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Internet of Everything vs Internet of Things: What’s the Difference?

Illustration: © IoT For All

Unless you’re an expert, there’s little difference between the Internet of Things (IoT) and the Internet of Everything (IoE). However, the latter term is broader, semantically. In this post, we’ll go into the details to explain why IoT software development companies use the term IoE comparatively rarely.

The Difference

The term IoT was coined in 1999 to refer to machine-to-machine, or M2M, communication. IoE appeared a few years later, to describe interrelated elements of a whole system, including people. IoE entails not only M2M communication but also P2M (people-to-machine) and even P2P (people-to-people) communication.

To understand the differences between the three types of communication, let’s consider several examples. Say it got dark outside and you turned on a light in the office, then you sat and typed on a keyboard. This scenario provides P2M examples of IoE.

We are so used to these things that we don’t even realize they are part of a system. Another example: You make a Skype call to your colleague. That’s a simple human-to-human, or P2P, communication. An example of M2M communication, on the other hand, is the process of data exchange between your office temperature sensing devices and the HVAC mainframe.

You might think M2M communication, being technological, is the most progressive means of interaction. but IoE focuses on P2M and P2P interactions as the most valuable. According to a Cisco analysis, as of 2022, 55% of connections will be of these two types. 

IoE is now considered the next stage of IoT development. Maybe this is why there are so few IoT development companies offering IoE development services at the moment. Internet of Things solutions are now more common and widespread.

4 Main Elements of the IoE Concept

 Thing

By thing, we mean an element of the system that participates in communication. A thing is an object capable of gathering information and sharing it with other elements of the system. The number of such connected devices, according to Cisco, will exceed 50 billion by 2020. 

What are things? In the IoT, a thing could be any object, from a smart gadget to a building rig. In the IoE, that expands to include, say, a nurse, as well as an MRI machine and a “smart” eyedropper. Any element that has a built-in sensing system and is connected on a network can be a part of the IoE.

People

People play a central role in the IoE concept, as without them there would be no linking bridge, no intelligent connection. It is people who connect the Internet of Things, analyze the received data and make data-driven decisions based on the statistics. People are at the center of M2M, P2M, P2P communications. People can also become connected themselves, for example, nurses working together in a healthcare center.

Data

In 2020, it’s projected that everyone using the internet will be receiving up to 1.7 MB of data per second.

As the amount of data available to us grows, management of all that information becomes more complicated. But it’s a crucial task because, without proper analysis, data is useless. Data is a constituent of both IoT and IoE. But it turns into beneficial insights only in the Internet of Everything. Otherwise, it’s just filling up memory storage.

Process

Process is the component innate to IoE. This is how all the other elements — people, things, data — work together to provide a smart, viable system. When all the elements are properly interconnected, each element receives the needed data and transfers it on to the next receiver. The magic takes place through wired or wireless connections.

Another way to explain this is that IoT describes a network and things, while IoE describes a network, things, and also people, data, and process.

Where Is IoE Applied?

As to the market, we can say confidently that IoT is a technology of any industry. IoE technology is especially relevant to some of the most important fields, including (1) manufacturing, (2) retail, (3) information, (4) finance & insurance, (5) healthcare. 

IoE technology has virtually unlimited possibilities. Here’s one example: More than 800 bicyclists die in traffic crashes around the world annually. What if there was a way to connect bike helmets with traffic lights, ambulances, and the hospital ecosystem in a single IoE. Would that increase the chances of survival for at least some of those cyclists? 

Another example: Do you realize how much food goes to waste, say at large supermarkets, because food isn’t purchased by its best-before date? Some perishable products like fruit and vegetables are thrown away due to overstocks even before they get to the market. What happens if you find a way to connect your food stocks with the racks and forklifts of the supermarket in-stock control system using IoE?

There are endless variations on uses of IoE right now, and many of them are already becoming familiar in our “smart” homes.

Summing up

In our industry, few would deny the value of IoE in improving our standard of living. Luckily, there’s a flourishing market of IoT development services. Who knows, maybe one day soon, you’ll be a “thing” in the IoE environment.

