My Latest Industry Week Column: why the edge is critical for IoT

Posted on 1st June 2019 in data, Industrial Internet of Things, Internet of Things, management, manufacturing, open data, sensors, Uncategorized

As is so often the case, technological success can often result in unintended consequences that, left unremedied, could negate the benefits.

As my latest Industry Week column I looked at one of those issues — the explosion of real-time sensor data collected by the IoT — and the solution to the problem that adds many other benefits in the process, shifting at least part of the data processing from the cloud to the “edge” of the system, preferably at the point of collection.

As I pointed out, if the data must be moved to the cloud first for processing (no mean feat, BTW, because it can also overwhelm the transmission networks) and then back to the collection point for action, it negates the IoT’s major benefit, being able to collect and then act on data in near-real time, allowing precise regulation of things.

Of course edge processing adds additional costs for distributed processing hardware and software, and can add risk if the device is easily tampered with, but, overall, it seems to me the edge should not replace, but definitely supplement the cloud in robust IoT systems.

I based the column on a comprehensive, short-of-over-promotion report, Data at the Edge, created by an industry consortium, State of the Edge. It’s a quick read, and I recommend it!

Read it and let me know what you think.

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Digital Twins: Could They Have Uncovered the Boeing 737 Problems Sooner? — and how might they benefit your company

Posted on 20th March 2019 in Augmented Reality, design, Internet of Things, maintenance, management, manufacturing, marketing, sensors, strategy, supply chain

Tragically, the Boeing 737-Max includes an Internet of Things technology — the “digital twin” — that might have avoided the current tragedies, if only its use had been extended from the engines to the entire plane’s operations. Even if your products don’t involve such high stakes as a jet plane does, your company should seriously consider adding the digital twin to your services, because it can benefit every aspect of your operations and strategy.

Let me explain.

A joint venture company between GE Aviation and a French company makes the Max’s CFM International LEAP engines.  Every one of them is paired to a digital representation of the engine, a digital twin, that allows an engineer thousands of miles away to instantly monitor an inflight engine’s current operations.  That, among many other benefits, allows GE to replace the preventive maintenance of the past, which had to rely on after-the-fact data about when the average engine needed to be maintained, with “predictive maintenance,” which uses data about the earliest evidence of a possible problem to trigger a sequence to intervene early on, well before a major problem. At its most extreme, that could mean that when a plane whose sensors have detected an engine anomaly lands at its destination, the mechanic would be ready to go, knowing not only where in the engine the problem existed, but also having had the replacement part automatically dispatched from a distribution hub, so the replacement could be done quickly and at least cost.  No wonder GE has created more than hundreds of thousands of digital twins for products ranging from the jet engines to medical devices.

What if Boeing had extended the “digital twin” concept to the entire plane’s operations, including the navigation software?  The real essence of the Internet of Things — of which the digital twin is perhaps the ideal example of how the IoT merges the digital and physical — is that it allows everyone who needs it to instantly share (the verb is critical!) that real-time data — something that was impossible in the past.  In this case, that could mean real-time sharing between the cockpit, the airline, and Boeing, rather than after the fact harvesting of data from a plane’s “black box.” Hypothetically, the ability to share the real-time cockpit data would have meant that all those parties could have collaboratively brainstormed solutions to possible in-flight problems such as the maneuvering characteristics augmentation system, (MCAS) that has been fingered the possible cause of the problems.

OK, how does this ability to share real-time data from a digital twin apply to your company’s products and operations? 

The potential benefits extend to every aspect of your operations:

More radically, the digital twin’s benefits in terms of reliability and improved design can allow a fundamental change in your business model, away from selling products to leasing them, with the customer’s actual cost based on use of the product (if the turbine is sitting on the ground being repaired, it ain’t bringing the manufacturer any revenue!).  All three major jet turbine manufacturers — GE, Pratt & Whitney, and Rolls-Royce — have made this switch. They can even create new revenue streams by selling the real-time in-flight data to airlines, which can mash it up with atmospheric conditions, fuel prices, etc., to maximize flight efficiency.  Similarly, Hortilux, which makes greenhouse lights, now provides artificial light, plus data on growing conditions, rather than selling the bulbs.

Even more important than the potential to improve individual aspects of operations through digital twins is the potential for innovation and creativity if all departments (and, if you choose, even supply chain and distribution network partners and, conceivably, your customers) look at and can discuss the data—ground truth—simultaneously.

Determination of exactly what happened and why with the 737-Maxes will require intense scrutiny, but your company can benefit from the wake-up call these tragedies have provided to the IoT’s new-found ability to gather and share operating data instantly, with the result being improving every aspect of your operations and strategy.

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Previewing “The Future Is Smart”: Siemens Leads Way In IoT Transformation

Posted on 26th July 2018 in 3-D printing, Augmented Reality, data, design, Essential Truths, Internet of Things, M2M, maintenance, management, manufacturing, paradigm shift, sensors, strategy, transportation

Huzzah!

On August 7th, HarperCollins’ new Leadership imprint (formerly Amacom) will publish The Future Is Smart, my guide to IoT strategy for businesses and the general public.  BTW: write me if you’d like to arrange a speaking engagement/book signing event!

As part of the build-up to the release, here’s another excerpt from the book, drawn from Chapter 5: “Siemens and GE:Old War Horses Leading the IoT Revolution.” It zeroes in on these two industrial companies from the 19th (!!) century that are arguably among the top IoT companies in the world (although, sadly, GE’s transformation, which I’ll detail in the next excerpt, has not resulted — so far — in a return to its former profitability). I highlighted these two companies in part to give comfort to old-line manufacturers that have been reluctant to embrace the IoT, and in part to shame them: if they can do it, why can’t you?

Siemens is a particularly exciting example, applying IoT thinking and technology to gain a competitive edge in the railroad business, which it has been involved in since the 19th century, and because its Amberg “Factory of the Future” is the epitome of the benefits of applying the IoT to manufacturing,  The excerpt is long, but I think the details on Siemens’ IoT transformation will make it worthwhile reading.

 

For all their (referring to Siemens and GE) own distinctive products and services, there are startling parallels between the two that are relevant to this book, particularly for readers whose companies have been unaware of the IoT or are modestly testing the waters. Both Siemens and GE have fully committed to the IoT and are radically reinventing themselves, their products, and their services. 

At the same time, they are not abandoning the physical for the digital: they still make products such as trains (NB: since this book went to press, GE announced it will quit to locomotive business as it struggles to regain momentum) and large medical diagnostic devices that remain necessary in the new economy, and those devices (as well as the new software lines) are used by many other companies in their own manufacturing. Both companies aren’t just testing the IoT: they are on the bleeding edge of innovation in terms of both IoT technology and services.

