Serendipity35 - Internet of Things

What Happened to the Internet of Things?

ronkowitz@gmail.com (Kenneth Ronkowitz) — Wed, 05 Mar 2025 12:59:00 +0000

IoT applications

After writing here about how the Internet and websites are not forever, I started looking at some old posts that perhaps should be deleted or updated. With 2200+ posts here since 2006, that seems like an overwhelming and unprofitable use of my time. Plus, maybe an old post has some historical value. But I do know that there are older posts that have links to things that just don't exist on the Internet anymore.

The last post I wrote here labeled "Internet of Things" (IoT) was in June 2021. IoT was on Gartner's trends list in 2012, and my first post about IoT here was in 2009, so I thought an new update was due.

When I wrote about this in 2014, there were around 10 billion connected devices. In 2024, the number has increased to over 30 billion devices, ranging from smart home gadgets (e.g., thermostats, speakers) to industrial machines and healthcare devices. Platforms like Amazon Alexa, Google Home, and Apple HomeKit provide hubs for connecting and controlling a range of IoT devices.

The past 10 years have seen the IoT landscape evolve from a collection of isolated devices to a more integrated, intelligent, and secure ecosystem. Advancements in connectivity, AI, edge computing, security, and standardization have made IoT more powerful, reliable, and accessible, with applications transforming industries, enhancing daily life, and reshaping how we interact with technology. The number of connected devices has skyrocketed, with billions of IoT devices now in use worldwide. This widespread connectivity has enabled smarter homes, cities, and industries.

IoT devices have become more user-friendly and accessible, with smart speakers, wearables, and home automation systems becoming commonplace in households. If you have a washing machine or dryer that reminds you via an app about its cycles. or a thermostat that knows when you are in rooms or on vacation, then IoT is in your home, whether you use that term or not.

Surveying the topic online turned up a good number of things that have pushed IoT forward or that IoT has pushed forward. Most recently, I would say that the 3 big things that have pushed IoT forward are 5G and advanced connectivity, the rise of edge computing, and AI and machine learning integration:

Technological improvements, such as the rollout of 5G networks, have greatly increased the speed and reliability of IoT connections. This has allowed for real-time data processing and more efficient communication between devices.

Many IoT devices now incorporate edge computing and AI to process data locally, reducing the reliance on cloud-based servers. This allows faster decision-making, less latency, and improved security by limiting the amount of data transmitted. IoT devices have increasingly incorporated AI and machine learning for predictive analytics and automation. This shift has allowed for smarter decision-making and automation in various industries, such as manufacturing (predictive maintenance), healthcare (patient monitoring), and agriculture (smart farming).

The integration of big data and advanced analytics has enabled more sophisticated insights from IoT data. This has led to better decision-making, predictive maintenance, and personalized user experiences.

One reason why I have heard less about IoT (and written less about it) is that it has expanded beyond consumer devices to industrial applications. I discovered a new term - Industrial Internet of Things (IIoT) that includes smart manufacturing, agriculture, healthcare, and transportation, improving efficiency and productivity.

There are also concerns that have emerged. As IoT devices proliferate, so have concerns about security. Advances in cybersecurity measures have been implemented to protect data and ensure the privacy of users. The IoT security landscape has seen new protocols and encryption standards being developed to protect against vulnerabilities, with an emphasis on device authentication and secure communication.

The rollout of 5G has enhanced IoT capabilities by providing faster, more reliable connections. This has enabled more efficient real-time data processing for smart cities, autonomous vehicles, and industrial IoT applications, which can now operate at a larger scale and with lower latency.

IoT devices are now able to use machine learning and AI to learn from user behavior and improve their performance. For example, smart thermostats can learn a household’s schedule and adjust settings automatically, while security cameras can differentiate between human and non-human motion.

