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Guest post by Jason English, Principal Analyst, Intellyx

Surely you’ve caught some of the excitement about drones for enterprise use. Packages and communications delivered to the world by these ultimately mobile IoT fliers. Heavy VC investment in commercial and supply chain drone applications could drive this sector to be worth as much as $13 billion by 2020.

We all remember Amazon teasing a drone-delivery future in this now-famous ad from 5 years ago. But there’s no way the online retailer will corner this game. Expect drone delivery research to advance quickly at leading transportation firms like FedEx, UPS and DHL. Uber Eats might even have drones fly over some sushi for engineers too busy for lunch.

But could drones possibly become passé for widespread business use before they can even get out of the hangar?

Drones are the ultimate IoT play for enterprise

Of all the interesting ‘things’ in the commercial IoT continuum, from geo-location tags in trucks and packages, to remote cameras, factory robots, smart sensors and controls, power meters, wearables and medical devices, nothing captures our imagination quite like a drone.

In a sense, drones can let our productive work ‘slip the surly bonds of earth,’ with the ability to move anything, and see anything, almost anywhere in the world. It gives businesses a flock of birds to command, rather than the two-dimensional constraints of surface dwelling gadgets and robots.

Take the telecommunications industry. The ability to dispatch a maintenance drone to inspect and verify the equipment on a relay tower can save a human technician a risky and time-consuming day trip up the pole for a visual inspection, improving service efficiency while reducing insurance premiums.

In many cases, the drones are even replacing telco network infrastructure themselves, maintaining a tethered position to provide communication services or wi-fi coverage services to the ground below, especially in emergency outage conditions. Facebook killed its ambitious Aquila project to expand global internet access last year, but that isn’t stopping other regional and private drone network programs.

For oil and gas, or just about any industry that involves surveying or inspection, the value of drones with advanced cameras is self-evident. Real estate firms now commonly provide dramatic flyover footage of for-sale properties, for epic establishing shots, without the epic budget.

Big agriculture is getting in on the game, exploring inspecting, seeding and possibly even spraying or weeding large crop fields with unmanned farmer drones.

And of course, for logistics and delivery services, the needle is moving. A UPS pilot program employed drones atop trucks to more efficiently handle actual doorstep delivery of packages, potentially saving the cost of untold hours of truck drivers stopping and getting out of their brown van for each package.

No drone zone - Sedona AZAre drones a nuisance, or a security menace?

I recall swimming on the serene shores of Lake Kachess here in Washington a few years ago with family and friends, miles from civilization and its accompanying noises, when an electric-razor whirring sound broke the spell of nature. A hobbyist from another campsite was buzzing us.

The kids thought it was pretty cool, but I didn’t appreciate it. What if it runs out of batteries, or flies out of range of the controller while overhead?

As drones started dropping to consumer-friendly price points, I started seeing ‘No Drones Allowed’ signs in National Park sites like Sedona, Arizona, Crater Lake, Oregon, and at Snoqualmie Falls near my house (the site famous for the ‘Twin Peaks’ show exteriors). Certainly a few disruptive drone hobbyists caused such a response.

In entertainment, drones are often associated with less-than-desirable government uses of military and surveillance activity. Hollywood films often place spy drones in the employ of authoritarian antagonists and put killer drones under the joystick of covert operations teams.

With the miniaturization of electronics and ever-improving transmitter capabilities in a lightweight package, many drones have also proven easily hackable, and detailed specifications and software mods are readily available on the Dark Web for the mischievous.

Drones are also quite effective as mobile hacking platforms — in essence they are flying laptops after all. Drones can remotely sniff for network packets without a hacker needing to step onto the target’s corporate campus.

Not the best PR for this category of IoT devices.

Flying through FAA guidelines

Fortunately, the FAA has been closely regulating and tracking the use of drones (or UAS – ‘Unmanned Aircraft Systems’ as they call them) from the start, and have implemented measures such as a 5-mile ‘no fly zone’ for drones around sites such as airports, and requiring any operator of a drone more than 0.55 pounds (most of them) get a specific license to fly.

Clearer guidelines certainly help, and lead to more responsible use of the technology. For their part, the FAA says they don’t want to inhibit innovation and commercial use of UAS, and based on news in drone industry journals like InterDrone, the agency is partnering with business operators to consider input on guidelines for situations such as night flight and flying over people.

Who’s Taking Down Drones?

I didn’t know this before I started writing this story, but it is actually illegal to shoot down drones in the United States — even if they venture onto private property — as much as I would expect some sort of ‘Castle Law’ to allow it in this gun-lobby-controlled nation. Drones are afforded the protections due any other commercial aircraft under Federal law.

So, short of the shotgun approach, who is taking down drones today?

  • Regulators. Most democratic nations seem to be fast-tracking commercial use approvals, in order to encourage additional innovation in the space and stay up to speed with the rest of the world. That said, expect new rules and licensing guidelines to develop.
  • Hackers. Certainly the strongest threat to commercial use of drones lies in the ability for determined saboteurs to intercept or interrupt control of these devices, which are optimized for performance and range, rather than encryption and security.
  • Organized Labor. Remember that UPS drone pilot program? Well-organized workers took issue with having much of their work automated by drones. Companies will need to consider the human side of their existing business when implementing drone programs.
  • Eagles. Yes, Dutch law enforcement officials developed a program to use the actual birds of prey, not the classic rock band, to snatch suspicious drones right out of the sky and ground them. How cool is that?

The Intellyx Take

Setting all the fun toys, military stigma, and regulation uncertainty aside, I expect commercial drones to become rather commonplace in the next five years, working alongside us — or, above us.

As drone technology improves, production costs will come down, while better sensors, IoT cybersecurity measures, and even onboard AI will come into play to make them a safer and situationally aware part of the automated fabric of many companies.

They’ll never be right for every kind of work though. Drones will need to expand and enhance the abilities of our human workforce to maintain strong support in the enterprise. In the end, businesses will still need to perform an objective cost-benefit analysis to determine where drones are best fit for purpose.

Then, let ‘em fly. Just don’t tell Rambo the Drone-Killing Ram.

©2019 Intellyx LLC. Sharing or reprint of this work, edited for length with attribution is authorized, under a Creative Commons Attribution-NoDerivatives 4.0 International License. At the time of this writing, none of the companies mentioned above are Intellyx customers. Image credits: No Drone Zone, Cococino National Forest; Drone, Witolt Wacshut; CC 2.0 license, Flickr.