Siemens and GE embody most of the marks of the IoT company outlined in the first chapter:

  • Unprecedented assembly-line precision and product quality
  • Drastically lower maintenance costs and product failure
  • Increased customer delight and loyalty
  • Improved decision-making
  • Creating new business models and revenue streams

And, while they haven’t formally addressed the sixth IoT hallmark, the circular management organization, both companies exhibit management characteristics consistent with it.

Bottom-line: if these two relics of the early Industrial Age can make the IoT transformation, why can’t you?

(Siemens’) innovations in industrial automation are now associated with the concept of the digital factory. “Siemens set the course for the digital automation of entire production facilities as far back as 1996, when the launch of its Totally Integrated Automation (TIA) Portal enabled companies to coordinate elements of their production operations and to closely intermesh hardware with software.”

Siemens has benefited in recent years from the German government’s formal strategy for what it calls “Industrie 4.0,” to merge physical products with digital controls and communications. The initiative is supported by funding from the German Federal Ministry of Education and Research and the German Federal Ministry of Economic Affairs and Energy and emphasizes the merger of the digital and physical in manufacturing through cyber-physical control systems. Because the U.S. federal government doesn’t weigh in on specific economic plans to the same extent, the concept is more advanced in Europe, and the term has gathered cachet, especially as specific examples have proved profitable.

Factory of the Future:
The shining example of Industrie 4.0 is the previously mentioned Siemens plant in Amberg. It has increasingly computerized over the past 25 twenty-five years, and now is a laboratory for fusion of the physical and digital.

The plant’s 99.99885 percent quality rate would be astounding by any measure, but is even more incredible when you realize that it does not do daily repetitions of the same mass-production product run. Instead, Amberg is where the company makes the Simatic programmable logic controls (PLCs) .. that are the heart of its industrial output and which are used worldwide to allow Machine-to-Machine (M2M) automated assembly line self-regulation. They are made in more than a thousand variations for 60,000 customers worldwide, requiring frequent readjustments of the production line. In one of the ultimate examples of eating your own dog food, a thousand Simatic units are used to control the assembly line. Total output at the factory is 12 million yearly, or approximately one per second.

One downside of the Amberg system’s efficiency is that automation has nearly eliminated assembly line jobs: the only time humans touch one of the products is to put the initial circuit board on the assembly line. The 1,100-person workforce deals almost entirely with computer issues and overall supervision of the assembly line. Nevertheless, Siemens doesn’t visualize a totally automated, workerless factory in the future:

“We’re not planning to create a workerless factory,” says [Plant Manager Professor Karl-Heinz] Büttner. After all, the machines themselves might be efficient, but they don’t come up with ideas for improving the system. Büttner adds that the employees’ suggested improvements account for 40 percent of annual productivity increases. The remaining 60 percent is a result of infrastructure investments, such as the purchase of new assembly lines and the innovative improvement of logistics equipment. The basic idea here, says Büttner, is that “employees are much better than management at determining what works or doesn’t work in daily operation and how processes can be optimized.” In 2013 the [plant] adopted 13,000 of these ideas and rewarded employees with payments totaling around €1 million.

As Siemens develops new IIoT software, it is deployed at the Amberg factory to control the Simatic control units, which generate more than 50 million data points daily for analysis. Among other programs, the factory runs the NX and Teamcenter project lifecycle management software, allowing the staff to share realtime insights on the assembly line and fine-tune its operation.

Siemens’s strategy of merging the physical and digital has meant that its software offerings constantly expand, and they facilitate the kind of real and virtual collaborative workstyles that will be discussed at length in Chapter 8. Among others, they include offerings that specifically address key aspects of the IoT:

  • Product Lifecycle Management software programs, which let engineers both model new products and extensively test them virtually, without having to build and test physical models. This both cuts costs and allows more experimentation with “what if” variations on a design, because the risk of creating alternatives is so low. As we will see later, products designed with PLM can reach the market 50 percent faster. One particularly interesting part of the PLM offerings is one specifically for additive manufacturing (i.e., 3-D printing), to capitalize on this emerging option. Siemens has brought all of these programs together under the Teamcenter label, emphasizing that it provides an “open framework for interoperability,” a critical example of the “share the data” Essential Truth discussed in Chapter 2, allowing anyone who needs it companywide to access critical realtime data.
  • Digital Twins used in coordination with PLM, discussed earlier (Chapter 4) as the highest manifestation of the digital/physical synthesis, allow rigorous testing of products before they are launched.
  • Perhaps the most important of these software offerings for full realization of the Industrie 4.0 vision is the new combination of Siemens XHQ Operations Intelligence Software with the open-systems Siemens MindSphere cloud that adds advanced analytics and machine learning. Also, because it is cloud-based, the XHQ data can be ported to other cloud-based applications. If your company is considering an IoT initiative, the cloud-based alternative not only can save money compared to self-storage, but also opens the opportunity for using cloud-based Software as a Service (SaaS).

 

Railigent

Fittingly, some of the most dramatic examples of Siemens’s IoT thinking in action have centered on one of its oldest lines of business: those electric trains invented in the nineteenth century.  The company’s Railigent system (which connects to its IoT Mindsphere platform) can:

  • cut rail systems’ operating costs by up to 10%
  • deliver eye-popping on-time performance (only 1 of 2,300 trains was late!)
  • and assure 99% availability through predictive maintenance.

Its new Mobility Services have taken over maintenance for more than fifty rail and transit programs.

Again, the company’s years of experience building and operating trains pays off in the cyberworld. Dr. Sebastian Schoning, ceo of Siemens’s client Gehring Technologies, which manufactures precision honing tools, told me that it was easier to sell Siemens’s digital services to his own client base because so much of the products they already own include Siemens devices, giving his customers confidence in the new offerings.

The key to Siemens’s Mobility Services is Sinalytics, its platform architecture for data analysis not just for rail, but also for industries ranging from medical equipment to windfarms. More than 300,000 devices currently feed realtime data to the platform. Sinalytics capitalizes on the data for multiple uses, including connectivity, data integration, analytics, and the all-important cyber security. They call the result not Big Data, but Smart Data. The platform also allows merging the data with data from sources such as weather forecasts which, in combination, can let clients optimize operating efficiency on a real-time M2M basis.