Edge computing has allowed IoT devices to process data locally rather than relying solely on cloud-based servers. This reduces latency and bandwidth usage, making it especially beneficial for time-sensitive applications like healthcare monitoring, industrial automation, and smart grids.

Despite the growth, the IoT market faces challenges such as chipset supply constraints, economic uncertainties, and geopolitical conflicts

Synergy

ronkowitz@gmail.com (Kenneth Ronkowitz) — Tue, 29 Jun 2021 11:01:00 +0000

Synergy is one of those words that caught fire with the general public in the late 20th century, especially in tech-related fields. In general, it is taken to mean the interaction of two or more things (organizations, substances, products, fields, etc.) that produces a greater effect when combined than separately. For example, if two colleges work jointly on a project, or the way there was cooperation between some pharmaceutical researchers in developing the COVID-19 vaccines.

But the word synergy is not a recent addition to the language. It appeared in the mid 19th century mostly in the field of physiology concerning the interaction of organs. It comes from the Greek sunergos meaning "working together" which comes from sun- ‘together’ + ergon ‘work’.

It has been used in diverse ways. In Christian theology, it was said that salvation involves synergy between divine grace and human freedom. I received a wedding engagement announcement that talked about the synergy between the two people. (They do both work in tech fields.)

The informational synergies which can be applied also in media involve a compression of transmission, access and use of information’s time, the flows, circuits and means of handling information being based on a complementary, integrated, transparent and coordinated use of knowledge.[32]

Walt Disney is given as an example of pioneering synergistic marketing. Back in the 1930s, the company licensed dozens of firms the right to use the Mickey Mouse character in products and ads. These products helped advertise their films. This kind of marketing is still used in media. For example, Marvel films are not only promoted by the company and the film distributors but also through licensed toys, games and posters.

Shifting to tech, synergy can also be defined as the combination of human strengths and computer strengths. The use of robots and AI are clear synergies. If you read into information theory, you will find discussions of synergy when multiple sources of information taken together provide more information than the sum of the information provided by each source alone.

In education, synergy can be when schools and colleges, departments, disciplines, researchers,

Strong and Weak AI

ronkowitz@gmail.com (Kenneth Ronkowitz) — Mon, 25 May 2020 10:01:00 +0000

Image by Gerd Altmann from Pixabay

Ask several people to define artificial intelligence (AI) and you'll get several different definitions. If some of them are tech people and the others are just regular folks, the definitions will vary even more. Some might say that it means human-like robots. You might get the answer that it is the digital assistant on their countertop or inside their mobile device.

One way of differentiating AI that I don't often hear is by the two categories of weak AI and strong AI.

Weak AI (also known as “Narrow AI”) simulates intelligence. These technologies use algorithms and programmed responses and generally are made for a specific task. When you ask a device to turn on a light or what time it is or to find a channel on your TV, you're using weak AI. The device or software isn't doing any kind of "thinking" though the response might seem to be smart (as in many tasks on a smartphone). You are much more likely to encounter weak AI in your daily life.

Strong AI is closer to mimicking the human brain. At this point, we could say that strong AI is “thinking” and "learning" but I would keep those terms in quotation marks. Those definitions of strong AI might also include some discussion of technology that learns and grows over time which brings us to machine learning (ML), which I would consider a subset of AI.

ML algorithms are becoming more sophisticated and it might excite or frighten you as a user that they are getting to the point where they are learning and executing based on the data around them. This is called "unsupervised ML." That means that the AI does not need to be explicitly programmed. In the sci-fi nightmare scenario, the AI no longer needs humans. Of course that is not even close to true today as the AI requires humans to set up the programming, supply the hardware and its power. I don't fear the AI takeover in the near future.

But strong AI and ML can go through huge amounts of data that it is connected to and find useful patterns. Some of those are patterns and connections that itis unlikely that a human would find. Recently, you may have heard of the attempts to use AI to find a coronavirus vaccine. AI can do very tedious, data-heavy and time-intensive tasks in a much faster timeframe.