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There is no overstatement in the saying that that Internet of Things (IoT) is reshaping business processes and workplaces in a never-before way. Connected devices are increasingly pushing the boundary of innovation for the enterprises and industries of all niches. Thanks to these connected devices and a huge upsurge of IOT mobile app development, consumers are being benefited most through frictionless user experience.

No wonder in the fact that the IoT software development is exploding with all possibilities and promises. Just like ever before, the market is brimming with a whole array of scalable, feature-rich, secure and user-optimized connected solutions that are transforming the way we interact with devices and use software solutions at the workplace.

In spite of such huge promise and possibilities, IOT software and app development faces some hefty and crucial challenges that developers of the present-day need to be aware of. Here we are going to explain some of these challenges in brief.

  • Operating System (OS) Considerations

The first technical challenge and pulling factor that IOT app development companies need to deal with is the consideration of the operating system of the devices. Since IOT devices have mostly shorter memory capacity and a single track operational capacity, developers need to approach the development challenges for such devices in a different way than with the desktop solutions. The developers need to pick an OS that perfectly fits the device capability and the objective of the application.

As of now, most of the IOT developers surveyed for their OS preferences have clearly chosen Linux. Linux according to most IOT developers, offers the perfect OS for IOT devices with a lot of memory constraints, microcontrollers, and IOT gateways.

  • Selecting the Gateways

The gateways in the IOT landscape plays the most critical role by connecting almost all the constituent elements ranging from connectivity protocols like Wi-Fi or Bluetooth, ports, IOT sensors, cloud systems, etc. Naturally, for the whole IOT ecosystem gateways really play the mission-critical role. 

When it comes to the choice of appropriate gateways for your IOT application, you have several well-known choices from renowned technology companies like Dell, Nexcom, Intel, etc. These gateway providers as if now are proved to be highly effective for end number of applications. Some of the key aspects that you need to consider in gateways include the particular specifications for the network, supporting development environment, power rating, memory capacity, etc.

  • Security & Privacy

One of the key aspects that IOT app developers should give utmost priority is the app security and privacy. The security here not just refers to the network security but practically security of every different component. As IOT devices penetrate the personal spaces of the users, they are often vulnerable to misuse and breaching of data security through cyber-attacks.

Maintaining optimum data security and safeguarding privacy are two aspects that always remained to be the contentious areas for the IOT app developers worldwide. Let us have a closer look at various security aspects of an IOT app.

  • Data Exchange Security: The data generated through an IOT app through the IoT sensors and devices pass through the gateway and is finally stored at the cloud server. To ensure optimum security to this data it is important to use encryption for safeguarding the data.
  • Physical Security: The IoT devices unlike other computing devices are normally used in private and remain unattended most of the times. This is why they remain vulnerable to a lot of security threats from hackers at the device level.
  • Cloud Storage Security: A cloud storage solution normally remains secure from threats and intrusions. Even then, the developers of the IOT apps need to make sure that the data in cloud storage remain safe and secure.
  • Privacy Updates: To protect the privacy of the user data processed and fetched by IoT devices, there need to be certain compliance rules. For instance, all fitness tracker devices collect user data on the basis of HIPAA guidelines. Such regulations and compliance standards basically safeguard the privacy of the user data.
  • Network Connectivity

The quintessential aspect of IOT app development is the fast and real-time data transmission between the device and the IOT gateway and the gateway to the cloud server. Poor connectivity will only render most of the critical app features to be ineffective. The connectivity issues and server breakdown still remain to be the major problems for too many IOT devices.

Actually, connectivity remains to be the first and foremost area of importance for connected devices that work hand in hand with gateways and cloud platforms. For meeting this challenge corresponding to connectivity with appropriate measures, the app design and device app environment play an important role. The connectivity solution should be considered as per the device constraints and capacities.

  • User-Optimized App Design

Another major focus area for IOT app development should be on the app design. The app design should be thoroughly intuitive and user-focused so that the users do not need to study manuals for using an IOT device. Even for industrial IOT devices, simple and clean design is extremely important to ensure faster decision making and visualization of the data. In this respect, close and mutually reciprocating cooperation between the developers and designers is a must for building IOT apps. Some of the key attributes that design inputs should ensure include the following.

  • Safe and secure user authentication
  • Frictionless transition across devices and applications
  • Personalized user experience based on user behavior and preferences
  • A consolidated IOT environment comprising all the elements in the pipeline.

 

  • Cross-Platform Deployment

Last but not least of all the major challenges that IOT app developers must deal with is deploying the app across multiple OS platforms. Since the IOT ecosystem comprises of a variety of device architectures, protocols, and operating systems, the app should be built to fit with all these variables for a seamless and efficient performance. This is why experts of international organizations such as the Engineering Task Force (IETF) and the Institute for Electrical and Electronic Engineers (IEEE) have already come up with explicit cross-platform development standards and architecture models to help smooth deployment across multiple OS platforms.

Conclusion

In spite of the overwhelming growth of the IOT applications and the ecosystem of connected devices, there is a multitude of challenges that the IOT app developers need to encounter regularly. By focusing on these challenges beforehand, they can at least take appropriate precautionary steps to ensure optimum quality and efficient output.

 

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A new wave of technologies, such as the Internet of Things (IoT), blockchain and artificial intelligence (AI), is transforming cities into smart cities. Many of these cities are building innovation labs and zones as part of their new civic landscape. Smart city innovation labs are vital components of the smart city ecosystem (Figure One). They provide an organized structure for cities, communities, experts, and vendors to come together to create solutions. Successful solutions piloted in smart city innovation labs are then scaled and deployed into a city’s operations and infrastructure.

Figure One. Strategy of Things Smart City Ecosystem Framework.

 
Many municipalities are considering and planning smart city innovation labs today. Over the past year, we helped to create, launch and operationalize San Mateo County’s Smart Region innovation lab (SMC Labs). From this experience, we share ten best practices for civic innovation leaders and smart city planners.
 
 
Ten Smart City Innovation Lab Best Practices
 

Develop a well defined innovation sandbox. Every smart city innovation lab has an unique mission. That mission is specific to its community, capabilities, priorities, and surrounding ecosystem. However, it is easy to get distracted and work on the “next shiny object”, vanity projects and “me too” innovation pilots. These projects don’t add value, but take resources and focus away from the problems the lab was created to address.

Build innovation discipline and focus by defining a “sandbox” from the start and updating it annually. The innovation sandbox defines clearly what types of projects are in-scope and which ones are not. The criteria includes alignment with city or department priorities, problem set type, problem owner(s) or sponsors, budget availability, cost, resource requirements, and organizational jurisdiction.