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Shielding Frontline Health Workers with AI

Illustration: © IoT For All

We are living through an unprecedented crisis. During the COVID-19 pandemic, healthcare workers have emerged as frontline heroes, working overtime to protect our communities from the spread of novel coronavirus. But they aren’t immune to the anxious, uncertain atmosphere the pandemic has fostered nor, indeed, the coronavirus itself.

We need to protect the first responders and hospital staff who put their wellbeing on the line to support their communities during a crisis. To my mind, that means using every tool at our disposal to the fullest — with AI chief among those at hand.

Creative Solution

There’s little doubt that the current situation demands a creative solution. The United States has become the center of the global pandemic; as of April 16th, the US confirmed 644,188 cases and endured 28,579 deaths. Despite efforts to flatten the curve by ordering regional shut-downs and stay-at-home orders, hospitals across the county have been all but overwhelmed by incoming cases. The impact on provider morale has, according to reporting from NPR, been similarly problematic.

“Nearly a month into the declared pandemic, some health care workers say they’re exhausted and burning out from the stress of treating a stream of critically ill patients in an increasingly overstretched health care system,” NPR reporters Will Stone and Leila Fadel recently wrote. “Many are questioning how long they can risk their own health […] In many hospitals, the pandemic has transformed emergency rooms and upended protocols and precautions that workers previously took for granted.”

Hospitals are doing all they can to keep their caregivers safe and protected, but their resources are stretched far too thin. According to reports, some hospitals in high-infection areas like New York City can only afford to give healthcare workers one N95 mask every five days. Used masks are collected, disinfected, and returned on a cycle between uses. But some frontline workers worry that, given the highly contagious nature of the disease, they may not be adequately protected.

“It can be disheartening to have that feeling of uncertainty that you are not going to be protected,” Sophia Rago, an ER nurse based in St. Louis, told reporters for NPR.

We need to shield our frontline workers as much as possible. The obvious solution would be to increase stores of personal protective equipment (PPE) and N95 masks; however, given that we face a national shortfall and harsh state-to-state bidding wars over the gear, that fix seems unlikely. What we can do to at least lessen the risk of patient-to-provider transmission is to invest in AI-powered solutions that can automate some healthcare protocols and limit the need for close contact.

“Traditional processes — those that rely on people to function in the critical path of signal processing — are constrained by the rate at which we can train, organize, and deploy human labor. Moreover, traditional processes deliver decreasing returns as they scale,” a team of digital health researchers recently wrote in an article for the Harvard Business Review.

“Digital systems can be scaled up without such constraints, at virtually infinite rates. The only theoretical bottlenecks are computing power and storage capacity — and we have plenty of both. Digital systems can keep pace with exponential growth.”

These AI-powered, digitally-facilitated solutions generally fall into two broad categories: disease containment and patient management.

Assessing AI’s Ability to Limit Disease Transmission

When it comes to limiting disease spread, the aim is to use AI tools to allocate human resources better while still protecting patients and staff. Take the screening system that was recently deployed at Tampa General Hospital in Florida, for example. This AI framework was designed by the autonomous care startup Care.ai and intended to facilitate early identification and interception of infected people before they come into contact with others. According to a report from the Wall Street Journal, the Care.ai tool taps into entryway cameras and conducts a facial thermal scan. If the system flags any feverish symptoms such as sweat or discoloration, it can notify healthcare staff and prompt immediate intervention.

Other technology companies––Microsoft, for one––have rolled out similar remote diagnostic and alert tools in facilities across the globe. Their unique capabilities vary, but their purposes are the same: to prevent the spread of infection and provide support to overworked personnel.

As representatives for Microsoft shared in a recent press release, “[AI technology] not only improves the efficiency of epidemic prevention, but it also reduces the work burden of frontline personnel so that limited human resources can be used more effectively.”

In these resource-strapped time, the aid is undoubtedly needed.

AI’s Applications for Diagnostics and Patient Management

Fighting a pandemic is a task that requires speed. Now more than ever, providers must be able to accurately and quickly identify infected patients so that they can trace and hopefully contain the viral spread. But doing so isn’t an easy order.