If you consider what your new smarter car is doing when it analyzes the road ahead, the lane lines, objects, your speed, the distance to the car ahead and hundreds or thousands of other factors, you see AI at work. Some of that is simpler weak AI, but more and more it is becoming stronger. Consider all the work being done on autonomous vehicles over the past two decades, much of which has found its way into vehicles that still have drivers.

Of course, cybersecurity and privacy become key issues when data is shared. You may feel more comfortable in allowing your thermostat to learn your habits or your car to learn about how you drive and where you drive than you are about letting the government know that same data. Discover the level of data we share online dong financial operations or even just our visiting sites, making purchases and our search history, and you'll find the level of paranoia rising. I may not know who you are reading this article, but I suspect someone else knows and is more interested in knowing than me.

Event-Based Internet

ronkowitz@gmail.com (Kenneth Ronkowitz) — Wed, 10 Jan 2018 14:33:00 +0000

Event-based Internet is going to be something you will hear more about this year. Though I had heard the term used, the first real application of it that I experienced was a game. But don't think this is all about fun and games. Look online and you will find examples of event-based Internet biosurveillance and event-based Internet robot teleoperation systems and other very sophisticated uses, especially connected to the Internet of Things (IoT).

What did more than a million people do this past Sunday night at 9pm ET? They tuned in on their mobile devices to HQ Trivia, a game show, on their phones.

For a few generations that have become used to time-shifting their viewing, this real-time game is a switch.

The HQ app has had early issues in scaling to the big numbers with game delays, video lag and times when the game just had to be rebooted. But it already has at least one imitator called "The Q" which looks almost identical in design, and imitation is supposed to be a form of flattery.

This 12-question trivia quiz has money prizes. Usually, the prize is $2000, but sometimes it jumps to $10 or $20K. But since there are multiple survivors of the 12 questions that win, the prizes are often less than $25 each.

Still, I see the show's potential (Is it actually a "show?") Business model? Sponsors, commercial breaks, sponsors and product placement in the questions, answers and banter in-between questions.

The bigger trend here is that this is a return to TV "appointment viewing." Advertisers like that and it only really occurs these days with sports, some news and award shows. (HQ pulled in its first audience of more than a million Sunday during the Golden Globe Awards, so...)

And is there some education connection in all this? Event-based Internet, like its TV equivalent, is engaging. Could it bring back "The Disconnected" learner?

I found a NASA report on "Lessons Learned from Real-Time, Event-Based Internet Science Communications." This report is focused on sharing science activities in real-time in order to involve and engage students and the public about science.

Event-based distributed systems are being used in areas such as enterprise management, information dissemination, finance,
environmental monitoring and geo-spatial systems.

Education has been "event-based" for hundreds of years. But learners have been time-shifting learning via distance education and especially via online learning for only a few decades. Event-based learning sounds a bit like hybrid or blended learning. But one difference is that learners are probably not going to tune in and be engaged with just a live lecture. Will it take a real event and maybe even gamification to get live learning?

In all my years teaching online, I have never been able to have all of a course's student attend a "live" session either because of time zone differences, work schedules or perhaps content that just wasn't compelling enough.

What will "Event-based Learning" look like?

Edge Computing

ronkowitz@gmail.com (Kenneth Ronkowitz) — Mon, 08 Jan 2018 12:45:00 +0000

I learned about edge computing a few years ago. It is a method of getting the most from data in a computing system by performing the data processing at the "edge" of the network. The edge is near the source of the data, not at a distance. By doing this, you reduce the communications bandwidth needed between sensors and a central datacenter. The analytics and knowledge generation are right at or near the source of the data.

The cloud, laptops, smartphones, tablets and sensors may be new things but the idea of decentralizing data processing is not. Remember the days of the mainframe computer?