 

Create procurement policies and processes for innovation projects. Innovation pilots fall outside the “sandbox” municipal procurement processes and policies operate in. These pilots may work with start-ups with limited operating history, use immature and evolving technology, or bought in non-traditional ways (“as a service”, loans, etc.). This mismatch leads to higher risks, extra work and long sourcing times. Due to this, many vendors choose not to work with cities.

Effective smart city innovation labs are agile and responsive. They employ new procurement policies and practices designed specifically for the unique needs of innovation projects. This includes simplified processes and compliance requirements, new risk management approaches, faster payment cycles and onboarding models.

 

Build a well defined plan for every innovation project. Many innovation pilots are “successful” during the pilot phase, but fail during the scaling phase. This is because the pilots were not fully thought out at the start. Some test a specific technology or solution, and not the approaches. Others test the wrong things (or not enough of the right things). Some are tested in conditions that are not truly reflective of the environment it will be deployed into. Still others don’t test extensively enough, or over a sufficient range of conditions.

Successful projects in smart city innovation labs involve extensive planning, cross-department collaboration, and a comprehensive review process throughout its lifecycle. They have well defined problem statements. They define a targeted and measurable outcome, a detailed set of test requirements and specific success criteria. While innovation projects contain uncertainty, minimize project execution uncertainties with “tried and true” project management plans and processes.

 

Continuously drive broad support for the lab. A successful civic innovation lab thrives on active support, collaboration and engagement from stakeholders across the civic ecosystem. However, many city departments and agencies operate in silos. Launching and having an innovation lab doesn’t mean that everyone knows about it, actively funnel projects to it, or support and engage with it.

Successful smart city innovation labs proactively drive awareness, interest and support from city leaders, agencies, and the community. This includes success stories, progress updates, technology briefings and demonstrations, project solicitations, and trainings. They engage with city and agency leaders regularly, host lab open houses and community tours. They conduct press and social media awareness campaigns. Regardless of the “who, how and what” of the outreach, the key is to do it regularly internally and externally.

 

Measure the things that matter - outcomes. There are many metrics that an innovation lab can be measured on. These range from the number of projects completed, organizations engaged, number of partnerships, investments and expenses, and so on. Ultimately, the only innovation lab metric that truly matters is to be able to answer the following question - “what real world difference has the lab made that justifies its continuing existence and funding?”.

All innovation lab projects focus on solving the problem at hand. It must quantify the impact of any solutions created. For example, many cities are monitoring air quality. A people counting sensor, mounted alongside an air quality sensor, quantifies the number of people impacted. Any corrective measures developed as a result of this project can now point to a quantifiable outcome.

 

Build an innovation partner ecosystem. A smart city innovation lab cannot address the city’s innovation needs by itself. A city is a complex ecosystem comprising multiple and diverse domains. Technologies are emerging and evolving rapidly. New digital skills, from software programming to data science, are required to build and operate the new smart city.

Successful smart city innovation labs complement their internal capabilities and resources by building an ecosystem of strategic and specialist partners and solutions providers, and subject matter experts. These partners are identified ahead of time, onboarded and then brought in on an as-needed basis to support projects and activities as needed. This model requires the lab to build strong partnership competence, processes, policies and the appropriate contract vehicles. In addition, the lab must continuously scan the innovation ecosystem, identify and recruit new partners ahead of the need.

 

Test approaches, not vendors or solutions. Real world city problems are complex. There is no magic “one size fits all” solution. For example, smart parking systems use sensor based and camera based approaches. In some cases, both approaches work equally well. In other cases, one or the other will work better. A common innovation mistake is to only test one approach or fall in love with a specific vendor solution and draw a generalized conclusion.

Effective innovation lab projects focus on testing various approaches (not vendors) in order to solve problems effectively. Given the rapid pace of technology evolution, take the time to identify, test and characterize the various solution approaches instead.

 

Employ a multi-connectivity smart city strategy. There are many options for smart city connectivity. These include, but not limited to cellular 3G/4G, Wi-Fi, LoRaWAN, SigFox, NB-IoT and Bluetooth, and so on. Use cases and solutions are now emerging to support these options. However, some smart city technologies in the marketplace work on one, while others work on more. There is no “one size fits all” connectivity method that works everywhere, every time, with everything.

To be effective, smart city innovation labs need to support several of these options. The reality is that there is not enough information to know which options work best for what applications, and when. What works in one city or region, may not work in another. Pilot projects test a possible solution, as well as the connectivity approach to that solution.

 

Make small innovation investments and spread them around. Open an innovation lab and a long line of solutions vendors will show up. Everyone has a potential solution that will solve a particular problem. Some of these solutions may even work. Unfortunately, there is not enough budget to look at every solution and solve every problem.

Focus on making smaller, but more investments around several areas. Overinvesting in one vendor or one approach, in a market where technologies are immature and still evolving, is not wise. Invest enough to confirm the pilot outcomes. A more detailed evaluation of the various solutions and vendors should be made when the pilot moves out of the innovation lab and into a formal procurement and RFP phase.

 

Simplify administrative and non-innovation workloads. While innovation pilot projects are challenging, interesting and even fun, administering and managing the projects are not. These unavoidable tasks range include managing inbound requests, proposals and ongoing projects. These tasks increasingly consume time and resources away from the core innovation activities.

Effective smart city innovation labs get ahead of this by organizing, simplifying and automating administrative activities right from the start. For example, SMC Labs reviews inbound proposals once a week and organizes follow up calls and meetings on a specific day once every two weeks. In addition, the lab uses a tracking and pilot management tool (Urban Leap) to track innovation projects. Administrative and management activities are unavoidable. However, advanced planning and tools help reduce the burden to keep the lab's focus on innovation.

 

Benson Chan is an innovation catalyst at Strategy of Things, helping cities become smarter and more responsive through its innovation laboratory, research and intelligence, 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.

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According to global management consulting firm Bain & Company, long-term prospects for the industrial Internet of Things remain ambitious. However, many executives are resetting timeline expectations for reaching scale due to early adoption struggles. Notably, certain “darlings of IoT” like predictive maintenance have not lived up to the hype. And while Bain’s survey of 600 industrial customers shows increasing traction with ‘workhorse’ scenarios like remote monitoring and asset tracking, it exposes areas where many teams and vendors are struggling to deliver the goods. In the end, an iterative strategy focused on specific business outcomes remains critical.