Elements of an IoT system on the trains that can be adapted to other physical products include:

  • Sensing. There are sensors on the engines and gearboxes. Vibration sensors on microphones measure noises from bearings in commuter trains. They can even measure how engine oil is aging, so it can be changed when really needed, rather than on an arbitrary schedule, a key predictive maintenance advantage.
  • Algorithms: These make sense of the data and act on it. They read out patterns, record deviations, and compare them with train control systems or with vehicles of the same type.
  • Predictive Maintenance: This replaces scheduled maintenance, dramatically reducing downtime and catastrophic failure. For example: “There’s a warning in one of the windows (of the control center display): engine temperature unusual. ‘We need to analyze the situation in greater depth to know what to do next—we call it root cause analysis,’ (says) Vice-President for Customer Support Herbert Padinger. ‘We look at its history and draw on comparative data from the fleet as a whole.’ Clicking on the message opens a chart showing changes in temperature during the past three months. The increased heat is gradually traced to a signal assembly. The Siemens experts talk with the customer to establish how urgent the need for action is, and then take the most appropriate steps.”8 Padinger says that temperature and vibration analyses from the critical gearboxes gives Siemens at least three days advance notice of a breakdown—plenty of time for maintenance or replacement. Predictive maintenance is now the norm for 70 to 80 percent of Siemens’s repairs.
  • Security: This is especially important given all of the miles of track and large crowds on station platforms. It includes video-based train dispatch and platform surveillance using Siemens’s SITRAIL D system, as well as cameras in the trains. The protections have to run the gamut from physical attacks to cyber-attacks. For security, the data is shared by digital radio, not networks that are also shared by consumers.

When operations of physical objects are digitized, it allows seamlessly integrating emerging digital technologies into the services—making these huge engines showcases for the newest technologies. For example, Siemens Digital Services also included augmented reality (so repair personnel can see manuals on heads-up displays), social collaboration platforms, and—perhaps most important—3-D printing-based additive manufacturing, so that replacement parts can be delivered with unprecedented speed. 3-D printing also allows a dramatic reduction in parts inventories, It allows for replacement of parts that may no longer be available through conventional parts depots. It may even improve on the original part’s function and durability, based on practical experience gained from observing the parts in use. For example, it’s often possible with 3-D printed replacement parts to consolidate three or four separate components into a single one, strengthening and simplifying it. Siemens has used 3-D printing for the past last three years, and it lets them assure customers that they will have replacement parts for the locomotive’s entire lifespan, which can exceed thirty years.

The new Mobility Services approach’s results are dramatic:

  • None of the Velaro trains that Siemens maintains for several operators have broken down since implementing Sinalytics. Among those in Spain only one has left more than fifteen minutes behind time in 2,300 trips: a 0.0004 percent lateness rate.
  • Reliability for London’s West Coast Mainline is 99.7 percent.
  • Perhaps most impressive because of the extreme cold conditions it must endure, the reliability rate for the Velaro service in Russia is 99.9 percent.11

Siemens’s ultimate goal is higher: what the company calls (pardon the pun) 100 percent Railability.

When it does reach those previously inconceivable quality benchmarks, Siemens predicts that, as the software and sensors evolve, the next stage will be new business models in which billing will be determined by guaranteeing customers availability and performance. The manufacturing industry is now at the stage where the automation of complete workflows is the only way to ensure a long-term, defendable, competitive position.

Siemens emphasizes that it’s not enough to simply digitize the design process. Everything from design through supply chain, manufacturing, distribution, and service must be linked in a continuous digital web, with “complete digital representation of the entire physical value chain is the ultimate goal.”

 

The fact that Siemens doesn’t just sell these IoT services but makes their own manufacturing the laboratory to develop and test them is an incredible testimonial to the IoT’s transformative potential in every aspect of companies’ operations. So, as I asked above, why are you holding back? Like to think that The Future Is Smart will give you the manual you need to make the transition (why wait for August  7, when you can preorder today?).

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Live Blogging #LlveWorx ’18, Day 2

Posted on 19th June 2018 in 3-D printing, AI, Augmented Reality, automotive, collaboration, communication, data, design, health, Internet of Things, logistics, M2M, maintenance, management, manufacturing, marketing, mobile, paradigm shift, retail, security, sensors, services, strategy, supply chain, transportation

Aiden Quilligan, Accenture Industry X.0, on AI:

  • Mindset and AI: must undo what Hollywood has done on this over years, pose it as human vs. machine.
  • We think it should be human PLUS machine.
  • he’s never seen anything move as fast as AI, especially in robotics
  • now, co-bots that work along side us
  • exoskeletons
  • what do we mean by AI?  Machine learning.  AI is range of technologies that can learn and then act. AI is the “new work colleague” we need to learn to get along with.
  • predictions: will generate #2.9 trillion in biz value and recover 6.2 billion hours of worker productivity in 2021.
  • myths:
    • 1) robots evil, coming for us: nothing inherently anti-human in them.
    • 2) will take our jobs. Element of truth in terms of repetitive, boring work that will be replaced. They will fill in for retiring workers. Some new industries created by them.  Believe there will be net creation of jobs.
    • 3) current approaches will still work.

6 steps to the Monetization of IoT, Terry Hughes:

  • Digital native companies (Uber) vs. digitally transforming companies
  • also companies such as Kodak that didn’t transform at all (vs. Fujifilm, which has transformed).
  • Forbes: 84% of companies have failed with at least one transformation program.  Each time you fail you lose 1/2 billion
  • steps:
    • 1) devices with potential
    • 2) cloud network communication
    • 3) software distribution
    • 4) partner and provider ecosystem
    • 5) create a marketplace.
    • 6) monetization of assets.
  • crazy example of software company that still ships packages rather than just download because of initial cost in new delivery system
  • 3 big software challenges for digitally transforming company
    • fragmented silos of software by product, business unit & software
    • messy and complex distribution channels
    • often no link between software and the hardware that it relates to
  • importance of an ecosystem
    • Blackberry example of one that didn’t have the ecosystem
  • 3rd parties will innovate and add value around a manufacturer’s core products
  • in IoT it’s a land grab for mindshare of 3rd-party innovators.
  • need strong developer program
  • tools for app development and integration
  • ease of building and publishing apps
  • path to discovery and revenue for developer
  • IDC: developer ecosystem allow enterprises to massively scale distribution
  • digitally native companies have totally different models (will get details later…)
  • hybrids:
    • GE Healthcare:  working with Gallus BioPharma
    • Heidelberg & Eig have digital biz model for folding carton printing. Pay per use
  • Ford is heading for mobility as a transformation

 

Bernard Marr: Why IoT, Combined With AI and Big Data, Fuels 4th Industrial Revolution

 

  • connecting everything in house to Internet
  • Spotify: their vision is they understand us better. Can correlate your activity on Apple Watch (such as spinning) & create a play list based on that)
  • FitBit: the photo will estimate your calorie content.
  • John Deere
  • ShotSpotter: the company that monitors gun shots
  • understanding customers & markets better than before:
    • Facebook: better at face recognition than we are. They can predict your IQ, your relationship status.
  • Lot of frightening, IMHO, examples of AI analyzing individuals and responding without consideration of ethics and privacy
  • 3) improving operations and efficiency:
    • self-driving boats
    • drones
    • medicine through Watson

panel on IoT:

  • Don’t be afraid of the cloud
  • Ryan Cahalane, Colfax: prepare for big, start small and move fast. They had remarkable growth with switch to IoT.  Not a digital strategy, but digital in everything they do. Have “connected welders,” for example.
  • Justin Hester, Hirotec: most importatnt strategic digital transformation decision your organization can make is the selection of a platform. The platform is the underlying digital thread that enables your team to meet  the unique and chanding needs of your organization and to scale those solutions rapidly. “Assisted reality” in ThingWorx
  • Shane O’Callahan, TSM (Ireland):  Make industrial automation equipment for manufacturing. Understanding your key value driver is where to start. Then start samll, scale fast and get a win!

