Liveblogging from Internet of Things Global Summit

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.

Human Side of IoT: Local Startup Empowers Forgotten Shop Floor Workers!

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.

OtoSense: the next level in sound-based IoT

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…..

#IoT Sensor Breakthroughs When Lives Are On the Line!

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!

IoT: LiveBlogging PTC’s LiveWorx

Got here a little late for CEO Jim Heppelman’s keynote, so here goes!

  • Vuforia: digital twin gives you everything needed for merging digital “decorations” on the physical object
  • Unique perspective: AR takes digital back to the physical. Can understand & make better decisions.
  • Virtual reality would allow much of the same. Add in 3-D printing, etc.
  • “IoT is PLM.” Says PTC might be only company prepared to do both.
  • Says their logo captures the merger of digital and physical.
  • Case studies: they partnered with Bosch’s Rexroth division. Cytropac built-in IoT connectivity–  used Creo. Full life-cycle management. Can identify patterns of usage, etc. Using PTC’s analytics capacity, machine learning analysis. Want to improve cooling efficiency (it was high at first). Model-based digital twin to monitor product in field, then design an upgrade. How can they increase cooling efficiency 30%??  Came up with new design to optimize water channel that they will build in using 3-D printing. Cool (literally!). 43% increase in cooling efficiency. The design change results in new recommendation engine that helps in sales. Replaced operating manual with 3-D that anyone can understand. (BTW: very cool stagecraft: Heppelmann walks around stage interviewing the Rexroth design team at their workstations).
  • Ooh: getting citizen developers involved!!!  Speeds process, flexibility. App shows how products are actually operating in the field. Lets sales be much more proactive in field. Reinventing CRM.  May no longer need a physical showroom — just put on the AR headset.
  • Connectivity between all assets. The digital twin is identical, not fraternal. Brings AR into factory. They can merge new manufacturing equipment with legacy ones that didn’t have connectivity.  ABB has cloud-based retrofit sensors. Thingworx can connect almost anything, makes Industry 4.0 possible. Amazing demo of a simulated 3-D disassembly and replacement.
  • Hmmm — closing graphic of his preso is a constantly rotating circular one. Anticipating my “circular company” talk on Wednesday????

Closing the Loop With Enterprise Change Management. Lewis Lawrence of Weatherford, services to petroleum industry:

  • former engineer. In charge of Weatherford’s Windchill installation (they also use Creo).
  • hard hit by the drop in gas prices
  • constant state of flux
  • 15 years of constant evolution
  • their mantra: design anywhere, build anywhere.
  • enterprise change — not just engineering.
  • hmmm: according to his graphics, their whole change process is linear. IMHO, that’s obsolete in era of constant change: must evolve to cyclical. Ponderous process…
  • collect data: anything can be added, if it’s latest

The IoT Can Even Help You Breathe Better: GCE Group’s Zen-O portable oxygen concentrator for people with respiratory problems (not actually launched yet):

  • InVMA has built IoT application using ThingWorx to let patients, docs and service providers carefully monitor data
  • GCE made radical change from their traditional business in gas control devices. Zen-O is in the consumer markets. They were very interested in connected products — especially since their key competitor launched one!
  • Goals: predictive maintenance, improved patient care, asset management, development insight.
  • Design process very collaborative, with many partners.

The Digital Value Chain: GE’s Manufacturing Journey. Robert Ibe, global IT Engineering Leader at GE Industrial Solutions:

  • supports Brilliant Factory program.
  • they design and manufacture electrical distribution equipment, 30 factories worldwide.
  • “wing-to-wing” integrated process
  • had a highly complex, obsolete legacy
  • started in 2014: they were still running really old CAD technology. 14 CAD repositories that didn’t talk to each other. 15 year old PLM software. No confidence in any of data they had.
  • They began change with PLM — that’s where the digital thread begins.  PLM is foundation for their transformation.
  • PLM misunderstood: use it to map out cohesive, cross-functional, model-based strategy. Highlight relevance of “design anywhere — manufacture anywhere.” Make PLM master of your domain. Make it critical to commercial & manufacturing. Advertise benefits & value.
  • Whole strategy based on CAD. Windchill heart of the process.
  • Rate of implementation faster than business can keep up with!
  • Process: implementation approach:
    • design systems integration
    • model-based design
    • digital thread
    • manufacturing productivity.
  • common enterprise PLM framework
  • within Windchill, can see entire “digital bill of documents.”
  • focused on becoming critical for supply chain.
  • total shift from their paper-based legacy.
  • integrated regulatory compliance with every step of design.