Notably, Bain’s survey finds increasing concerns around integration with existing enterprise systems and data portability. Executives worry their visions for digital transformation will be restricted by internal skill gaps and proprietary vendor services. Understandably, they fear losing control of any data not managed by their own enterprise IT departments. Despite this, confidence remains high that an estimated 20 billion devices will be successfully connected by 2020.

Many executives feel the value proposition for industrial IoT is still emerging. For them, the ability to capitalize on this value and achieve better business results remains elusive. To address these challenges, Bain calls for organizations to build a new operating model and position themselves for long-term success in a connected world.

Recommendations for accelerating IoT adoption in the enterprise

First, Bain recommends industrial organizations choose specific, high-value use cases to tackle upfront. Prove out your ability to address security and other valid IT concerns. Then, adopt an iterative approach for demonstrating ROI and ease of enterprise integration.

Second, use experienced partners to address your gaps. Don’t try building everything yourself. Differentiation comes from the combination of acquired data with your industry-specific domain knowledge. We’ve seen manufacturing digital transformation initiatives stall out when internal engineering teams try to build their own IoT infrastructure. Software for collecting data (and system integration services) can be bought. Build your value, not your tools.  

Third, don’t expect overnight success. You’re building up organizational capabilities and working with a new set of specialized partners. Commit to a realistic investment timeline and prepare for change. You’ll likely need to bring in new, more entrepreneurial talent to drive your connected business model. At a minimum, empower your existing teams to think differently. Remember, you’re not rolling out a new CRM application. You’re transforming your enterprise. Act accordingly.

Fourth, industrial IoT revenue starts at the top. Executives must ensure the entire organization is aligned for transition to the new operating model. This requires both vision and clear communication. Unsurprisingly, those responsible for existing products and revenue streams fear cannibalization. Furthermore, IoT initiatives take time to meet traditional P&L requirements. If executives don’t create an environment where the new operating model can take root, prevailing forces will prevent its maturation while competitors move ahead.      

Prepare to scale the business

Eventually companies reach the point on their digital transformation journey where they’ve proven out their connected product technology and business concepts. Now what? Bain concludes with a method for assessing readiness to scale up your industrial IoT efforts.

To begin, how well do you understand the full potential of industrial IoT to your enterprise? IoT can dramatically impact the quality of manufactured products, service offerings, maintenance  procedures, and other areas of your enterprise. But what will this cost, and what will revenue look like once the system is deployed to production and fully commercialized?

Never forget, your competitors aren’t standing still. You can be sure they’re working on their own industrial IoT initiatives. What is your plan to win in this new arena?

Additionally, scaling IoT requires incentives alignment and coordinated execution across the enterprise. Engineering, IT, service, sales, and business teams must work together for organizations to realize the benefits of digital transformation. Make sure everyone understands their part and is rowing in the same direction.

Bain summarizes their last recommendation with a sentiment that we refer to as “strategy over software.” By strategy, we mean not just a plan, but a comprehensive roadmap, organization structure, and business model across the enterprise to support the success of your industrial IoT initiative.

Digital transformation is a journey

As you start your journey, you’re going to need an industrial IoT platform. Whether it makes sense to build your own or buy one depends on a variety of factors. But digital transformation isn’t just about technology. As Bain notes repeatedly, it’s about so much more. Business models and sales strategies, along with clear user stories, team roles, and responsibilities are equally critical to successful IoT initiatives. Beyond a platform, an experienced digital transformation partner can accelerate planning, implementation, and successful commercialization of your connected systems.

 
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The IoT Brings Smart Cities to Life

Guest article by Richard van Hooijdonk

In around 30 years, planet Earth will be home to almost ten billion people, 68 per cent of which will live in urban areas. And those urban areas will face a torrent of problems, as authorities will have to rely on limited resources to provide public services to a growing number of citizens. Besides traffic congestion and the potential rise in crime rates, rapid urbanization could also lead to a number of environmental problems like air pollution and overwhelmed waste collection systems. To tackle these challenges and make cities more liveable and manageable, governments are increasingly turning to the smart city concept.

At the heart of this approach is the use of technology to improve public services such as transportation, water systems, waste disposal, and many others. And among all the technologies smart cities deploy, the Internet of Things stands out as the most important, as it’s a network of sensors and connected devices that collect data critical for understanding how urban areas function. As Stephen Brobst, the chief technology officer of Teradata, a big data analytics company, says, the IoT enables us to “get a view of the whole city across these different domains of the life of the city as it’s captured in the sensor data.”

The many ways in which the IoT helps smart cities

Investments in smart cities are ramping up across the world and are expected to grow from $80 billion this year to $135 billion by 2021. Part of that money is allocated for IoT projects that help governments and residents to increase energy efficiency, improve traffic flow, reduce pollution, cut costs, and enjoy a number of other benefits. In other words, the IoT helps smart cities to achieve many of their key goals. Take, for example, the problem of traffic congestion in cities, which is in large part caused by drivers looking for parking space. IoT sensors embedded into the city’s streets, as in the case of Barcelona, can detect empty parking spots and alert drivers through a smartphone app. This helps people park their cars faster, saving time and fuel while reducing harmful emissions.

Many smart cities also tend to promote bike-sharing services as a way to reduce pollution and congestion, but bike theft could be an obstacle for that plan. One way IoT tech can help solve this issue is through technology such as Bitlock, a keyless bike lock that’s unlocked by the user’s smartphone and tracks the GPS location of the bike. This will help police potentially track and recover stolen bikes, while also allowing private and public organizations to analyze bike traffic patterns and find ways to improve the service.

IoT technology is also efficient in tracking and analyzing water use in buildings. For instance, Banyan Water, a smart water management company, claims it’s helped customers to save more than seven billion liters of water since its inception in 2011. The way it does this is by placing sensors and ultrasonic meters that track water consumption across the building, using software to analyze the gathered data and find anomalies such as leaks and overspend.

Municipal waste management companies could benefit from the IoT, too, by placing sensors in waste collection sites, and instead of adhering to strict schedules, dispatching haulers only when collection is really needed. This could cut “overhead for waste makers by up to a whopping 60 percent.”