The mainframe is/was a centralized approach to computing. All computing resources are at one location. That approach made sense once upon a time when computing resources were very expensive - and big. The first mainframe in 1943 weighed five tons and was 51 feet long. Mainframes allowed for centralized administration and optimized data storage on disc.

Access to the mainframe came via "dumb" terminals or thin clients that had no processing power. These terminals couldn't do any data processing, so all the data went to, was stored in, and was crunched at the centralized mainframe.

Much has changed. Yes, a mainframe approach is still used by businesses like credit card companies and airlines to send and display data via fairly dumb terminals. And it is costly. And slower. And when the centralized system goes down, all the clients go down. You have probably been in some location that couldn't process your order or or access your data because "our computers are down."

It turned out that you could even save money by setting up a decentralized, or “distributed,” client-server network. Processing is distributed between servers that provide a service and clients that request it. The client-server model needed PCs that could process data and perform calculations on their own in order to have applications to be decentralized.

Google Co-Founder Sergey Brin shows U.S. Secretary of State John Kerry the computers inside one of
Google's self-driving cars - a data center on wheels. June 23, 2016. [State Department photo/ Public Domain]

Add faster bandwidth and the cloud and a host of other technologies (wireless sensor networks, mobile data acquisition, mobile signature analysis, cooperative distributed peer-to-peer ad hoc networking and processing) and you can compute at the edge. Terms like local cloud/fog computing and grid/mesh computing, dew computing, mobile edge computing, cloudlets, distributed data storage and retrieval, autonomic self-healing networks, remote cloud services, augmented reality and more that I haven't encountered yet have all come into being.

Recently, I heard a podcast on "Smart Elevators & Self-Driving Cars Need More Computing Power" that got me thinking about the millions of objects (Internet of Things) connecting to the Internet now. Vehicles, elevators, hospital equipment, factory machines, appliances and a fast-growing list of things are making companies like Microsoft and GE put more computing resources at the edge of the network.

This is computer architecture for people not things. In 2017, there were about 8 billion devices connect to the net. It is expected that in 2020 that number will be 20 billion. Do you want the sensors in your car that are analyzing traffic and environmental data to be sending it to some centralized resource - or doing it in your car? Milliseconds matter in avoiding a crash. You need the processing to be done on the edge. Cars are "data centers on wheels."

Remember the early days of the space program? All the computing power was on Earth. You have no doubt heard the comparison that the iPhone in your pocket has hundreds or even thousands of times the computing power of the those early spacecraft. That was dangerous, but it was the only option. Now, much of the computing power is at the edge - even if the vehicle is also at the edge of our solar system. And things that are not as far off as outer space - like a remote oil pump - also need to compute at the edge rather than needing to connect at a distance to processing power.

Plan to spend more time in the future at the edge.

Monetizing Your Privacy

ronkowitz@gmail.com (Kenneth Ronkowitz) — Tue, 05 Dec 2017 13:40:00 +0000

Data is money. People are using your data to make money. What if you could sell, rather than give away, your private data? Is it possible that some day your data might be more valuable than the thing that is supplying your data?

John Ellis deals with big data and how it may change business models. He was Ford Motor Company’s global technologist and head of the Ford Developer Program, so cars are the starting place for the book, but beyond transportation, insurance, telecommunications, government and home building are all addressed. His book, The Zero Dollar Car: How the Revolution in Big Data will Change Your Life, is not as much about protecting our data as users, as it is about taking ownership of it. In essence, he is suggesting that users may be able to "sell" their data to companies (including data collectors such as Google) in exchange for free or reduced cost services or things.

I'm not convinced this will lead to a free/zero dollar car, but the idea is interesting. You are already allowing companies to use your data when you use a browser, shop at a website, use GPS on your phone or in a car device. The growth of the Internet of Things (IoT) means that your home thermostat, refrigerator, television and other devices are also supplying your personal data to companies. And many companies, Google, Apple and Amazon are prime examples, use your data to make money. Of course, this is also why Google can offer you free tools and services like Gmail, Documents etc.