It’s Not Your Grandmother’s IoT: Blockchain and IoT Morph Into An Emerging Technology Powerhouse:

  • Example of claims for fair-traded coffee that I’ve used in past

Finding Business Value in IoT panel:

  • Bayer — been in IoT (injection devices for medicine) for 7 years.  Reduced a lot of parts inventory.
  • Remote control of vending machines replaces paper & pencil
  • Your team needs to evangelize for biz benefits of IoT
  • New Opportunities:
    • vision and language
    • interacting with physical world
    • problem solving.
  • Didn’t know!  Skype can do real-time translation.
  • Google Deep Mind team worked internally, cut energy costs at its server farms. 15% energy reduction.
  • Digital progress makes economic pie bigger, BUT  most people aren’t benefitting economicallly. Some may be worse off. “Great decoupling” — mushrooming economic gap. One reason is that tech affects different groups differently.
  • “Entirely possible to create inclusive prosperity” through tech!

 

WEDNESDAY

Delivering Smart City Solutions and an Open Citywide Platform to Accelerate Economic Growth and Promote New Solution Innovation, Scott McCarley, PTC:

  • $40 trillion potential benefits from smart cities
  • 1st example & starting point for many cities, is smart lightpoles. Major savings plus value added. Real benefit is building on that, with systems of systems (water, traffic, energy, etc.) — the systems don’t operate in isolation.
  • Future buildings may have built-in batteries to add to power supply. Water reclamation, etc.
  • Cities are focused on KPIs across all target markets.
  • Cornerstone systems for a city: power & grid, water/wastewater, building management, city services & infrastructure.
  • Leveraging ThingWorx to address these needs:
    • deploy out-of-box IoT solutions from a ThingWorx Solution Provider: All examples, include Aquamatix, DEPsys (grid), Sensus, All Traffic, Smoove (bike sharing).
    • leverage ThingWorx to rapidly develop new IoT solutions.
      connect to any device, rapidly develop applications, visually model systems, quickly develop new apps. Augmented reality will play a role!
    • create role-based dashboards:
      one for your own operations, another for city.
    • bring the platform to create a citywide platform.
      Sum of connected physical assets, communication networks, and smart city solutions.

Digital Supply Networks: The Smart Factory. Steven Shepley, Deloitte:

  • 3 types of systems: 1) foundational visualization solutions:  KPIs, etc. 2) advanced analytical solutions 3) cyber-physical solutions.
  • Priority smart factory solutions:
    • advanced planning (risk-adjusted MRP), dynamic sequencing, cross network.
    • value chain integration: signal-based customer/supplies integration, dynamic distribution routing/tracking, digital twin.
    • asset efficiency: predictive maintenance, real-time asset tracking intelligence, energy management
    • labor productivity: robotic and cognitive automation, augmented reality-driven efficiency, real-time safety monitoring
    • exponential tech: 3-D printing, drones, flexible robots.
  • How to be successful: think big, start small, scale fast
  • Act differently: multi-disciplinary teams,
  • sensors getting simpler, easier to connect & retrofit. National Connectors particularly good.

Global Smart Home, Smart Enterprise, and Smart Cities IoT Use Cases. Ken Herron, Unified InBox, Pte.

  • new focus on customer
  • H2M: human to machine communication is THE key to IoT success. Respect their interests.
  • Austin TX: “robot whisperer” — industrial robot company. Their robots aging out, getting out of tune, etc. Predictive analytics anticipates problems.
  • Stuttgart: connected cow — if one cow is getting sick, may spread to entire herd. Intervene.
  • Kuala Lumpur: building bot — things such as paper towel dispensers communicating with management.
  • London: Concierge chatbot — shopper browsing can chat with assistant on combining outfits.
  • Dubai: smart camera. Help find your car in mega-shopping center: read license plates, message the camera, it gives you map to the car.
  • Singapore: Shout — for natural disasters. Walks the person making the alert through process, confirms choices.
  • Stuttgart: Feinstaubalarm — occasional very bad airborne dust at certain times. Tells people with lung problems options, such as taking mass transit.
  • Singapore: Smart appliances — I always thought smart fridge was stupid, but in-fridge camera that lets you shoot a “shelfie” does make sense
  • Fulda Germany: smart clothing for military & police: full record of personal health at the moment. Neat!
  • Noida India — smart sneakers can automatically post your run results (see connection to my SmartAging concept)

