The essays, articles, and links below are about the Internet of Things (IoT), it's origins, challenges, and opportunities to combine new business models, technology innovation, and customer-centric design approaches to help businesses make better decisions, allow people to do more with less, and make the world a better place.
I was honored to deliver a keynote presentation last week on behalf of Exosite at Sensors Expo in San Jose on how Conway's Law (systems mimic the organizations that produce them) leads to the inevitable fragmentation of IoT solutions, and how the "Inverted Conway Maneuver" (deploying technology that mimics the way you want your future organization to behave) is key to solving people problems through the intelligent application of IoT technology.
Honored to be published in Forbes this week on how organizations can build an IoT competency.
The Road To Unconscious Competence
In the field of psychology, the “conscious competence” learning model describes how individuals move from incompetence to competence in a certain subject area as they move from unconscious incompetence (naiveté about the competency deficit) to conscious incompetence (acknowledgment that there is competency deficit) to conscious competence (demonstrated competency through a concerted effort) to unconscious competence (competency as second nature).
This same model of “conscious competence” is true for organizations faced with the task of digital transformation through smart connected products, a visual depiction of which appears here.
There is no shortcut to mastery. Identification of gaps followed by planning, execution, growth and institutionalization of process and behavior are necessary waypoints on the journey to IoT competency.
The Internet of Things (IoT) represents new opportunities for manufacturers to capitalize on the value of data for their business. One of those opportunities is through leveraging an approach called machine learning, which is a branch of artificial intelligence that enables machines (or virtual representations of machines in the cloud) to learn new behaviors based on their external environments, internal health, and changing inputs. However, in order for machine learning to work, humans must be able to grok the context of how the machine data is collected, aggregated, and consumed.
I was featured in a piece on Forbes this week on cyberphysical security and how to keep IoT devices and data safe. It's hard to say something meaningful in just a few sentences, but here's what they used:
Consumers should understand the crucial role they play in cybersecurity, especially in regard to IoT devices, which have become increasingly accessible and vulnerable to hacking incidents. Consumers can make great strides in protecting themselves by using devices from reputable manufacturers, and protecting sensitive information like passwords and login credentials.
I was honored to be published this week in a piece on IoT Agenda on how the Internet of Things is changing the way that organizations think about innovation:
Innovation programs are historically the vehicle that protects against internal stagnation and external irrelevance. However, the larger an organization gets, the more difficult it becomes to innovate outside of historical core competencies and market-facing product lines, both of which are common with IoT.
I was honored this week to be interviewed by Forbes on a cyber security Q&A piece on how to mitigate DDoS attacks:
Although total victory over hackers may be impossible, we can combat their efforts via a balanced approach that focuses as much on mitigating exploits as on preventing them. Develop a security strategy that can only be beaten by physical attacks, limits the scope of attacks to individual devices, and secures data at each step in the pipeline based on whether it is at rest, in motion, or in use.
"The road to advanced analytics and machine learning starts with basic connectivity and data collection. This journey includes pinpointing the questions that need to be answered with data analysis, identifying the data needed to answer those questions, and putting processes in place to gather the correct type and amount of that data to properly support machine learning."
Also, Kurt Dykema was interviewed in the same piece, saying that the first step is to start with an internal project based on a passion to solve an interesting problem:
Find an interesting problem that the team wants to crack, and let them develop their skills in machine learning while they work on a problem they are passionate about.`
Critical observations accounted for a massive portion of the 43-page report, but it ultimately centered on the inability of IoT device manufacturers and lack of incentive to adapt to new security challenges.
Inability and lack of incentive are two aspects of security for manufacturers that I'm sure will change significantly in 2017.
I’ve seen companies hire data scientists to try and figure out what’s going on with their data, but they haven’t captured enough data for the data scientist to actually do anything, and the data scientists don’t understand the machines well enough to know how to instrument them to get the answers that they want. So it turns into this cycle of inefficiency where if companies don’t get the first part right – which is basic connectivity, adding sensors to machines, getting data flowing, and learning to collect data on failures – if they don’t do that first they won’t ever be successful adding data scientists to their teams and driving true operational efficiency.