Things to keep in mind when implementing IoT projects
Clearly, IoT technology can improve lives in urban areas in many different ways, but simply implementing the latest tech won’t necessarily make a city ‘smart’. Marc Jadoul, the head of IoT market development at Nokia, explains that even before the first sensor is installed, the authorities must define their future objectives and budget. The next step is to create broadband internet and IoT infrastructure that can sustain increased traffic. Jadoul also suggests that the authorities need to “think big, but start small” and “identify appropriate milestones and metrics” to be able to monitor their progress. Lastly, technology isn’t the goal, but rather an instrument to make people’s lives better and more connected. To that end, the authorities should promote citizens’ engagement in ‘smart’ projects by asking for their feedback and informing them of the progress. After all, “it’s citizens’ acceptance and engagement that will eventually determine success or failure of any smart city initiative,” Jadoul concludes.

Two key challenges for the IoT and smart cities
And while authorities and citizens see smart cities as a way to live better lives, hackers see them as a potential target. The wealth of data and sensitive services that connected devices produce can be abused by bad actors to disrupt a city’s operations. For instance, imagine if cyber-attacks crippled a traffic light system or a water filtration plant and the hackers asked for ransom. This makes cyber-security one of the key priorities of any smart city endeavour. Another challenge for authorities is the need to buy expensive servers, sensors, high-speed internet networks, and a range of other equipment. Many cities struggle to find the money, although IoT projects could lead to cost savings “to the tune of $2.3 trillion in efficiencies created and revenue generated worldwide by 2024.”

Just rolling out the tech won’t be enough
As our planet becomes increasingly crowded and more people flood to cities, authorities will be under pressure to provide public services to an ever-growing number of citizens and offset the negative consequences of urbanization. Technology such as the IoT and the concept of smart cities might be a solution and a way to fight traffic congestion, pollution, inadequate water systems, and a number of other problems. But for this approach to succeed, citizen acceptance and engagement is crucial, as simply rolling out the tech won’t be enough.

Author: Richard van Hooijdonk
International keynote speaker, trend watcher and futurist Richard van Hooijdonk offers inspiring lectures on how technology impacts the way we live, work and do business. Over 420,000 people have already attended his renowned inspiration sessions, in the Netherlands as well as abroad. He works together with RTL television and presents the weekly radio program ‘Mindshift’ on BNR news radio. Van Hooijdonk is also a guest lecturer at Nyenrode and Erasmus Universities. https://www.richardvanhooijdonk.com

 

 

 

 

Sources:

Cover photo by https://www.shutterstock.com/g/yingyaipumi

Azevedo, Mary Ann, https://newsroom.cisco.com/feature-content?type=webcontent&articleId=1868607.

Giarratana, Chris, https://www.trafficsafetystore.com/blog/how-iot-technology-is-creating-the-future-smart-cities/.

Glaeser, Edward and Helen Dempster, https://www.theigc.org/reader/contagion-crime-and-congestion-overcoming-the-downsides-of-density/cities-and-urbanisation-encourage-economic-growth-in-the-developing-world/.

Horwitz, Lauren, https://www.cisco.com/c/en/us/solutions/internet-of-things/smart-city-infrastructure-guide.html.

Ismail, Nick, https://www.information-age.com/smart-city-technology-123473905/.

Jadoul, Marc, https://www.nokia.com/blog/10-recommendations-creating-smart-city/.

Maddox, Teena, https://www.techrepublic.com/article/smart-cities-expected-to-invest-80b-in-technologies-in-2018/.

https://www.nationalgeographic.com/environment/habitats/urban-threats/.

http://www.sensanetworks.com/blog/waste-management-gets-sexy-smart-sensor-tech/

https://www.un.org/development/desa/en/news/population/world-population-prospects-2017.html.

https://www.un.org/development/desa/en/news/population/2018-revision-of-world-urbanization-prospects.html.

 

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IoT - Adopt or Face Extinction!

IoT platforms, services and solutions have exploded in the recent years. This article is a thought leadership paper written 2 years ago when IoT was beginning to become prevalent. The article made projects that have come to fruition based on IoT's proliferation in the market. And, the thought leadership is still as applicable as it was before.
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As we covered in the past, Gartner is out with their predictions for IoT. This time for the year's 2018-2023. The announcement was made at the Gartner Symposium/ITxpo 2018 in Barcelona, Spain. 

Nick Jones, research vice president at Gartner said, “The IoT will continue to deliver new opportunities for digital business innovation for the next decade, many of which will be enabled by new or improved technologies. CIOs who master innovative IoT trends have the opportunity to lead digital innovation in their business.”

And CIOs if you're not paying attention, get on it. Gartner says you need skills and partners to support IoT. Come 2023 the average CIO will be responsible for more than three times as many endpoints as this year.

Gartner shortlisted the 10 most strategic IoT technologies and trends that will enable new revenue streams and business models, as well as new experiences and relationships:

Trend No. 1: Artificial Intelligence (AI)

Gartner forecasts that 14.2 billion connected things will be in use in 2019, and that the total will reach 25 billion by 2021, producing immense volume of data. “Data is the fuel that powers the IoT and the organization’s ability to derive meaning from it will define their long term success,” said Mr. Jones. “AI will be applied to a wide range of IoT information, including video, still images, speech, network traffic activity and sensor data.”

The technology landscape for AI is complex and will remain so through 2023, with many IT vendors investing heavily in AI, variants of AI coexisting, and new AI-based tolls and services emerging. Despite this complexity, it will be possible to achieve good results with AI in a wide range of IoT situations. As a result, CIOs must build an organization with the tools and skills to exploit AI in their IoT strategy.

Trend No. 2: Social, Legal and Ethical IoT

As the IoT matures and becomes more widely deployed, a wide range of social, legal and ethical issues will grow in importance. These include ownership of data and the deductions made from it; algorithmic bias; privacy; and compliance with regulations such as the General Data Protection Regulation.

“Successful deployment of an IoT solution demands that it’s not just technically effective but also socially acceptable,” said Mr. Jones. “CIOs must, therefore, educate themselves and their staff in this area, and consider forming groups, such as ethics councils, to review corporate strategy. CIOs should also consider having key algorithms and AI systems reviewed by external consultancies to identify potential bias.”

Trend No. 3: Infonomics and Data Broking

Last year’s Gartner survey of IoT projects showed 35 percent of respondents were selling or planning to sell data collected by their products and services. The theory of infonomics takes this monetization of data further by seeing it as a strategic business asset to be recorded in the company accounts. By 2023, the buying and selling of IoT data will become an essential part of many IoT systems. CIOs must educate their organizations on the risks and opportunities related to data broking in order to set the IT policies required in this area and to advise other parts of the organization.