Jeffrey Miller, PTC: Digital Transformation:

  • if you start with digital strategy you’re starting in wrong place Start with business strategy. 
  • Couple with innovation vision merged with digital strategy. Add business use cases.
  • Jobs: it’s not how much you spend on R & D, but “about the people you have, you you’re dled, and how much you get it”
  • create an environment for innovation
    • do we encourage experimentation?
    • is it ok to fail
  • identify digital technologies to provide the required operating capabilities:
    • have we conducted proofs of concept?
    • experimented, tested  and validated?
    • reviewed use cases & success studies?
    • delivered small, important, scalable successes?

Matt,  PTC: Bringing Business Value to AR:

  • augmented service guidance
  • remote expert guidance
  • manufacturing: machine setup and turnover, assembly and process
  • example of Bell & Howell towers to store online sales in WalMart stores for customer pickup: very expensive to send one to a store for salesperson to use in sales — now just use AR app to give realistic demo without expense.
  • service: poor documentation organization, wants accurate, relevant, onsite info for technician. Want to remove return visits because the repair wasn’t done 1st time, or there’s a new technician. Manuals in binders, etc. Instead, with AR, requirements are quick access to current info. Finally, a demo.

Suchitra Bose, Accenture: Manufacturing IIoT, Driving the Speed of Digital Manufacturing:

  • convergence of IT and OT
  • expanding digital footprint across your entire factory
  • PTC has wide range of case studies (“use cases” in biz speak…) on aspects of IoT & manufacturing.
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Wahoo! Liveblogging #Liveworx ’18!

Posted on 18th June 2018 in 3-D printing, AI, Augmented Reality, automotive, collaboration, data, Internet of Things, logistics, M2M, maintenance, management, manufacturing, marketing, mobile, paradigm shift, privacy, retail, security, sensors, strategy

Always my fav event, I’ll be liveblogging #LiveWorx ’18.  Stay tuned!

Keynote: Jim Heppelmann:

  • “from a place to a pace” — how fast are we moving?
  • no longer OK to think of a future destination, builds inertia (“your main competitor”). Disruption may have already happened. Hard to sustain advantage due to pace of change. Must “embrace a pace of change”
  • Um, this sounds like argument for my circular company paradigm shift!!!
  • Customer Experience Center will occupy top floor of new building.
  • combo of  physical, human and digital — transforming all at once speeds change:
    • physical: been constrained by subtractive manufacturing, while nature improves via cell division (i.e., additive). “Adopt Mother Nature’s mindset.” — new additive aspects of Creo. Example of Triumph cycle sing-arm using additive. CREO uses AI to optimize performance: non-symmetrical design. Still need to use simulation tests: new intermittent, continuous style: they are doing new partnership with ANSYS (product simulation software), unified modeling and simulation with no gaps. Historically, simulation only used at end of design cycle, now can use it throughout the process: “pervasive simulation.”
      • ANSYS “Discovery Live”: optimizes for real-time. Integrates with Creo — instant feedback on new designs. “simulation critical to innovation.”
    • digital: working with Microsoft Azure (Rodney Clark, Microsoft IoT VP). Microsoft investing $5b in IoT.  1st collaboration is an industrial welder: IoT data optimizes productivity.  BAE can train new employees 30-40% quicker.
    • finally, human: “Mother Nature designed ups to interface with the physical. How do we integrate with the digital? — Siri, Alexa, Cortna still too slow.  Sight is our best bet. “Need direct pipeline to reality ” — that’s AR. “Smart, connected humans.” Sysmex: for medical lab analysis. Hospitals need real-time access to blood cell analysis. They have real-time calibration of analysis equipment. Also improving knowledge of the support techs, using AR and digital twins when repairs are needed.
      • Will help 2.5 billion workers become more productive
      • AR can project how a process is being programmed (gotta see this one. will try to get video).
      • All of their human/digital interface initiatives united under Vuforia. Already have 10,000 enterprises using it.
    • Factories are a new focus of PTC. 200 companies now using it in 800 factories. Examples from Woodward & Colfax.  Big savings on new employee training.

Keynote: Prof. Linda Hill, HBS, “Collective Genius”:

  • Innovation= novel + useful
  • Example of Pixar: collective genius “filmmaking is a team sport.”
  • 3 characteristics of creative organizations they looked at:
    • “creative abrasion” — diversity and debate
    • “creative agility” — quickly test the idea & get feedback. Experiment rather than run pilots, which often include politics
    • “creative resolution” — ability to make integrative decisions. Don’t necessarily defer to the experts.
    • sense of community and shared purpose.
  • values: bold ambition, collaboration, responsibility, learning.
  • rules of engagement: respect, trust, influence, see the whole, question everything, be data-driven.

Ray Miciek, Aquitas Solutions. Getting Started on IoT-based Maintenance:

  • his company specializes in asset maintenance.
  • “produce products with assets that never fail”
  • 82% of all asset failures are random, because they are more IT-related now
  • find someplace in org. where you could gain info to avoid failure.
  • Can start small, then quickly expand.

 

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Great Podcast Discussion of #IoT Strategy With Old Friend Jason Daniels

Posted on 13th March 2018 in 3-D printing, aging, agriculture, circular company, cities, collaboration, data, design, energy, environmental, Essential Truths, government, health, home automation, Internet of Things, logistics, m-health, M2M, maintenance, management, manufacturing, marketing, mobile, open data, paradigm shift, privacy, Quantified Self, retail, security, seniors, sensors, services, smart cities, smart home, SmartAging, strategy, supply chain, transportation, wearables

Right after I submitted my final manuscript for The Future is Smart I had a chance to spend an hour with old friend Jason Daniels (we collaborated on a series of “21st Century Homeland Security Tips You Won’t Hear From Officials” videos back when I was a homeland security theorist) on his “Studio @ 50 Oliver” podcast.

We covered just about every topic I hit in the book, with a heavy emphasis on the attitude shifts (“IoT Essential Truths” needed to really capitalize on the IoT and the bleeding-edge concept I introduce at the end of the book, the “Circular Corporation,” with departments and individuals (even including your supply chain, distribution network and customers, if you choose) in a continuous, circular management style revolving around a shared real-time IoT hub.  Hope you’ll enjoy it!