I'm honored to be participating this year at the 2016 Minnesota Water Technology Summit at the US Bank Stadium on the topic IoT and Water Management along with Tom Arata (Ecolab), Pat Cardiff (Grande Cheese), John Dustman (Summit Envirosolutions), and Jen Nowlin (Accredent).
ABSTRACT: Internet of Things (IoT) technology is rapidly infiltrating into water systems, products and infrastructure to enhance monitoring capabilities, process efficiency and customer service across the entire water industry. This panel session will bring technology, business, manufacturing and engineering experts together to present and discuss water solutions that have been implemented and are scalable today, that will be leading water management best practices for the future.
Specifically, I'll be presenting two use cases on how the Internet of Things (IoT) is enabling intelligent remote monitoring of industrial water usage and treatment in factories as well as remote monitoring of water maker performance on commercial shipping vessels and luxury yachts.
Devices all around us are becoming connected to the Internet. A quick search online turns up a multitude of wild predictions. One thing is for certain: by 2020, there are going to be a lot of devices connected to the Internet.
I just released Dapper, a publishing tool for static websites. Dapper is a simple but powerful static website generator written in Perl. Dapper makes it easy to develop locally and deploy your site to Amazon S3 directly. It works great for corporate, portfolio, or personal websites and blogs.
The bounding condition in deploying the Internet of Things (IoT) is not going to be the deployment of devices but rather the management and analysis of the data coming off those devices. If you are interested in making use of the IoT, that’s what you need to be working on: Data Engineering.
I disagree that the deployment of IoT is "not a problem". However, it's true that the problem of dealing with large unstructured data streams is where the true dragons and opportunities are.
From the IPSO (Internet Protocol for Smart Objects) Alliance, the following paper makes the case for why IP will rule when it comes to the proliferation of smart objects. Adam Dunkels, PhD, Senior Scientist, Swedish Institute of Computer Science and JP Vasseur, Distinguished Engineer, Cisco Systems:
IP provides standardized, lightweight, and platform-independent network access to smart objects and other embedded networked devices. The use of IP makes devices accessible from anywhere and from anything; general-purpose PC computers, cell phones, PDAs as well as database servers and other automated equipment such as a temperature sensor or a light bulb.
The FIDO Alliance plans to change the nature of authentication by developing specifications that define an open, scalable, interoperable set of mechanisms that supplant reliance on passwords to securely authenticate users of online services.
Trading simplicity (no passwords) can at times come at the cost of high security. However, I like the line of thinking that makes simplifying assumptions such as if a person is near to a device (physical proximity), they are very likely authorized to provision and use that device.
Siemens on the future of self-organizing factories:
In smart factories, communities of machines will organize themselves, supply chaings will automatically coordinate with one another, and unfinished products will send the data needed for their processing to the machines that will turn them into merchandise.
The differences between the IIoT and IoT are not just a matter of slight degree or semantics. If your Fitbit or Nest device fails, it might be inconvenient. But if a train braking system fails, it could be a matter of life and death.
Not all IIoT applications are a matter of life and death, but it's true that industrial requirements for reliability, safety, and security are usually more stringent than with consumer applications.
Pablo Valerio, writing for EE Times, quoting Adam Gould, Vice President of the Sensinode Business at ARM:
We need standards at the radio level, the security layer, and the data format level... For developers it is necessary that they know that those devices are going to be able to talk to each other and [that they] really focus on the application.
This industry is still just getting organized. I agree with Adam. The best is yet to come and the first step is connecting devices based on open standards like CoAP.
Dan Harmon, Sensing Business Development Director for Texas Instruments, writing for Electronic Design:
Many systems use thermistors or temperature sensors to monitor the temperature at key locations in the system. While this works fine, measuring the temperature means that we are monitoring a secondary or lagging indicator.
Interesting approach. The idea of measuring current rather than temperature for predicting thermal performance is a good one since it gets us closer to the root cause of head in embedded systems: power.