Trend No. 4: The Shift from Intelligent Edge to Intelligent Mesh

The shift from centralized and cloud to edge architectures is well under way in the IoT space. However, this is not the end point because the neat set of layers associated with edge architecture will evolve to a more unstructured architecture comprising of a wide range of “things” and services connected in a dynamic mesh. These mesh architectures will enable more flexible, intelligent and responsive IoT systems — although often at the cost of additional complexities. CIOs must prepare for mesh architectures’ impact on IT infrastructure, skills and sourcing.

Trend No. 5: IoT Governance

As the IoT continues to expand, the need for a governance framework that ensures appropriate behavior in the creation, storage, use and deletion of information related to IoT projects will become increasingly important. Governance ranges from simple technical tasks such as device audits and firmware updates to more complex issues such as the control of devices and the usage of the information they generate. CIOs must take on the role of educating their organizations on governance issues and in some cases invest in staff and technologies to tackle governance.

Trend No. 6: Sensor Innovation

The sensor market will evolve continuously through 2023. New sensors will enable a wider range of situations and events to be detected, current sensors will fall in price to become more affordable or will be packaged in new ways to support new applications, and new algorithms will emerge to deduce more information from current sensor technologies. CIOs should ensure their teams are monitoring sensor innovations to identify those that might assist new opportunities and business innovation.

Trend No. 7: Trusted Hardware and Operating System

Gartner surveys invariably show that security is the most significant area of technical concern for organizations deploying IoT systems. This is because organizations often don’t have control over the source and nature of the software and hardware being utilised in IoT initiatives. “However, by 2023, we expect to see the deployment of hardware and software combinations that together create more trustworthy and secure IoT systems,” said Mr. Jones. “We advise CIOs to collaborate with chief information security officers to ensure the right staff are involved in reviewing any decisions that involve purchasing IoT devices and embedded operating systems.”

Trend 8: Novel IoT User Experiences

The IoT user experience (UX) covers a wide range of technologies and design techniques. It will be driven by four factors: new sensors, new algorithms, new experience architectures and context, and socially aware experiences. With an increasing number of interactions occurring with things that don’t have screens and keyboards, organizations’ UX designers will be required to use new technologies and adopt new perspectives if they want to create a superior UX that reduces friction, locks in users, and encourages usage and retention.

Trend No. 9: Silicon Chip Innovation

“Currently, most IoT endpoint devices use conventional processor chips, with low-power ARM architectures being particularly popular. However, traditional instruction sets and memory architectures aren’t well-suited to all the tasks that endpoints need to perform,” said Mr. Jones. “For example, the performance of deep neural networks (DNNs) is often limited by memory bandwidth, rather than processing power.”

By 2023, it’s expected that new special-purpose chips will reduce the power consumption required to run a DNN, enabling new edge architectures and embedded DNN functions in low-power IoT endpoints. This will support new capabilities such as data analytics integrated with sensors, and speech recognition included in low cost battery-powered devices. CIOs are advised to take note of this trend as silicon chips enabling functions such as embedded AI will in turn enable organizations to create highly innovative products and services.

Trend No. 10: New Wireless Networking Technologies for IoT

IoT networking involves balancing a set of competing requirements, such as endpoint cost, power consumption, bandwidth, latency, connection density, operating cost, quality of service, and range. No single networking technology optimizes all of these and new IoT networking technologies will provide CIOs with additional choice and flexibility. In particular they should explore 5G, the forthcoming generation of low earth orbit satellites, and backscatter networks.

Gartner clients can learn more in the report “Top Strategic IoT Trends and Technologies Through 2023.”

Photo credit: Jim Templeton Cross www.templeton-cross.com, Gartner Symposium/ITxpo Barcelona 2011

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Here's the latest IoT Central Digest. Encourage your friends and colleagues to be a part of our community by forwarding this newsletter to them. They can join IoT Central here. You can contribute your thoughts on IoT here.  

Featured Resources and Technical Contributions

Source for picture: contribution marked with a +

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Here's the latest IoT Central Digest. Encourage your friends and colleagues to be a part of our community by forwarding this newsletter to them. They can join IoT Central here. You can contribute your thoughts on IoT here.  

Featured Resources and Technical Contributions

 

Source for picture: contribution marked with a +

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We are well aware that IoT offers a range of possibilities across various industries including consumer electronics and cars, healthcare, utilities, transportation, manufacturing, and so on. Also, Industrial IoT offers means to obtain insights into the business operations.

IoT offers a greater promise in the healthcare sector than in other sectors. This is because the IoT principles are already being applied to enhance the quality of care, reduce the cost of care, and improve the overall access to care. 

The integration of IoT features into medical devices greatly enhances the effectiveness and quality of service, especially bringing greater value to those requiring constant supervision and elderly patients suffering from chronic illnesses. The IoT has the potential to not only keep the patients healthy and safe but also to improve how the doctors provide care as well.

A few estimates have revealed that by 2025 spending on Healthcare IoT solutions will reach around $1 trillion, and hopefully, will make the conditions favourable for highly accessible, on-time, and personalized health services for everyone.

The IoT can also enhance patient satisfaction and engagement by allowing patients to spend more time interacting with their doctors. This article will explore some of the major applications of healthcare IoT and the challenges it poses for healthcare today.

Applications of Healthcare IoT

Starting with managing chronic diseases to preventing a disease, there are a broad range of applications for IoT in the healthcare sector. Now, let’s dig deeper into each of the major applications.

Providing Constant Attention

The patients who are hospitalized and whose health status requires close attention can be monitored constantly using noninvasive, IoT driven monitoring. This kind of solution uses sensors to gather comprehensive physiological data and the cloud and gateways to examine and preserve the data and then send the examined information wirelessly to physicians for further analysis and review.

This eliminates the need for the doctor having to visit at regular intervals to check the vital signs of a patient, instead offering a continuous and automated flow of data. In this way, it enhances the quality of care via constant attention and lowers the cost involved by eliminating the need for a physician to engage actively in data gathering and analysis.

Building Trust

The connectivity of a healthcare system with the IoT places emphasis on the patient needs. This means timely intervention by doctors, enhanced accuracy in case of diagnosis, proactive treatments, and improved treatment outcomes result in a care that is highly accountable and gains trust among the patients.

Remote Patient Monitoring

All over the world, there are many people who face health issues due to lack of access to effective health monitoring. But, with the help of powerful, interrelated IoT solutions, monitoring the patients has become easier than ever.

These solutions can be utilized to capture the health data of a patient in a secure way from different sensors, make use of complex algorithms to examine the data and then share it via wireless connectivity with the physicians who can make proper health recommendations.