To borrow a quote from Forbes contributor Wendy Singer, “Analyzing test results nowadays requires skilled technicians and a lot of precious time, as much as a few days. But in our current reality, healthcare systems need to analyze thousands of results instantly, and to expose as few lab workers as possible to the virus.”

We don’t have that kind of time––and we can’t put our lab workers at undue risk. Thankfully, cutting-edge AI technologies may provide a solution. With AI, hospitals can automate some steps of the testing process, cutting down on the time and effort needed to process test results. These capabilities aren’t just hypothetical; in the weeks since the start of the pandemic, the health tech startup Diagnostics.ai has provided laboratories in the US and UK with a diagnostic tool that streamlines the testing process by automating DNA analysis.

However, the applications of AI diagnostics aren’t limited to testing alone. Some have also used artificial intelligence to support population management in overstretched hospitals. One Israeli medical-device developer, EarlySense, recently developed an AI-powered sensor that can identify which patients will most likely face complications like sepsis and respiratory failure within six to eight hours. This can give a hospital the information it needs to best allocate limited resources and staff attention.

No AI innovation — no matter how brilliant or helpful — will fix our resources shortfall. There is no question that healthcare providers need more PPE and support, or that they need it immediately. However, the benefits that AI provides to screen and patient management efforts are evident. It seems reasonable that we at least consider the weight the deployment of such tools could remove from our exhausted front-liners’ shoulders.

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Innovating during COVID-19: A Story of Collaboration

Connected World’s Peggy Smedley recently sat down for a webcast with Eddy Van Steyvoort, VP, business line automotive and on-road, IGW/VCST, which is a part of BMT Group, Kevin Wrenn, EVP, products, PTC, and Filip Bossuyt, CEO, Ad Ultima, for a discussion about innovating in a time of COVID-19, a story of collaboration.

Van Steyvoort shares the smart factory project, which started in 2017, in silos and realized quickly that it needed to think in an end-to-end scenario. He says it recognizes it had to change its systems, the organization, and its way of thinking to a more end-to-end focus to improve efficiency, reliability, quality, and the way it supports customers. The question became how does it change; and which tools to use? It decided to go to PTC and Ad Ultima to help support it.

“PTC’s PLM Software was known already in the BMT Group and that was a very, very, very strong asset and also a very strong signal from the beginning that we had already the relation, which was already there,” Van Steyvoort says. “We could build on that relation. That was the reason why we established a total plan as partners, and not let’s say as a customer supplier, but as partners,” he adds.

Then the COVID-19 coronavirus pandemic hit. Van Steyvoort opines the automotive industry has been shook by coronavirus, but it didn’t want to stop the strong drive on the project and decided not to change the long-term strategy.

He insists it now knows what AR (augmented reality) is and what it can bring during COVID-19, explaining that it can support people locally from a global perspective to show them how to do things. This is one of the lessons learned during this time—that it needs to invest even more in augmented reality tools.

Ad Ultima’s Bossuyt adds it is helping VCST to think end-to-end and to realize its digital transformation. “Becoming digital is a challenge today because you have to do it end-to-end. You cannot do it for only a part of your business.”

Adding to the conversation, PTC’s Wrenn says PTC can help with openness. “We are open on multiple dimensions. Our technology is open. It enables people to do digital transformation, as Eddy was talking about, connections all the way from engineering, all the way to the factory floor, and even out to their customers. Wwe are also open from a partnership standpoint. Ad Ultima is a really important partner of PTC’s and likewise of VCST. So we are used to working in these environments both from a technology standpoint and a partnership standpoint.”

When the COVID-19 pandemic first hit, PTC’s first response was to reach out to its customers and partners to make sure they could work from home. Wrenn says the technology is made to work from home and not have to be physically on site to be able to operate the technology. “It was much more important for us to figure out how our customers could create business continuity, and at the same time we were doing it for ourselves.”

In all of this, each individual learned something very important. Van Steyvoort says it is important to create a very strong sense of urgency from the very start and keep communicating this through the whole organization that it is a future-based strategy. “Instead of focusing on the change, focus on the alternative of doing nothing, because doing nothing that means you will lose the game.” Also, don’t be afraid to express the hopes and fears.

