payment Options:

Most of the people that use parking lots are frequent users as their offices, colleges or other every day visiting establishments are nearby to the parking lot. For such users, paying their parking fee manually every day could be tiresome and time-consuming so an additional feature of integrated payment modules can be added to this system so that they can complete their daily payment transaction digitally without wasting any time.

  • Low Pollution:

All the time saved for manually searching for a parking spot and roaming around the lot with your car increases fuel consumption. It’s a shocking fact that a million barrel of oil is consumed just for the purpose of finding a parking spot. Smart parking system using IoT saves all this fuel and lowers pollution while doing this.

  • Lower Cost Of Operation:

When the smart parking uses IoT system, the process becomes automated which leads to a lower manual input to this process. Lower manual input prompts to lower labor cost which in turn decreases operational cost.

  • User Experience:

Due to such smart parking solutions, customers get a very user-friendly experience which makes them come back to your establishment. Compared to old parking lots where the user has to drive through aisles until they find a place to park their car, this definitely becomes the best alternative that any user would opt instead of the old way.

Conclusion:

The Smart Parking System using IoT is by far the most user-friendly solution with very low installation as well as operational cost. This becomes a win-win situation for both, the user as well as the parking lot owner. Opting for Smart parking solution will be the best business step for parking lot owners and a really great and beneficial experience for the users.

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As the Global PM and CTO for Lumada, it's been a rewarding journey to create a portable Industrial #IoT platform that could run at the Edge on a factory floor, in a train, inside a data center or in any hyper-scale public cloud.

This composable platform (use just what you need for your specific use case) combined with our revolutionary Asset Avatars (Digital Twins) that bring Lumada to life, is the very definition of "Visionary." I also want to send a big congratulations to our Visionary friends at PTC (ThingWorx) and SAP (Leonardo).

Our #Lumada #IIoT platform coupled w/ our IoT Hardware Appliance takes you from #Edge to #Cloud.

Thanks to all the Hitachi collaborators, colleagues and friends I was lucky enough to take this journey with.

Get a free copy of the Gartner report here:
https://www.hitachivantara.com/ext/gartner-magic-quadrant-for-industrial-iot.html

-Rob

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Today, retail stores are constantly focusing on leveraging the emerging technologies like cloud, mobile, RFID, beacons, etc., to provide connected retail services and better shopping experience to customers. For example, store owners are integrating sensors in the key zones of retail stores and connecting them to cloud through a gateway that enables real-time data analysis related to products, sales, and customers from these sensors.

Interestingly, IoT and connected technologies are taking the retail industry by storm. 96% retailers are ready to make changes required to implement the Internet of Things in their stores

IoT in retail can help retailers improve store operations, enhance customer experience and drive more conversions. Moreover, IoT can help retailers solve day-to-day problems such as tracking energy utilization, managing in-floor navigation, detecting crowded areas, reducing check out timings, managing product shelves, preventing theft, monitoring goods, etc. Let us how IoT helps in few of these scenarios.

 

 

In-Store Navigation with IoT-enabled Devices

Identifying in-store navigation is one of the common problems in retail stores. Here, IoT devices with integrated technologies like Bluetooth, Wi-Fi, magnetic positions and augmented reality, etc., can facilitate in-store navigation to help customers navigate through the store and find the desired product.

It gives customers a multichannel shopping experience through digitization of physical assets. In-store navigation also helps increase the path to purchase rate before a product stock outs.

Example:
Bluetooth low energy (BLE) beacons are small sensors placed strategically throughout the retail store. These sensors are equipped with Bluetooth smart technology and compatible with smartphones. This BLE beacon device sends out continuous radio signals to nearby smart devices in the range. Smart devices in that range catch the signal and trigger events such as availability of a new product or launch of a new offer. Further, that device sends a unique ID to cloud server. The server checks that ID and responds back, through which communication between signal and smart device is established using a unique ID. Almost all customers nowadays carry smart devices like mobile phones and tablets. If BLE is used, customers can be notified on their smartphone with personalized coupons and deals as soon as they enter the store.

The above solution improves customer’s in-store experience and also increases footfall ratio. It also facilitates quick product search and increases conversion rates while generating a powerful shopping environment that can help enhance product offerings and store layouts.

Energy Management with Smart Devices

Energy consumption is a major cost consuming factor for the retail businesses, be it in refrigeration, lighting, heating, air conditioning, etc. Using these energy sources efficiently can bring cost saving of up to 20 percent per year. IoT-enabled smart devices can help resolve problems of energy management and saving.

There are several IoT-based platforms that can log, monitor and beep alarms or alert the in-store personnel about temperature, energy usage, heating, gas leakage, electricity breakdowns, etc., with the help of integrated sensors. Using these smart energy management devices, store owners can directly interact with the controllers of refrigerators and retrieve prioritized information with the help of sensors.

Example:
Every year, a large retail chain attributes nearly $2B of loss to wasted or spoiled food, with issues relating to its legacy refrigeration system, accounting for approximately 15% of this total—or $300 mm. In case of emergency situations like powercut or excessive heating, alarms from the controllers of these refrigeration systems reach the operations team only after 5 or 6 hours, and there is no mechanism to provide warnings before these situations occur. Here smart refrigeration IoT device can provide cloud-based temperature monitoring solution to notify the controllers about emergencies using temperature sensors and mesh networking technology.

Theft Prevention with Geo-Fencing

The crime of shoplifting in the retail industry is increasing day-by-day, because retailers fail to provide sufficient attention to shoplifters. According to National Association for Shoplifting Prevention (NASP), more than $25 million worth of merchandise gets stolen from retail shops each day. Adding more to retailers’ loss is retail shrinkage, which includes shoplifting, employe theft, paperwork error, vendor fraud and many more.

To overcome the problem of shoplifting and retail shrinkage, retailers can use Geo-fencing technique.
Geo-fencing relies on the global positioning system or a radio frequency identification (RFID) tag that allows a store operator to create a virtual barrier or zone around specific locations in retail shops. When a customer tries to move product from the specific location, an alert is triggered and a message is sent to the store in-charge. Geo-fencing enabled in IoT devices or beacons can help retailers in a number of ways; from keeping goods safe, tracking customers and employee movements, managing company-owned resources to minimizing incidents of theft and loss.

Customer Engagement with Sensor-Enabled Shopping Carts

The sensor-enabled shopping cart is a technique adopted by most of the retail merchandisers. These shopping carts help retailers grow their business in every aspect by helping them visualize shopper’s flows by category/subcategory, understand the shopping pattern, analyze the dwell path, and enable faster checkout.

This smart cart design involves sensors with connectivity protocols around the cart, which have the ability to track the movement of the wheels and match up with the distance the cart has traveled. It helps retailers with an accurate data of shopping carts with the inside-store journey. The data from this cart can be sent to the server or to cloud for further analysis.

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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” 

http://www.linkedin.com/in/pgmad

This post original appeared here.

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Quantifying IoT Insecurity Costs

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.

 

 

 

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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.

  • The AWS IoT SDK supports C, JavaScript, Arduino, Python, iOS, and Android with open source libraries and developer guide, which helps developers with their IoT product development. AWS IoT Core consists of the Device Gateway that allows bidirectional communication between devices and the AWS. The device gateway ensures that the devices are communicating through cloud securely and efficiently in real time. This device gateway supports MQTT, Websockets, and HTTP 1.1. It can also support billions of devices at a time without the infrastructure management.
  • 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.

 

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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.

 

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