Reduced Costs

With the availability of real-time data from the connected healthcare solutions, the doctors can not only take better care of their patients but also lessen their number of visits to the patient as they can monitor their patients remotely. This decreases the overall health care costs as the costs involved in hospital stays and readmissions are cut down to a greater extent.

Configuring Emergency Alerts

Healthcare IoT allows care teams to gather and connect millions of data points regarding the personal fitness of a patient from wearables like activity, temperature, perspiration, sleep, and heart-rate. As a result, the information obtained from sensors can send out real-time alerts to caregivers and patients so they obtain event-triggered messaging such as triggers and alerts for elevated heart-rate and so on. This will hugely enhance workflow optimization and ensure all the care is handled from home.  

Challenges of IoT in Healthcare

The IoT continues to face challenges in spite of the promise of what it can achieve in healthcare. If these challenges are not addressed soon, they could put the IoT at risk of failure.

By intent and design, the IoT devices collect and transmit real-time data. The infrastructure required to receive and process this information should be designed and developed for scale. This means obtaining, processing, and storing data in real-time from millions of IoT devices and applying analytics to gain insights from this data. Unfortunately, most of the providers lack the know-how and infrastructure to access the data.

Also, most of the devices reporting healthcare data suffer from a lack of common security practices or standards. Due to this, many healthcare IT professionals have raised concerns about the risks associated with data breaches and IoT device tampering.

Other major challenges include lack of EHR system integration and lack of adoption of interoperability. Addressing these problems will further revolutionize the health industry as more organizations will start implementing IoT for their healthcare services.

Conclusion

Healthcare IoT is transforming the way the facilities are delivered to patients. In order to derive the true value of healthcare IoT, the interrelated healthcare devices and the processes that are supporting them must work as a joint system that is comprehensive, integrated, and secure. With healthcare IoT facing few challenges, the healthcare providers are hopeful that the IoT will have a positive impact on delivering valuable data and supporting patient care.  

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Since its conception, IoT has been impacting numerous businesses in unprecedented ways than expected. IoT infrastructure market, one of contemporary niche verticals of the building construction and infrastructure development sphere, now holds the reputation of being encompassed among the many IoT influenced business spheres. The proliferation of the Internet of things in infrastructure development has led to the procreation of smart homes and cities, touted as a revolutionary phenomenon of the 21st century. With the rising demand for connectivity to enable smart security, social surveillance, smart transportation, energy safety, smart metering, and efficient governance for enhancing consumer lifestyle, IoT infrastructure industry is likely to garner much acclaim in the ensuing years. Estimates compiled in a recent IoT infrastructure market research report forecast this business space to have accumulated a valuation of close to USD 15 billion in 2016.

U.S. IoT infrastructure market, by application, 2016 & 2024 (USD Billion)

A succinct overview of IoT infrastructure market in terms of the application spectrum

IoT infrastructure industry outlook from smart homes

The proliferation of IoT in the home sector has brought about a barrage of changes in consumer standard of living. IoT-enabled homes offer some of the best advantages that can transform a person’s lifestyle across the urban space. Smart devices such as the Nest thermostat, Amazon Echo, smart fridges, Google Home, Wink Relay and Controller, etc., have been popularized across IoT infrastructure market and liberally deployed in smart homes, subject to their incredible benefits such as controlled energy consumption, automated notifications, weather alerts, etc. Fiercely vying with one another to consolidate their positions in IoT infrastructure industry, tech companies have been going the whole hog to introduce highly advanced connected devices for smart homes.

IoT infrastructure market outlook from smart buildings

The deployment of big data and IoT in smart buildings helps deliver actionable insights to improve consumer living comfort, optimize building operations, and reduce energy expenditure. The robust rise in the number of connected devices being installed in smart buildings bears evidence to the fact that IoT infrastructure industry share from smart buildings is likely to plummet in the years ahead. Companies have been planning strategies to brainstorm numerous connected devices for exploiting the potential of IoT in buildings. Recently for instance, Kone signed on a multi-year deal with IBM, with an aim to maneuver the IBM IoT Cloud Platform for connecting, monitoring, and optimizing building components such as doors, elevators, turnstiles, and escalators.

IoT infrastructure market outlook from smart cities

A recently compiled report depicts that close to 60% U.S. citizens prefer living in smart cities, given their incredible advantages. The rising proliferation of smart cities is evident from the incredible proportion of smart city projects that are being undertaken across myriad geographies – which may have a mammoth impact on the revenue graph of IoT infrastructure industry. The numerous advantages provided by smart cities with regards to planning, finance, energy safety, transportation, and other urban aspects have accelerated their demand and popularity across IoT infrastructure market. In consequence, tech behemoths have been signing public-private partnerships, that would lead to the generation of layered framework to address the many challenges of smart city projects by building effective, connected solutions.

The Internet of Things, conceived back in the 1980s at the Carnegie Mellon University, has now metamorphosed into a prodigy that defines efficiency, sustainability, and convenience. The deployment of this concept in infrastructure is likely to open up a plethora of opportunities for construction companies, real estate developers, technology behemoths, and infrastructure development firms, that would strive to brainstorm numerous solutions for connected infrastructure, augmenting IoT infrastructure industry trends. An IoT related report by a research firm claims close to 1.40 billion IoT units to be shipped ahead for smart city projects by 2020, for smart homes, smart buildings, smart transportation, sustainability and climate change. This provides ample evidence to the fact that IoT infrastructure market is here to stay, boasting of a widespread array of technologies, platforms, and applications. A report compiled by Global Market Insights, Inc., claims IoT infrastructure market size to surpass a valuation of more than USD 130 billion by 2024 – which is apparently close to 8.5 times its value in 2016.

Source:https://www.gminsights.com/industry-analysis/iot-infrastructure-market

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Adapting To Digital Transformation

From incorporating a faster and more efficient fleet-tracking technology to application delivery, digital transformation has become a permeating voice in talking about taking businesses to the next level by fast-tracking their time to market, reducing optimization costs, and creating a fluid business model.
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For more than a century, advances in technology, machinery and automation have oftentimes replaced humans as a means to accomplish tasks. In this podcast, Rob Tiffany tackles the unsavory topic of workforce reduction as certain tasks have evolved from manual to mobile to IoT.