Business Impact of IoT, Eric Schaeffer, Accenture:

  • Michelin delivery trucks totally reinvented, major fuel savings, other benefits.
  • manufacturing being deconstructed
  • smart, connected products are causing it
  • industrial companies must begin transformation today

Thingworx: Platform for Management Revolution. W. David Stephenson, Stephenson Strategies:

Here are key points from my presentation about how the IoT can allow radical transformation from linear & hierarchical companies to IoT-centric “circular companies” (my entire presentation can be found here):

  • The IoT can be the platform for dramatic management change that was impossible in the past.
  • Making this change requires an extraordinary shift in management thinking: from hierarchy to collaboration.
  • The results will be worth the effort: not only more efficiency & precision, but also new creativity, revenue streams, & customer loyalty. 
  • In short, it will allow total transformation!

Kickstarting America’s Digital Transformation. Aneesh Chopra & Nicholas Thompson!

  • on day one, Our President (not the buffoon) told Chopra he wanted default to be switch from closed to open government & data.
  • National Wireless Initiative: became law 1 yr. after it was introduced.  Nationwide interoperable, secure wireless system.
  • Obama wanted to harness power of Internet to grow the economy. Talked to CIO of P & G, who was focused on opening up the company to get ideas from outside.
  • Thompson big on open data, but he thinks a lot more now is closed, we’re going wrong way.
  • Interesting example of getting down cost of solar to $1 per installed watt!!
  • Thompson: growing feeling that technology isn’t serving us economically. Chopra: need to democratize the benefits.
  • Chopra talking about opening up Labor Dept. data to lead to creative job opportunities for underserved.

 

 

 

 

ThingWorx Analytics Video: microcosm of why IoT is so transformative!

I’ll speak at PTC’s LiveWorx lollapalooza later this month (ooh: act quickly and I can get you a $300 registration discount: use code EDUCATE300) on my IoT-based Circular Company meme, so I’ve been devouring everything I can about ThingWorx to prepare.

Came across a nifty 6:09 vid about one component of ThingWorx, its Analytics feature. It seems to me this video sez it all about both how you can both launch an incremental IoT strategy (a recent focus of mine, given my webinar with Mendix) that will begin to pay immediate benefits and can serve as the basis for more ambitious transformation later, especially because you’ll already have the analytical tools such as ThingWorx Analytics already installed.

What caught my eye was that Flowserve, the pump giant involved in this case, could retrofit existing pumps with retrofit sensors from National Instruments — crucial for two reasons:

  • you may have major investments in existing, durable machinery: hard to justify scrapping it just to take advantage of the IoT
  • relatively few high-end, high-cost machinery and devices have been redesigned from the ground up to incorporate IoT monitoring and operations.

Note the screen grab: each of these sensors takes 30,000 readings per second. How’s that for real-time data?  PTC refers to this as part of the “volume, velocity and variety challenge of data” with the IoT.

As a microcosm of the IoT’s benefits, this example shows how easy it is to use those massive amounts of data and how they can be used to improve understanding and performance.

There are three major components:

  • ThingWatcher:
    This is the most critical component, because it sifts through the incredible amount of data from the edge, learns what constitutes normal performance for that sensor (creating “pop-up learning flags”), and then monitors it future performance for anomalies and, as the sample video shows, delivers real-time alerts to users (without requiring human monitoring) so they can make adjustments and/or order repairs.  Finds anomalies from edge devices in real-time. Automatically observes and learns the normal state pattern for every device or sensor. It then monitors each for anomalies and delivers re- al-time alerts to end users.
  • ThingPredictor:
    For the all-important new function of predictive maintenance, two different types of ThingPredictor indicators pop up when if anomalies are detected, predicting how long it may be until failure, allowing plenty of time for less-costly, anticipatory repairs. Because the specific deviation is identified in advance, repair crews will have the needed part with them when needed, rather than having to make an additional trip back to pick up parts.

    If you ask for a standard predictive scoring you don’t specify which performance features to include and get a simple predictive score. However, you can specify several key features to evaluate and get a more detailed (and probably more helpful) answer. For example,  “if you indicate an important feature count of three, the causal scoring output will include the three most influential features for each record and the percentage weights of each feature’s influence on the score.”