Ellis talks about a car that pays for itself with your use and data, but the book could also be the Zero Dollar House or maybe an apartment. Big technology companies already profit from the sale of this kind of information. Shouldn't we have that option?

Duly noted: the data we supply also helps us. Your GPS or maps program uses your route and speed to calculate traffic patterns and reroute or notify you. The health data that your Apple watch or fitness band uploads can help you be healthier, and in aggregate it can help the general population too.

I remember years ago when Google began to predict flu outbreaks in geographic areas based on searches for flu-related terms. If all the cars on the road were Net-enabled and someone was monitoring the ambient temperature and their use of windshield wipers, what could be done with that data? What does an ambient temperature of 28 F degrees and heavy wiper use by cars in Buffalo, New York indicate? Snowstorm. Thousands or millions of roaming weather stations. And that data would be very useful to weather services and companies (like airlines and shipping companies) that rely on weather data - and are willing to pay for that data.

Am I saying that you should give up your privacy for money or services? No, but you should have that option - and the option to keep all your data private.

Machine Learning :: Human Learning

ronkowitz@gmail.com (Kenneth Ronkowitz) — Tue, 18 Jul 2017 13:08:00 +0000

Image by Erik Charlton, Flickr

AI - “artificial intelligence” - was introduced at a science conference at Dartmouth University in 1956. Back then it was a theory, but in the past few decade it has become something beyond theoretical. been less theory and more in practice than decades before.

The role of AI in education is still more theory than practice.

A goal in AI is to get machines to learn. I hesitate to say "think" but that is certainly a goal too. I am reading The Innovators: How a Group of Hackers, Geniuses, and Geeks Created the Digital Revolution currently and in that history there is a lot of discussion of the people trying to get machines to do more than just compute (calculate) but to learn from its experiences without requiring a human to program those changes. The classic example is the chess playing computer that gets better every time it wins or loses. Is that "learning?"

But has it had an impact on how you teach or how your students learn?

It may have been a mistake in the early days of AI and computers that we viewed the machine as being like the human brain. It is - and it isn't.

But neuroscientists are now finding that they can also discover more about human learning as a result of machine learning. An article on opencolleges.edu.au points to several interesting insights from the machine and human learning research that may play a role in AI in education.

One thing that became clear is that physical environment is something humans learn easier than machines. After a child has started walking or opened a few doors or drawers or climbed a few stairs, she learns how to do it. Show her a different door, drawer, or a spiral staircase and it doesn't make much of a difference. A robot equipped with some AI will have a much steeper learning curve to learn these simple things. It also has a poor sense of its "body." Just watch any videos online of humanoid robots trying to do those things and you'll see how difficult it is for a machine.

Then again, it takes a lot longer for humans to learn how to drive a car on a highway safely. And even when it is learned, our attention, or lack thereof, is a huge problem. AI in vehicles is learning how to drive fairly rapidly, and its attention is superior to human attention. Currently, it is still a fall back human error in mist cases, but that will certainly change in a decade or two. I learned to parallel park a car many years ago and I am still lousy at doing it. A car can do it better than me.

Although computers can do tasks they are programmed to do without any learning curve, for AI to work they need to learn by doing - much like humans. The article points out that AI systems that traced letters with robotic arms had an easier time recognizing diverse styles of handwriting and letters than visual-only systems.

AI means a machine gets better at a task the more it does it, and it can also apply that learning to similar but not identical situations. You can program a computer to play notes and play a series of notes as a song, but getting it to compose real music requires AI.

Humans also learn from shared experiences. A lot of the learning in a classroom comes from interactions between the teacher and students and student to student. This makes me feel pretty confident in the continued need for teachers in the learning process.

One day, I am sure that machines will communicate with each other and learn from each other. This may be part of the reason that some tech and learning luminaries like Elon Musk have fears about AI.