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Liveblogging from Internet of Things Global Summit

Posted on 10th October 2017 in communication, government, Internet of Things, privacy, security, sensors, smart cities, transportation, US government

Critical Infrastructure and IoT

Robert Metzger, Shareholder, Rogers Joseph O’Donnell 

  • a variety of constraints to direct government involvement in IoT
  • regulators: doesn’t trust private sector to do enough, but regulation tends to be prescriptive.
  • NIST can play critical role: standards and best practices, esp. on privacy and security.
  • Comparatively, any company knows more about potential and liabilities of IoT than any government body. Can lead to bewildering array of IoT regulations that can hamper the problem.
  • Business model problem: security expensive, may require more power, add less functionality, all of which run against incentive to get the service out at lowest price. Need selective regulation and minimum standards. Government should require minimum standards as part of its procurement. Government rarely willing to pay for this.
  • Pending US regulation shows constant tension between regulation and innovation.

             2017 IoT Summit

Gary Butler, CEO, Camgian 

  • Utah cities network embedding sensors.
  • Scalability and flexibility needed. Must be able to interface with constantly improving sensors.
  • Expensive to retrofit sensors on infrastructure.
  • From physical security perspective: cameras, etc. to provide real-time situational awareness. Beyond human surveillance. Add AI to augment human surveillance.
  • “Dealing with ‘data deluge.'”  Example of proliferation of drones. NIST might help with developing standards for this.
  • Battery systems: reducing power consumption & creating energy-dense batteries. Government could help. Government could also be a leader in adoption.

 

Cyber-Criminality, Security and Risk in an IoT World

John Carlin, Chair, Cybersecurity & Technology Program, Aspen Institute

  • Social media involved in most cyberwar attacks & most perps under 21.  They become linked solely by social media.
  • offensive threats far outstrip defenses when it comes to data
  • now we’re connecting billions of things, very vulnerable. Add in driverless cars & threat even greater. Examples: non-encrypted data from pacemakers, and the WIRED Jeep demo.

Belisario Contreras, Cyber Security Program Manager, Organization of American States

  • must think globally.
  • criminals have all the time to prepare, we must respond within minutes.
  • comprehensive approach: broad policy framework in 6 Latin American countries.

Samia Melhem, Global Lead, Digital Development, World Bank

  • projects: she works on telecommunications and transportation investing in government infrastructure in these areas. Most of these governments have been handicapped by lack of funding. Need expert data integrators. Integrating cybersecurity.

Stephen Pattison, VP Public Affairs, ARM

  • (yikes, never thought about this!) cyberterrorist hacks self-driving car & drives it into a crowds.
  • many cyber-engineers who might go to dark side — why hasn’t this been studied?
  • could we get to point where IoT-devices are certified secure (but threats continually evolve. Upgradeability is critical.
  • do we need a whistleblower protection?
  • “big data starts with little data”

Session 4: Key Policy Considerations for Building the Cars of Tomorrow – What do Industry Stakeholders Want from Policymakers?

Ken DiPrima, AVP New Product Development, IoT Solutions, AT&T

  • 4-level security approach: emphasis on end-point, locked-down connectivity through SIM, application level …
  • deep in 5-G: how do you leverage it, esp. for cars?
  • connecting 25+ of auto OEMs. Lot of trials.

Rob Yates, Co-President, Lemay Yates Associates

  • massive increase in connectivity. What do you do with all the data? Will require massive infrastructure increase.

Michelle Avary, Executive Board, FASTR, VP Automotive, Aeris

  • about 1 Gig of data per car with present cars. Up to 30 with a lot of streaming.
  • don’t need connectivity for self-driving car: but why not have connectivity? Also important f0r the vehicle to know and communicate its physical state. Machine learning needs data to progress.
  • people won’t buy vehicles when they are really autonomous — economics won’t support it, will move to mobility as a service.

Paul Scullion, Senior Manager, Vehicle Safety and Connected Automation, Global Automakers

  • emphasis on connected cars, how it might affect ownership patterns.
  • regulatory process slow, but a lot of action on state level. “fear and uncertainty” on state level. Balance of safety and innovation.

Steven Bayless, Regulatory Affairs & Public Policy, Intelligent Transportation Society of America

  • issues: for example, can you get traffic signals to change based on data from cars?
  • car industry doesn’t have lot of experience with collaborative issues.

How Are Smart Cities Being Developed and Leveraged for the Citizen?

Sokwoo Rhee, Associate Director of Cyber-Physical Systems Program, National Institute of Standards and Technology (NIST)

  • NIST GCTC Approach: Smart and Secure Cities. Partnered with Homeland Security to bring in cybersecurity & privacy at the basis of smart city efforts “Smart and Secure Cities and Communities Challenge”

Bob Bennett, Chief Innovation Officer, City of Kansas, MO

  • fusing “silos of awesomeness.”
  • 85% of data you need for smart cities already available.
  • “don’t blow up silos, just put windows on them.”
  • downtown is 53 smartest blocks in US
  • can now do predictive maintenance on roads
  • Prospect Ave.: neighborhood with worst problems. Major priority.
  • great program involving multiple data sources, to predict and take care of potholes — not only predictive maintenance but also use a new pothole mix that can last 12 years 
  • 122 common factors all cities doing smart cities look at!
  • cities have money for all sorts of previously allocated issues — need to get the city manager, not mayor, to deal with it
  • privacy and security: their private-sector partner has great resoures, complemented by the city’s own staff.

Mike Zeto, AVP General Manager, IoT Solutions, AT&T

  • THE AT&T Smart Cities guy. 
  • creating services to facilitate smart cities.
  • energy and utilities are major focus in scaling smart cities, including capital funding. AT&T Digital Infrastructure (done with GE) “iPhone for cities.”
  • work in Miami-Dade that improved public safety, especially in public housing. Similar project in Atlanta.
  • privacy and security: their resources in both have been one of their strengths from the beginning.

Greg Toth, Founder, Internet of Things DC

  • security issues as big as ever
  • smart city collaboration booming
  • smart home stagnating because early adopter boom over, value not sure
  • Quantified-Self devices not really taking hold (yours truly was one of very few attendees who said they were still using their devices — you’d have to tear my Apple Watch off).
  • community involvement greater than ever
  • looming problem of maintaining network of sensors as they age
  • privacy & security: privacy and security aren’t top priorities for most startups.

DAY TWO:

IoT TECH TALKS

  • Dominik Schiener, Co-Founder , IOTA speaking on blockchain
    • working with IoT version of blockchain for IoT — big feature is it is scaleable
    • why do we need it?  Data sets shared among all parties. Each can verify the datasets of other participants. Datasets that have been tampered are excluded.
    • Creates immutable single source of truth.
    • It also facilitates payments, esp. micropayments (even machine to machine)
    • Allows smart contracts. Fully transparent. Smart and trustless escrow.
    • Facilitates “machine economy”
    • Toward “smart decentralization”
    • Use cases:
      • secure car data — VW. Can’t be faked.
      • Pan-European charging stations for EVs. “Give machines wallets”
      • Supply chain tracking — probably 1st area to really adopt blockchain
      • Data marketplace — buy and sell data securely (consumers can become pro-sumers, selling their personal data).
      • audit trail. https://audit-trail.tangle.works
  • DJ Saul, CMO & Managing Director, iStrategyLabs IoT, AI and Augmented Reality
    • focusing on marketing uses.