  • ThingOptimizer:
    Finally, you can use “ThingOptimizer” to do some what-if calculations to decide which possible “levers,” as ThingWorx calls the key variables, could change the projections to either maximize a positive factor or minimize the negatives. “Prescriptive scoring results include both an original score (the score before any lever attributes are changed) and an optimized score (the score after optimal values are applied to the lever attributes). In addition, for each attribute identified in your data as a lever, original and optimal values are included in the prescriptive scoring results.” It sort of reminds me how the introduction of VisiCalc allowed users, for the first time, to play around with variables to see which would have the best results.
Best of all, as the video illustrates, ThingWorx Analytics would facilitate the kind of “Circular Company” I’ll address in my speech, because the exact same real-time data could simultaneously be used by operating personnel to fine tune operations and catch a problem in time for predictive maintenance, and by senior management to get an instant overview of how operations are going at all the installations. Same data, many uses.
Bottom line: a robust IoT platform could be the key to an incremental strategy to begin by improving daily operations and reducing maintenance problems, and also be the underpinning for more radical transformation as your IoT strategy becomes more advanced!  See you at LiveWorx!

Servitization With IoT: Weird Biz-Speak, But Sound Strategy

I love it when manufacturers stop selling things — and their revenues soar!

That’s one of the things I’ll cover on May 2nd  in”Define Your Breakout IoT” strategy, (sign-up) a webinar I’m doing with Mendix. I’ll outline an incremental approach to the IoT in which you can make some early, tentative steps (such as implementing Augury’s hand-held vibration sensor as a way to start predictive maintenance) and then, as you gain experience and increase savings and efficiency, plow the savings back into more dramatic transformation.

One example of the latter that I’ll detail in the webinar is one of my four “Essential Truths” of the IoT: rethink products. By that I meant not only reinventing products to be smart (especially by building in sensors so they can report their real-time status 24/7), but, having done that, exploring new ways to market them.  Or, as one graphic I’ll use in the presentation puts it, in mangled biz-speak, “servitization.”

              Hortilux bulbs

Most of the examples I’ve written about in that regard have been from major businesses, such as GE and Rolls-Royce jet turbines, that are now leased as services (with the price determined by thrust generated), but Mendix has a smaller, niche client that also successfully made the conversion: Hortilux, a manufacturer of grow lights for greenhouses.

The Hortilux decided to differentiate itself in an increasingly competitive grow light market by evolving from simply selling bulbs to instead providing a comprehensive continuing service that helps its customers optimize availability and lifetime of grow light systems, while cut energy cost.     

Using Mendix tools, they created Hortisensehttp://www.hortidaily.com/article/31774/Hortilux-launches-Hortisense-software-suite, a digital platform that monitors and safeguards various grow light processes in the greenhouse using sensors and PLCs. Software applications interpret the data and present valuable information to the grower anytime, anywhere, and on any device.

With Mendix, Hortilux created an application to collect sensor data on light, temperature, soil, weather and more. Now users can optimize plants’ photosynthesis, energy consumption, and greenhouse maintenance. Most ambitiously, it provides comprehensive “crop yield management:” 

  • Digital cultivation schedule
  • Light strategies based on plant physiology and life cycle
  • Automatic light adjustment based on predictive analytics (e.g. weather forecast, energy prices, produce prices)

The app even allows predictive maintenance, predicting bulbs’ life expectancy and notifying maintenance to replace them in time to avoid disruptions in operations.

In the days when we suffered from what I call “Collective Blindness,” when we lacked the tools to “see” inside products to m0nitor and perhaps fix them based on real-time operating data, it made sense to sell products and provide hit-or-miss maintenance when they broke down.

Now that we can monitor them 24/7 and get early enough warning to instead provide predictive maintenance, it makes equal sense to switching to marketing them as services, with mutual benefits including:

  • increased customer satisfaction because of less down-time
  • new revenues from selling customers services based on availability of the real-time data, which in turn allows them more operating precision
  • increased customer loyalty, because the customer is less likely to actually go on the open market and buy a competing product
  • the opportunity to improve operations through software upgrades to the product.

Servitization: ugly word, but smart strategy. Hope you’ll join us on the 2nd!

Sound’s emerging IoT role

Could sound be a critical IoT tool?