I would prefer my smart or autonomous vehicle to "talk" to other vehicles on the roads nearby and share information on traffic, obstructions and vehicles nearby with those quirky human drivers only.

AI built into learning systems, such as an online course, could guide the learning path and even anticipate problems and offer corrections to avoid them. Is that an AI "teacher" or the often-promoted "guide on the side?"

This year on the TV show Humans, one of the human couples goes for marriage counseling with a "synth" (robot). She may be a forerunner of a synth teacher.

The counselor (back to us) can read the husband's body language and knows he does not like talking to a synth marriage counselor.

Is Education Ready to Connect to the Internet of Things?

ronkowitz@gmail.com (Kenneth Ronkowitz) — Tue, 09 Aug 2016 12:37:00 +0000

I first encountered the term "Internet of Things" (IoT) in 2013. It is the idea that "things" (physical devices) would be connected in their own network(s). The talk was that things in your home, office and vehicles would be wirelessly connected because they were embedded with electronics, software, sensors, actuators, and network connectivity. Things would talk to things. Things would collect and exchange data.
Some of the early predictions seemed rather silly. Taking a tagged carton of milk out of the refrigerator and not putting it back would tell my food ordering device (such as an Amazon Echo) that I was out of milk. My empty Bluetooth coffee mug would tell the Keurig coffeemaker to make me another cup.
But the "smart home" - something that pre-dates the Internet - where the HVAC knew I was almost home and adjusted the temperature off the economical setting to my comfort zone and maybe put on the front light and started dinner, was rather appealing.
In 2014, the EDUCAUSE Learning Initiative (ELI) published its “7 Things You Should Know About the Internet of Things. The Internet of Things (and its annoying abbreviation of IoT) sounded rather ominous as I imagined them proliferating across our social and physical landscapes. The ELI report said “the IoT has its roots in industrial production, where machine-to-machine communication enabled the manufacture of complex items, but it is now expanding in the commercial realm, where small monitoring devices allow such things as ovens, cars, garage doors, and the human heartbeat to be checked from a computing device.”
Some of the discussions have also been about considerations of values, ethics and ideology, especially if you consider the sharing of the data gathered.
As your watch gathers data about your activity, food intake and heart rate, it has valuable data about your health. I do this on my Fitbit with its app. Perhaps you share that with an online service (as with the Apple watch & Apple itself) in order to get further feedback information about your health and fitness and even recommendations about things to do to improve it. If you want a really complete analysis, you are asked (hopefully) to share your medications, health history etc. Now, what if that is shared with your medical insurer and your employer?
Might we end up with a Minority Report of predictive analytics that tell the insurance company and your employer whether or not you are a risk?
Okay, I made a leap there, but not a huge one.
This summer, EDUCAUSE published a few articles on IoT concerning higher education and the collaboration required for the IoT to work. I don't see education at any level really making significant use of IoT right now, though colleges are certainly gathering more and more data about students. That data might be used to improve admissions. Perhaps, your LMS gathers data about student activity and inactivity and can use it to predict what students need academic interventions.
It's more of an academic challenge to find things that can be used currently.
History Lesson: Way back in 1988, Mark Weiser talked about computers embedded into everyday objects and called this third wave "ubiquitous computing." Pre-Internet, this was the idea of many computers, not just the one on your desk, for one person. Add ten years and in 1999, Keven Ashton posited a fourth wave which he called the Internet of Things.
Connection was the key to both ideas. It took another decade until cheaper and smaller processors and chipsets, growing coverage of broadband networks, Bluetooth and smartphones made some of the promises of IoT seem reasonable.
Almost any thing could be connected to the Internet. We would have guessed at computers of all sizes, cars and appliances. I don't think things such as light bulbs would have been on anyone's list.
Some forecasters predict 20 billion devices will be connected by 2020; others put the number closer to 40-100+ billion connected devices by that time.
And what will educators do with this?