Raising the bar for federal IoT Security – ‘The Internet of Things Cybersecurity Improvement Act’

  • Jim Langevin, Congressman, US House of Representatives
    • very real threat with IoT
    • technology outpacing the law
    • far too many manufacturers don’t make security a priority. Are customers aware?
    • consumers have right to know about protections (or lack thereof)
    • “failure is not an option”
    • need rigorous testing
  • Beau Woods, Deputy Director, Cyber Statecraft Initiative, Atlantic Council
    • intersection of cybersecurity & human condition
    • dependence on connected devices growing faster than our ability to regulate it
    • UL developing certification for medical devices
    • traceability for car parts
  • John Marinho, Vice President Cybersecurity and Technology, CTIA
    • industry constantly evolving global standards — US can’t be isolated.
    • cybersecurity with IoT must be 24/7. CTIA created an IoT working group, meets every two weeks online.
    • believe in public/private partnerships, rather than just regulatory.

Session 9: Meeting the Short and Long-Term Connectivity Requirements of IoT – Approaches and Technologies

  •  Andreas Geiss, Head of Unit ‘Spectrum Policy’, DG CONNECT, European Commission
    • freeing up a lot of spectrum, service neutral
    • unlicensed spectrum, esp. for short-range devices. New frequency bands. New medical device bands. 
    • trying to work with regulators globally to allow for globally-usable devices.
  • Geoff Mulligan, Chairman, LoRa Alliance; Former Presidential Innovation Fellow, The White House
    • wireless tradeoffs: choose two — low power/long distance/high speed.
    • not licensed vs. unlicensed spectrum. Mix of many options, based on open standards, all based on TCP/IP
    • LPWANs:
      • low power wide area networks
      • battery operated
      • long range
      • low cost
      • couple well with satellite networks
    • LoRaWAN
      • LPWAN based on LoRa Radio
      • unlicensed band
      • open standards base
      • openly available
      • open business model
      • low capex and opex could covered entire country for $120M in South Korea
      • IoT is evolutionary, not revolutionary — don’t want to separate it from other aspects of Internet
  • Jeffrey Yan, Director, Technology Policy, Microsoft
    • at Microsoft they see it as critical for a wide range of global issues, including agriculture.
  • Charity Weeden, Senior Director of Policy, Satellite Industry Association
    • IoT critical during disasters
    • total architecture needs to be seamless, everywhere.
  • Andrew Hudson, Head of Technology Policy, GSMA
    • must have secure, scalable networks

Session 10: IoT Data-Ownership and Licencing – Who Owns the Data?

  • Stacey Gray, Policy Lead IoT, Future Privacy Forum 
    • consumer privacy right place to begin.
    • need “rights based” approach to IoT data
    • at this point, have to show y0u have been actually harmed by release of data before you can sue.
  • Patrick Parodi, Founder, The Wireless Registry
    • focus on identity
    • who owns SSID identities? How do you create an identity for things?
  • Mark Eichorn, Assistant Director, Division of Privacy and Identity Protection, Federal Trade Commission 
    • cases involving lead generators for payday loan. Reselling personal financial info.
  • Susan Allen, Attorney-Advisor, Office of Policy and International Affairs, United States Patent & Trademark Office 
    • focusing on copyright.
    • stakeholders have different rights based on roles
  • Vince Jesaitis, Director, US Public Affairs, ARM
    • who owns data depends on what it is. Health data very tough standards. Financial data much more loose.
    • data shouldn’t be treated differently if it comes from a phone or a browser.
    • industrial side: autonomous vehicle data pretty well regulated.  Pending legislation dealing with smart cities emphasis open data.
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Human Side of IoT: Local Startup Empowers Forgotten Shop Floor Workers!

Posted on 13th September 2017 in 3-D printing, data, design, Essential Truths, Internet of Things, M2M, management, manufacturing, sensors

Let’s not forget: human workers can and must still pay a role in the IoT!

Sure, the vast majority of IoT focus is on large-scale precision and automated manufacturing (Industrie 4.0 as it is known in Germany, or the Industrial Internet here). However, an ingenious local startup, Tulip, is bringing IoT tools to the workbench and shop floor, empowering individual industrial engineers to create no-code, low-code apps that can really revolutionize things in the factory.  Yes, many jobs will be replaced by IoT tech, but with Tulip, others will be “enabled” — workers will still be there to make decisions, and they’ll be empowered as never before.

Um, I’m thinking superhuman factory Transformers, LOL!

The Tulip IoT gateway allows anyone to add sensors, tools, cameras and even “pick to light bins” (never heard that bit of shop lingo, but they looked cool in video) to the work station, without writing a line of code, because of the company’s diverse drivers support factory floor devices. It claims to “fill the gap between rigid back-end manufacturing IT systems and the dynamic operations taking place on the shop floor.”

Rony Kubat, the young MIT grad who’s the company’s co-founder is on a mission “to revolutionize manufacturing software,” as he says, because people who actually have to play a hands-on roll in product design and production on  shop floor have been ignored in the IoT, and many processes such as training are still paper-based:

“Manufacturing software needs to evolve. Legacy applications neglect the human side of manufacturing and therefore suffer from low adoption. The use of custom, expensive-to-maintain, in-house solutions is rampant. The inability of existing solutions to address the needs of people on the shop floor is driving the proliferation of paper-based workflows and the use of word processing, spreadsheet and presentation applications as the mainstay of manufacturing operations. Tulip aims to change all this through our intuitive, people-centric platform. Our system makes it easy for manufacturers to connect hands-on work processes, machines and backend IT systems through flexible self-serve manufacturing apps”.

While automation in factory floors continues to grow, manufacturers often find their hands-on workforce left behind, using paper and legacy technology. Manufacturers are seeing an enormous need to empower their workforce with intuitive digital tools. Tulip is a solution to this problem. Front-line engineers create flexible shop-floor apps that connect workers, machines and existing IT systems. These apps guide shop-floor operations enabling real-time data collection and making that data useful to workers on factory floors. Tulip’s IoT gateway integrates the devices, sensors and machines on the shop floor, making it easy to monitor and interact with previously siloed data streams (you got me there: I HATE siloed data). The platform’s self-serve analytics engine lets manufacturers turn this data into actionable insights, supporting continuous process improvement.