I’d fixated in the past on a metaphor I called “Collective Blindness,” as a way to explain how difficult it used to be to get accurate, real-time data about how a whole range of things, from tractors to your body, were actually working (or not) because we had no way to penetrate the surface of these objects as they were used. As a result, we created some not-so-great work-arounds to cope with this lack of information.

Then along came the IoT, and no more collective blindness!

Now I’m belatedly learning about some exciting efforts to use another sense, sound, for the IoT.  Most prominent, of course, is Amazon’s Alexa and her buddies (BTW, when I ask Siri if she knows Alexa, her response was an elusive “this is about you, not me,” LOL), but I’ve found a variety of start-ups pursuing quite different aspects of sound. They nicely illustrate the variety of ways sound might be used.

technician using Auguscope to detect   sound irregularities in machinery

First is Augury.

What I particularly love about their device and accompanying smartphone app it is that they are just about the lowest-cost, easiest-to-use, rapid payback industrial IoT devices I can think of.

That makes them a great choice to begin an incremental approach to the IoT, testing the waters by some measures that can be implemented quickly, pay rapid bottom-line benefits and therefore may lure skeptical senior management who might then be willing to then try bolder measures   (this incremental approach was what I outlined in my Managing the Internet of Things Revolution e-guide for SAP, and I’ll be doing a webinar on the approach in April with Mendix, which makes a nifty no-code, low-code tool).

Instead of requiring built-in sensors, an Auguscope is a hand-held device that plant personnel can carry anywhere in the building it’s needed to analyze how the HVAC system is working. A magnetic sensor temporarily attaches to the machine and the data flows from the Auguscope to the cloud where it is analyzed to see if the sound is deviating from pre-recorded normal sounds, indicating maintenance is needed. Consistent with other IoT products that are marketed as services instead of sold, it uses a “Diagnostics as a Service” model, so there are no up-front costs and customers pay as they go. The company hopes that the technology will eventually be built into household appliances such as washers and dryers.

Presenso is the second company using sound to enable predictive maintenance.  It is sophisticated cloud-based software that takes data from a wide range of already-installed sensors and interprets any kind of data: sound, temperature, voltage, etc.  It builds a model of the machine’s normal operating data and then creates visualizations when the data varies from the norm. Presenso’s power comes from combining artificial intelligence and big data.

Finally, and most creative is Chirp (hmm: Chrome wouldn’t let me enter their site, which it said was insecure. Here’s the URL:www.chirp.io/ — try at your own risk…) , a UK company that transmits data using audio clips that really sound like chirps. It’s amazing!  Check out this video of an app in India that uses sound to pay fares on the country’s version of Uber:


Another Chirp app is a godsend to all who forget Wi-Fi passwords: your phone “chirps” a secure access code, allowing you to join the network automatically.   The company has released iOS and Android versions.  As VentureBeat reported:

“Each chirp lasts a couple of seconds, and the receiving device “listens” for a handful of notes played quickly in a certain order, in a certain range, and at a certain speed. While there are other easy ways of sharing files and data in real-time, such as Bluetooth, Chirp doesn’t require devices to pair in advance, there is no need to set up an account, and it’s ultimately a much quicker way of sharing files.

“That said, with Chirp, the file itself isn’t sent peer-to-peer, and the data doesn’t actually travel directly via audio. Chirp merely decodes and encodes the file, with the associated sound serving as the delivery mechanism. A link is generated for the recipient(s) to access it on Chirp’s servers, but the process from sending to receiving is seamless and near-instant.”

In terms of IoT applications, it could also connect with physical objects (hmm: retailing uses??). The Chirp platform is so cool that I suspect it will be a global hit (the company says it’s already used in 90 countries).

So, I’ve had my senses opened: from now on, I’ll add voice and sound in general to the list of cool IoT attributes.  Because voice and sound are so ubiquitous, they really meet the late Mark Weiser’s test:  “the most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it.” What could be more woven into the fabric of everyday life than sound — and, potentially, more valuable?


BTW: let me put in a plug for another IoT voice product. During the past two months, I recorded 7 hours of my voice speaking a very strange mishmash of sentences drawn from, among others, Little Women, Jack London’s Call of the Wild, The Wizard of Oz, and The Velveteen Rabbit (I worried about the she-wolf sneaking up on Meg, LOL….). Using the algorithms developed for Alexa, the Vocal ID team will slice and dice my voice and create a natural sounding one for someone who cannot speak due to a birth defect or disease.  I hope you’ll join me in volunteering for this wonderful program.