Listen to the Podcast 

 

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Security systems installed in a typical facility consists of cameras, access control, intrusion sensors and fire alarms. Typically, these devices are places behind a firewall on a dedicated network. Building control systems are installed on a secondary network can contains lighting, HVAC, fire protection, elevators/lifts, chillers and air/moisture sensors. These systems serve their purpose and will continue to be adapted and make facility systems design more complicated. This complexity can be controlled using common development tools and platforms. Not only will this approach make the process of creating smarter, safer, more energy efficient systems but will also reduce the number accidental deaths and injuries that occur every year.

 

The redundant network design approach is not a very efficient nor cost effective way of operating a facility. This is starting to change as savvy building managers are making the decision to integrate security and building control systems and map them onto a single network. This can entail integrating multiple disparate systems, sensors, NVR devices and video management software. The concept of integrating a camera or access control system to an HVAC system, or a visitor/facility management system or edge recording device to a lighting or fire protection system may seem unusual to some. Yet, this is where many security systems integrators and manufactures are missing out on untapped applications and services opportunities. Modern integrated security and building systems can give facility managers and security directors the tools to improve, simplify operations and reduce the efforts of the operations staff and points of control teams.

 

In the past, the security industry has relied on it’s own approach to integrated systems know as physical security information management (PSIM). PSIM attempts to provide an open architecture to integrate multiple security system products into a single operating platform. This approach has been very hit-or-miss and has left a bad taste in the mouths of systems integrators and end-users. On the flip side of the coin, facility managers have their own integration platform known as a building automation system (BAS). As it relates to physical security, BAS systems are intended to integrate with PSIMs and control individual security systems. However, BAS systems come in many different flavors; many of them are not viewed in a glowing light by building operation end users. Past integrations are not all filled with doom-and-gloom. There are some successful integrations attempted by the collaborative efforts of building controls and physical security organizations. The question is why is this design practice not more common where the benefits and economics make sense?

 

In order to facilitate the adoption and implementation of an integrated system the use of open standard protocols is an absolute must. The building automation industry created BACnet and LONworks which allow for real-time remote connectivity between sensors, actuators, controller devices and software. In the case of LONworks, hardware manufactures have the ability to include a chipset with built-in building control system support. It took some time, but finally the security industry created the protocols ONVIF and PSIA. These open architectures allows the end-user to choose vendors selecting either security or BAS equipment based on features and price. The end-user can also decide to install partial system upgrades without the risk of making costly investments in obsolete legacy systems. With that said, The security industry is curious about implementing the building controls protocols but needs an easier way to integrate them into their hardware and software products in an ad-hoc applications based manner.

 

There are security directors that are not completely sold on the idea of integrating with building control systems. On the other hand, facility managers may question the benefits of sharing a network with security systems especially when functions do not overlap with life-safety systems. However, system integration between building controls, physical and now cybersecurity offers more than just staffing convenience and operational efficiency. Here are a few results from a truly integrated security system.

Faster Response to Incidents – With the use of a robust mobile software solution and integration approaches such camera-to-access control-to-lighting or HVAC staff members can be freed from a console which makes them readily available to respond to incidents or equipment failure.

Provide more accurate compliance reports – Data provided by building controls and security edge devices can be paired with artificial intelligence technologies such as neural networks and genetic algorithms. This helps facilities to comply with government regulations with regards to security.

Reduce accidents and save money – Integrated systems provide better control of building and security systems. For example, if some accidentally stumbles into a restricted area or manages to make it to overly heated or chilled area the access control system, Variable air volume (VAV), or other HVAC system components can send alerts and create historical trend reports. Also a single network architecture can make managing system components easier.

 

Integrated building control and security systems are gaining some traction. However, it is still not a mainstream approach among many manufactures and systems integrators. One proposed solution is to utilize a common platform that is utilizes the industry protocol standards as application and system component building blocks.

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If you are planning to innovate your business and disrupt your niche with the Internet of Things, it may not be as easy as it seems. There are quite a few things that you need to be prepared with, and you should know certain facts about the technology before starting out.

Before anything else, it is important for you to be patient, understand the nuances of the technology and know how best to incorporate it into your business.

It is true that your IoT product will differ from what your competitors are planning to offer and in the technologically driven environment, it is important to differentiate. But, there are underlying patterns that cannot differ, which is why you need to know the basics of IoT before getting started.

So, are you ready to know them?

#1 Start with Design Thinking

When you are surfacing a company with IoT at the core, it is important to think slightly different. All your life as a businessman, you have thought of tactical ways to get your business started, focusing on objectives and goals. However, when you are dealing with IoT, your focus will need to differ. The idea is to think from the user’s perspective and create a framework that will create more practical and usable approaches.

The design strategy should be your first priority. You need to know how and what will work when you are designing for the users. There are a few things you might want to know before planning the design.

What is it that your users need? When we automated home ACs over WiFi, the purpose was to allow remote access, and not keep an eye out for another remote. Once this point is cleared, you may want to think of path defining solutions for the basic idea. The remote needed to go obsolete, which is why the path defining idea was to convert your mobile into a remote. Finally, you will need to build the prototypes and craft a story around it. The idea is to define a product that talks for itself.

#2 Workaround security

When you are working on an IoT-based startup, you might want to think about a security-first solution. You will need to protect the data that can be availed from the connected device so as to offer better security. Remember, the security for IoT based solutions are complex and difficult as compared to a regular security need. If there are more connected devices in the network, the security threat grows and you will find it difficult to control and manage.

So, when you are planning an IoT solution, you will need to think of security before you plan anything else in the device management or define other aspects of the solution.

#3 Managing the costs

Like with any other venture, you will need to think IoT solution development cost. There are costs involved in every stage, and these costs evolve through your development phases.

For instance, let’s start with the development cost of the IoT solution. From planning to actual feature selection to development with connected devices, there are various phases that you need to manage and work around.

Similarly, introducing security into your IoT solution will cost you, which you need to think about before panning it out. Finally, you will need to plan for the operation and maintenance costs of the IoT business, which requires either bootstrapped funds or investment, if you want to survive in the long run. Remember, the IoT business will not get your immediate returns on the investment.

#4 Scaling is different

The scaling of your IoT business works in a different manner from the scaling of other businesses. If a business works at a particular size, it is not necessary it will work for other sizes too. So, before scaling, you will need to figure out will the scaling manage the increased needs and demands of your company. The prototype scaling would differ from the actual business scaling.

Conclusion

IoT businesses are different from the normal businesses, and you will need to understand the nuances before you start building the prototype and acquiring customers. It is more user-oriented and works with a focus on the end goal to be achieved. If you are planning an IoT business, you should ideally consult a professional before starting with the strategies.

 

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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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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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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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