As more and more millenials join the workforce, they will expect industrial equipment to offer the ease of navigation and intuitive interface similar to the smartphones and tablets they have grown up with. In the era of ubiquitous sensors and miniaturized mobile computing, a hassle-free UX design and contextual awareness will be the glue binding together the Industrial Internet.
Ryan Daws, on the visceral reaction against Google's recent acquisition of Nest, and the emerging reality of pervasive computing in our lives:
Well I’m sorry to be blunt, but if you want this world you’re going to have to give up some privacy.
This issue has less to do with privacy than it does with trust. Nest owners that were previously happy, are now unhappy because of their distrust of the unknwn future of their private information with Google.
[...] today we’re launching the Wolfram Connected Devices Project—whose goal is to work with device manufacturers and the technical community to provide a definitive, curated, source of systematic knowledge about connected devices.
“In the world of texting and IMing … the default is to end just by stopping, with no punctuation mark at all,” Liberman wrote me. “In that situation, choosing to add a period also adds meaning because the reader(s) need to figure out why you did it. [...]
I'm still clinging to the virtues of properly-punctuated text messages, but it's admittedly a dying (dare I say grotesque?) art. I wonder what sort of punctuation we'll use to communicate internet-connected machine statuses 10 years from now. Is it time to finally canonize a set of standard irony and sarcasm marks, but for machines only?
Adrian McEwen, quoting from Donald Norman's classic, The Design of Every Day Things:
Affordances provide strong clues to the operations of things. Plates are for pushing. Knobs are for turning. Slots are for inserting things into. Balls are for throwing or bouncing. When affordances are taken advantage of, the user knows what to do just by looking: no picture, label, or instruction is required. Complex things may require explanation, but simple things should not. When simple things need pictures, labels, or instructions, the design has failed.
Adrian goes on to draw parallels to the design of internet-connected devices:
As adoption of the Internet of Things gathers pace, more and more of our cities, homes and environment will become suffused with technology. With these additional behaviours and capabilities will come additional complexity - something that successful designers of connected devices and services will need to counter.
By their very nature, many of the new capabilities bestowed upon objects will be hidden from sight or not immediately apparent from first glance, which makes intuitive design difficult. What are the affordances of digitally-enhanced objects?
How do we convey to the user of an object that it can communicate with the cloud? Or that this device is capable of short-range comminication such as RFID? What does it mean that a toy knows what the temperature is, or when it is shaken? How do you know if your local bus shelter is watching you or, possibly more importantly, why?
This idea of applying affordances to the design of internet-connected devices is a useful concept, especially when thinking of how to apply internet-connected affordances without violating existing physical ones.
The final tweak is a bit vague, but involves giving Bluetooth devices a way to talk directly to the internet by giving them some kind of dedicated channel, which could be used for IPv6 communications. But because Bluetooth is a low-power protocol, implementing IPv6 could require more battery power than is wise. So how the SIG or chipmakers add IPv6 compatibility to Bluetooth-power devices seems to need a bit more explanation and development from the chipmakers.
Applications that are based on reliable transport can be secured using TLS, but no compelling alternative exists for securing datagram-based applications. In this paper we present DTLS, a datagram-capable version of TLS.
Ran across this excellent paper today by Raza, Trabalza, and Voigt on the topic of compressing and fragmenting DTLS headers over 802.15.4 networks using 6LoWPAN for CoAP. From the abstract:
Real deployments of the IoT require security. CoAP is being standardized as an application layer protocol for the Internet of Things (IoT). CoAP proposes to use DTLS to provide end-to-end security to protect the IoT. DTLS is a heavyweight protocol and its headers are too long to fit in a single IEEE802.15.4 MTU. 6LoWPAN provides header compression mechanisms to reduce the size of upper layer headers. 6LoWPAN header compression mechanisms can be used to compress the security headers as well. In this paper we propose 6LoWPAN header compression for DTLS. We link our compressed DTLS with the 6LoWPAN standard using standardized mechanisms.
CoAP requires DTLS, so that's not new. However, compressing and fragmenting DTLS such that it can be transported using 6LoWPAN is really neat. IoT needs more security-based solutions like this for resource-constrained sensing applications.