IoT Intangibles: Increased Customer Loyalty

There are so many direct, quantifiable benefits of the IoT, such as increased quality (that 99.9988% quality rate at Siemens’s Amberg plant!) and precision, that we may forget there are also potential intangible benefits.

Most important of those is customer loyalty, brought about by dramatic shifts both in product designs and how they are marketed.

Much of this results from the IoT lifting the veil of Collective Blindness to which I’ve referred before: in particular, our prior inability to document how products were actually used once they left the loading dock. As I’ve speculated, that probably meant that manufacturers got deceptive information about how customers actually used products and their degree of satisfaction. The difficulty of getting feedback logically meant that those who most liked and most hated a product were over-represented: those who kinda liked it weren’t sufficiently motivated to take the extra steps to be heard.

Now, by contrast, product designers, marketers, and maintenance staffs can share (that critical verb from my Circular Company vision!) real-time data about how a product is actually operating in the field, often from a “digital twin” they can access right at their desks.

Why’s that important?

It can give them easy insights (especially if those different departments do access and discuss the data at the same time, each offering its own unique perspectives, on issues that will build customer loyalty:

  • what new features can we add that will keep them happy?
  • can we offer upgrades such as new operating software (such as the Tesla software that was automatically installed in every single car and avoided a recall) that will provide better customer experiences and keep the product fresh?
  • what possible maintenance problems can we spot in their earliest stages, so we can put “predictive maintenance” services into play at minimal cost and bother to the customer?

I got interested in this issue of product design and customer loyalty while consulting for IBM in the 9o’s, when it introduced the IBM PS 2E (for Energy & Environmental), a CES best-of-show winner in part because of its snap-together modular design. While today’s thin-profile-at-all-costs PC and laptop designs have made user-friendly upgrades a distant memory, one of the things that appealed to me about this design was the realization that if you could keep users satisfied that they were on top of  new developments by incremental substitution of new modules, they’d be more loyal and less likely to explore other providers.

In the same vein, as GE has found, the rapid feedback can dramatically speed upgrades and new features. That’s important for loyalty: if you maintain a continuing interaction with the customer and anticipate their demands for new features, they’ll have less reason to go on the open market and evaluate all of your competitors’ products when they do want to move up.

 

Equally important for customer loyalty is the new marketing options that the continuous flow of real-time operating data offer you. For a growing number of companies, that means they’re no longer selling products, but leasing them, with the price based on actual customer usage: if it ain’t bein’ used, it ain’t costing them anything and it ain’t bringing you any revenue!

Examples include:

  • jet turbines which, because of the real-time data flow, can be marketed on the basis of thrust generated: if it’s sitting on the ground, the leasee doesn’t pay.  The same real-time data flow allows the manufacturer to schedule predictive maintenance at the earliest sign of a problem, reducing both its cost and the impact on the customer.
  • Siemens’s Mobility Services, which add in features such as 3-D manufactured spare parts that speed maintenance and reduced costs, keeping the trains running.
  • Philips’s lighting services, which are billed on the basis of use, not sold.
  • SAP’s prototype smart vending machine, which (if you opt in) may offer you a special discount based on your past purchasing habits.

At its most extreme is Caterpillar’s Reman process, where the company takes back and remanufactures old products, giving them a new life — and creating new revenues — when competitors’ products are in the landfill.

Loyalty can also be a benefit of IoT strategies for manufacturers’ own operations as well. Remember that the technological obstacles to instant sharing of real-time data have been eliminted for the supply chain as well. If you choose to share it, your resupply programs can also be automatically triggered on a M2M basis, giving an inherent advantage to the domestic supplier who can get the needed part there in a few hours, versua the low-cost supplier abroad who may take weeks to reach your loading dock.

It may be harder to quantify than quality improvements or streamlined production through the IoT, but that doesn’t mean that dependable revenue streams from loyal customers aren’t an important potential benefit as well.