The company has grown quickly, and has dozens of customers in fields as varied as medical devices, pharma, and aerospace. The results are dramatic and quite varied:

  • Quality: A Deloitte analysis of Tulip’s use at Jabil, a global contract manufacturer, documented 10+% production increases. It reduced quality issues in manual assembly by more than 10%. found production yield increased by more than 10 percent, and manual assembly quality issues were reduced by 60 percent in the initial four weeks of operation.
  • Training: Other customers reduced the amount of time to train new operators by  90 percent, in a highly complicated, customized and regulated biopharmaceutical training situation: “Previously, the only way to train new operators was to walk them repeatedly through all the steps with an experienced operator and a process engineer. Tulip quickly deployed its software along with IoT gateways for the machines and devices on the process, and managed to cut training time almost by half.”
  • Time to Market: They reduced a major athletic apparel maker’s time to market by 50% for hundreds of new product variations. That required constantly evaluating the impact of dozens of different quality drivers to isolate defects’ root causes — including both manual and automated platforms. Before Tulip, it could take weeks of analysis until a process was ready for production. According to the quality engineer on the project, “I used Tulip’s apps to communicate quality issues to upstream operators in real-time. This feedback loop enabled the operators to take immediate corrective action and prevent additional defects from occurring.”

Similar to my friends at Mendix, the no-code/low-code aspect of Tulip’s Manufacturing App Platform lets process engineers without programming backgrounds create shop floor apps through interactive step-by-step work instructions. “The apps give you access through our cloud to an abundance of information and real-time analytics that can help you measure and fine-tune your manufacturing operations,” Tulip Co-Founder Natan Linder says (the whiz-kid is also chairman of 3-D printer startup Formlabs). 

Linder looked at analytics apps that let users create apps through simple tools and thought why not provide the same kind of tools for training technicians on standard operating procedures or for building product or tracking quality defects? “This is a self-service tool that a process or quality engineer can use to build apps. They can create sophisticated workflows without writing code…. Our cloud authoring environment basically allows you to just drag and drop and connect all the different faucets and links to create a sophisticated app in minutes, and deploy it to the floor, without writing code,” he says. Tulip enables sharing appropriate real-time analytics with each team member no matter where they are and to set up personal alerts for the data that’s relevant to each.

IMHO, this is a perfect example of my IoT “Essential Truth” of “empowering every worker with real-time data.”  Rather than senior management parceling out (as they saw fit) the little amount of historical data that was available in the past, now workers can share (critical verb) that data instantly and combine it with the horse sense that can only be gained by those actually doing the work for years. Miracles will follow!

Writ large, the benefits of empowering shop floor workers are potentially huge.  According to the UK Telegraph, output can increase 8-9 %, while cutting costs 7-8%, cutting costs approximately 7-8 percent. The same research estimates that industrial companies “could see as much as a 300 basis point boost to their bottom line.”

Examples of the relevant shop-floor analytics include:

  • “Show real-time metrics from the shop floor
  • Report trends in your operations
  • Send customized alerts based on user defined triggers
  • Inform key stakeholders with relevant data”

IDC Analyst John Santagate neatly sums up the argument for empowering workers through the IoT thusly:

“With all of the talk and concern around the risk of losing the human element in manufacturing, due to the increasing use of robotics, it is refreshing to see a company focus on improving the work that is still done by human hands.  We typically hear the value proposition of deploying robots and automation of improvements to efficiency, quality, and consistency.  But what if you could achieve these improvements to your manufacturing process by simply applying analytics and technology to the human effort?  This is exactly what they are working on at Tulip.  

“Data analytics is typically thought about at the machine level. Manufacturers measure things such as throughput, efficiency, and quality by applying sensors to their manufacturing equipment, capturing the data signals, and conducting analytics.  The analytics provide an understanding of the health of the manufacturing process and enable them to make any necessary changes to improve the process.  Often, such efforts are top down driven.  Management drives these projects in order to improve the performance of the business.  An alternative approach is to enable the production floor to proactively identify improvement opportunities and take action, a bottom-up approach. For this self-service approach to succeed shop-floor engineers need a flexible platform such as Tulip’s, that allows them to replace paper-based processes with technology and build the applications that enable them to manage hands-on processes.  The real time analytics and visibility of hands-on manufacturing processes from Tulip’s platform puts the opportunity to identify improvement opportunities directly in the hands of people engaged in the work cells.

“Digital transformation in manufacturing is about leveraging advanced digital technology to improve how a company operates.  But, as the manufacturing industry focuses on digital transformation it must not forget the value of the human element.  Indeed, we don’t often think about digital transformation in relation to human effort, but this is exactly the sort of thinking that can deliver some of the early wins in digital transformation. “ 

Well said — and thanks to Tulip for filling a critical and often overlooked aspect of the IoT!

I’m reminded of my old friend Steve Clay-Young, who managed the BAC’s shop in Boston, and first alerted me to the “National Home- workshop Guild” which Popular Science started in the Depression and then played a critical part in the war effort. Craftsmen who belonged all got plans and turned out quality products on their home lathes.  I can definitely see a rebirth of the concept as the cost of 3-D printers from Kubat’s other startup, Formlabs drops, and we can have the kind of home (or at least locally-based production that Eric Drexler dreamed of in his great Engines of Creation (which threw in another transformational production technology, nanotech). 

I’m clearing space in my own workshop so I can begin production on IoT/nanotech/3-D printed products. Move over, GE.

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OtoSense: the next level in sound-based IoT

Posted on 3rd August 2017 in automotive, data, Internet of Things, maintenance, mobile, sensors, transportation

It sounds (pardon the pun) as if the IoT may really be taking off as an important diagnostic repair tool.

I wrote a while ago about the Auguscope, which represents a great way to begin an incremental approach to the IoT because it’s a hand-held device to monitor equipment’s sounds and diagnose possible problems based on abnormalities.

Now NPR reports on a local (Cambridge) firm, OtoSense, that is expanding on this concept on the software end. Its tagline is “First software platform turning real-time machine sounds and vibrations into actionable meaning at the edge.”

Love the platform’s origins: it grows out of founder Sebastien Christian’s research on deafness (as I wrote in my earlier post, I view suddenly being able to interpret things’ sounds as a variation on how the IoT eliminates the “Collective Blindness”  that I’ve used to describe our past inability to monitor things before the IoT’s advent):

“[Christian} … is a quantum physicist and neuroscientist who spent much of his career studying deaf children. He modeled how human hearing works. And then he realized, hey, I could use this model to help other deaf things, like, say, almost all machines.”

(aside: I see this as another important application of my favorite IoT question: learning to automatically ask “who else can use this data?” How does that apply to YOUR work? But I digress).

According to Technology Review, the company is concentrating primarily on analyzing car sounds from IoT detectors on the vehicle at this point (working with a number of car manufacturers) although they believe the concept can be applied to a wide range of sound-emitting machinery:

“… OtoSense is working with major automakers on software that could give cars their own sense of hearing to diagnose themselves before any problem gets too expensive. The technology could also help human-driven and automated vehicles stay safe, for example by listening for emergency sirens or sounds indicating road surface quality.

OtoSense has developed machine-learning software that can be trained to identify specific noises, including subtle changes in an engine or a vehicle’s brakes. French automaker PSA Group, owner of brands including Citroen and Peugeot, is testing a version of the software trained using thousands of sounds from its different vehicle models.

Under a project dubbed AudioHound, OtoSense has developed a prototype tablet app that a technician or even car owner could use to record audio for automated diagnosis, says Guillaume Catusseau, who works on vehicle noise in PSA’s R&D department.”

According to NPR, the company is working to apply the same approach to a wide range of other types of machines, from assembly lines to DIY drills. As always with IoT data, handling massive amounts of data will be a challenge, so they will emphasize edge processing.

OtoSense has a “design factory” on the site, where potential customers answer a variety of questions about the sounds they must monitor (such as whether the software will be used indoors or out, whether it is to detect anomalies, etc. that will allow the company to choose the appropriate version of the program.

TechCrunch did a great article on the concept, which underscores really making sound detection precise will take a lot of time and refinement, in part because of the fact that — guess what — sounds from a variety of sources are often mingled, so the relevant ones must be determined and isolated:

“We have loads of audio data, but lack critical labels. In the case of deep learning models, ‘black box’ problems make it hard to determine why an acoustical anomaly was flagged in the first place. We are still working the kinks out of real-time machine learning at the edge. And sounds often come packaged with more noise than signal, limiting the features that can be extracted from audio data.”

In part, as with other forms of pattern recognition such as voice, this is because it will require accumulating huge data files:

“Behind many of the greatest breakthroughs in machine learning lies a painstakingly assembled dataset.ImageNet for object recognition and things like the Linguistic Data Consortium and GOOG-411 in the case of speech recognition. But finding an adequate dataset to juxtapose the sound of a car-door shutting and a bedroom-door shutting is quite challenging.

“’Deep learning can do a lot if you build the model correctly, you just need a lot of machine data,’ says Scott Stephenson, CEO of Deepgram, a startup helping companies search through their audio data. ‘Speech recognition 15 years ago wasn’t that great without datasets.’

“Crowdsourced labeling of dogs and cats on Amazon Mechanical Turk is one thing. Collecting 100,000 sounds of ball bearings and labeling the loose ones is something entirely different.

“And while these problems plague even single-purpose acoustical classifiers, the holy grail of the space is a generalizable tool for identifying all sounds, not simply building a model to differentiate the sounds of those doors.

…”A lack of source separation can further complicate matters. This is one that even humans struggle with. If you’ve ever tried to pick out a single table conversation at a loud restaurant, you have an appreciation for how difficult it can be to make sense of overlapping sounds.

Bottom line: there’s still a lot of theoretical and product-specific testing that must be done before IoT-based sound detection will be an infallible diagnostic tool for predictive maintenance, but clearly there’s precedent for the concept, and the potential payoff are great!

 

LOL: as the NPR story pointed out, this science may owe its origins to two MIT grads of an earlier era, “Click” and “Clack” of Car Talk, who frequently got listeners to contribute their own hilarious descriptions of the sounds they heard from their malfunctioning cars.   BRTTTTphssssBRTTTT…..

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#IoT Sensor Breakthroughs When Lives Are On the Line!

Posted on 7th July 2017 in agriculture, data, defense, energy, Homeland Security, Internet of Things, M2M, maintenance, mobile, security, sensors, smart cities, US government

One of my unchanging principles is always to look to situations where there’s a lot at stake — especially human lives — for breakthroughs in difficult issues.

Exhibit A of this principle for the IoT is sensor design, where needing to frequently service or recharge critical sensors that detect battlefield conditions can put soldiers’ lives at stake (yes, as long-time readers know, this is particularly of interest to me because my Army officer son was wounded in Iraq).

FedTech reports encouraging research at DARPA on how to create sensors that have ultra-low power requirements, can lie dormant for long periods of time and yet are exquisitely sensitive to critical changes in conditions (such as vehicle or troop movements) that might put soldiers at risk in battlefield conditions.

The  N-ZERO (Near Zero RF and Power Operations)  program is a three-year initiative to create new, low-energy battlefield sensors, particularly for use at forward operating bases where conditions can change quickly and soldiers are constantly at risk — especially if they have to service the sensors:

“State-of-the-art military sensors rely on “active electronics” to detect vibration, light, sound or other signals for situational awareness and to inform tactical planning and action. That means the sensors constantly consume power, with much of that power spent processing what often turns out to be irrelevant data. This power consumption limits sensors’ useful lifetimes to a few weeks or months with even the best batteries and has slowed the development of new sensor technologies and capabilities. The chronic need to service or redeploy power-depleted sensors is not only costly and time-consuming but also increases warfighter exposure to danger.”

…. (the project has) the goal of developing the technological foundation for persistent, event-driven sensing capabilities in which the sensor can remain dormant, with near-zero power consumption, until awakened by an external trigger or stimulus. Examples of relevant stimuli are acoustic signatures of particular vehicle types or radio signatures of specific communications protocols. If successful, the program could extend the lifetime of remotely deployed communications and environmental sensors—also known as unattended ground sensors (UGS)—from weeks or months to years.”

A key goal is a 20-fold battery size reduction while still having the sensor last longer.

What cost-conscious pipeline operators, large ag business or “smart city” transportation director wouldn’t be interested in that kind of product as well?

According to Signal, the three-phase project is ahead of its targets. In the first part, which ended in December, the DARPA team created “zero-power receivers that can detect very weak signals — less than 70 decibel-milliwatt radio-frequency (RF) transmissions, a measure that is better than originally expected.” This is critical to the military (and would have huge benefits to business as well, since monitoring frequently must be 24/7 but reporting of background data  (vs. significant changes) would both deplete batteries while requiring processing of huge volumes of meaningless data). Accordingly, a key goal would be to create “… radio receivers that are continuously alert for friendly radio transmissions, but with near zero power consumption when transmissions are not present.” A target is  “exploitation of the energy in the signal signature itself to detect and discriminate the events of interest while rejecting noise and interference. This requires the development of passive or event-powered sensors and signal-processing circuitry. The successful development of these techniques and components could enable deployments of sensors that can remain “off” (that is, in a state that does not consume battery power), yet alert for detecting signatures of interest, resulting in greatly extended durations of operation.”

The “exploitation of .. energy in the signal signature itself sounds reminiscent of the University of Washington research I’ve reported in the past that would harness ambient back-scatter to allow battery-less wireless transmission, another key potential advance in IoT sensor networks.

The following phrases of N-ZERO will each take a year.

Let’s hope that the project is an overall success, and that the end products will also be commercialized. I’ve always felt sensor cost and power needs were potential IoT Achilles’ heels, so that would be a major boost!

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