Libelium: flexibility a key strategy for IoT startups

I’ve been fixated recently on venerable manufacturing firms such as 169-yr. old Siemens making the IoT switch.  Time to switch focus, and look at one of my fav pure-play IoT firms, Libelium.  I think Libelium proves that smart IoT firms must, above all, remain nimble and flexible,  by three interdependent strategies:

  • avoiding picking winners among communications protocols and other standards.
  • avoiding over-specialization.
  • partnering instead of going it alone.
Libelium CEO Alicia Asin

Libelium CEO Alicia Asin

If you aren’t familiar with Libelium, it’s a Spanish company that recently turned 10 (my, how time flies!) in a category littered with failures that had interesting concepts but didn’t survive. Bright, young, CEO Alicia Asin, one of my favorite IoT thought leaders (and do-ers!) was recently named best manager of the year in the Aragón region in Spain.  I sat down with her for a wide-ranging discussion when she recently visited the Hub of the Universe.

I’ve loved the company since its inception, particularly because it is active in so many sectors of the IoT, including logistics, industrial control, smart meters, home automation and a couple of my most favorite, agriculture (I have a weak spot for anything that combines “IoT” AND “precision”!) and smart cities.  I asked Asin why the company hadn’t picked one of those verticals as its sole focus: “it was too risky to choose one market. That’s still the same: the IoT is still so fragmented in various verticals.”

The best illustration of the company’s strategy in action is its Waspmote sensor platform, which it calls the “most complete Internet of Things platform in the market with worldwide certifications.” It can monitor up to 120 sensors to cover hundreds of IoT applications in the wide range of markets Libelium serves with this diversified strategy, ranging from the environment to “smart” parking.  The new versions of their sensors include actuators, to not simply report data, but also allow M2M control of devices such as irrigation valves, thermostats, illumination systems, motors and PLC’s. Equally important, because of the potentially high cost of having to replace the sensors, the new ones use extremely little power, so they can last        .

Equally important as the company’s refusal to limit itself to a single vertical market is its commitment to open systems and multiple communications protocols, including LoRaWAN, SIGFOX, ZigBee and 4G — a total of 16 radio technologies. It also provides both open source SDK and APIs.

Why?  As Asin told me:

 

“There is not going to be a standard. This (competiting standards and technology) is the new normal.

“I talk to some cities that want to become involved in smart cities, and they say we want to start working on this but we want to use the protocol that will be the winner.

“No one knows what will be the winner.

“We use things that are resilient. We install all the agents — if you aren’t happy with one, you just open the interface and change it. You don’t have to uninstall anything. What if one of these companies increases their prices to heaven, or you are not happy with the coverage, or the company disappears? We allow you to have all your options open.

“The problem is that this (not picking a standard) is a new message, and people don’t like to listen.  This is how we interpret the future.”

Libelium makes 110 different plug and play sensors (or as they call them, “Plug and Sense,” to detect a wide range of data from sources including gases, events, parking, energy use, agriculture, and water.  They claim the lowest power consumption in the industry, leading to longer life and lower maintenance and operating costs.

Finally, the company doesn’t try to do everything itself: Libelium has a large and growing partner network (or ecosystem, as it calls it — music to the ears of someone who believes in looking to nature for profitable business inspiration). Carrying the collaboration theme even farther, they’ve created an “IoT Marketplace,” where pre-assembled device combinations from Libelium and partners can be purchased to meet the specific needs of niches such as e-health,  vineyards, water quality, smart factories, and smart parking.  As the company says, “the lack of integrated solutions from hardware to application level is a barrier for fast adoption,” and the kits take away that barrier.

I can’t stress it enough: for IoT startups that aren’t totally focused on a single niche (a high-stakes strategy), Libelium offers a great model because of its flexibility, agnostic view of standards, diversification among a variety of niches, and eagerness to collaborate with other vendors.


BTW: Asin is particularly proud of the company’s newest offering, My Signals,which debuted in October and has already won several awards.  She told me that they hope the device will allow delivering Tier 1 medical care to billions of underserved people worldwide who live in rural areas with little access to hospitals.  It combines 15 different sensors measuring the most important body parameters that would ordinarily be measured in a hospital, including ECG, glucose, airflow, pulse, oxygen in

It combines 15 different sensors measuring the most important body parameters that would ordinarily be measured in a hospital, including ECG, glucose, airflow, pulse, blood oxygen, and blood pressure. The data is encrypted and sent to the Libelium Cloud in real-time to be visualized on the user’s private account.

It fits in a small suitcase and costs less than 1/100th the amount of a traditional Emergency Observation Unit.

The kit was created to make it possible for m-health developers to create prototypes cheaply and quickly.