Ad Ultima’s Bossuyt notes the most important thing is the power of the network and working together with different partners where there is a lot of trust and all the stakeholders are aligned, which has created very good results. PTC’s Wrenn adds the new normal after COVID-19 is it will make people think about the kind of projects because digitalization is going to be a requirement in the new normal.

Going forward, the next steps for VCST is to link the CAD (computer-aided design) information to the PLM (product lifecycle management), that it goes through visualization in ThingWorx, and that the whole picture will be a completely integrated solution for the future. As Van Steyvoort says, “The sky is the limit. The technology is not the limit anymore.”

Want to tweet about this article? Use hashtags #IoT #sustainability #AI #5G #cloud #edge #digitaltransformation #machinelearning #futureofwork #PLM #CAD #AR

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AI-Driven Video Analytics for the Retail Industry

Illustration: © IoT For All

Artificial intelligence (AI) is directly correlated with Data Science, which is aimed at extracting business value from an array of information. This value can consist of expanding the capabilities of forecasting, knowledge of regularities, informed decision-making, cost reduction, etc. In other words, artificial intelligence operates with massive arrays of information, analyzes incoming data, and develops adaptive solutions based on them. 

In the modern world, the retail industry is rapidly increasing the application of artificial intelligence in all possible work processes. Thus, leveraging opportunities by applying analytics can undoubtedly improve a wide range of operations in the grocery industry. With AI, the largest supermarket chains are achieving very ambitious aims: 

  • improving and expanding customer service capabilities,
  • automating supply chain planning and orders delivery,
  • reducing product waste,
  • sharpening the management of out-of-stock and over-stock (grocery stock out), and
  • enhancing demand forecasting. 

The AI solution ecosystem is extensive and able to satisfy most needs of all grocery retailers (from large chains to the smallest businesses). As of now – during the quarantine, online grocery analytics has become a real “savior” in terms of managing stock-out conditions. With intelligent data-driven approaches, supermarkets can process a large amount of information, accurately forecast consumer demand and supply inventory, and generate the most accurate pricing and purchasing recommendations. As a result, grocery retailers will not only stay afloat, but will continue to generate profits even throughout the most critical situations, like during the coronavirus pandemic. With that being said, it is evident that all companies now require an immediate action plan in response to COVID-19. 

A New Level of Video Surveillance

As a rule, most grocery stores have a continuous video surveillance system. Previously, such systems were installed only for security purposes: controlling the safety of products and preventing theft. But now, artificial intelligence video analysis is able to monitor the behavior of customers from the moment they enter the store until payment. How does it work, and why do stores need it?

Large grocery chains like Amazon and Walmart use high-tech cameras that utilize automatic object identification (RFID). Typically, such a system is used in unmanned electric vehicles to monitor passenger behavior and process visual information via a computer. But the primary goal of video grocery store analytics is to determine which items are in high demand, which products buyers most often return to the shelves, etc. Moreover, cameras recognize faces, determine heights, weights, ages, and other physical characteristics of customers. Subsequently, the AI (based on all the obtained data) identifies the most popular products from specific consumer groups and offers options for changing the pricing policy. A computer automates all these processes without human intervention. 

Preventing Grocery Stock-out and Shrinkage

Artificial intelligence in the retail industry is capable of solving problems that people cannot cope with. Experts state that a person physically cannot view all the video surveillance. There is not enough time for this, and unfortunately, human vision is not perfect. But this is no longer necessary! Video analytics for grocery stores perfectly copes with such tasks. For example, connecting cameras to the store’s automated warehouse system and equipping shelves with sensors can uncover gaps in inventory records and stimulate investigations. Grocery store data analytics can also monitor stocks and provide signals about replenishment needs. Facial recognition technology as described above is capable of comparing the faces of people with criminals (or wanted individuals) and warn security.

Advancing Traffic Flows and Store Layout

Data collected about customer behavior helps supermarket managers optimize store layout. Moreover, the computer program can design the most “optimal” layout and test it, generating an overall better customer experience and an increase in the store’s monthly profit figure. 

Data can be collected about the number of people that enter a store and the amount of time they spend shopping. Based on this data, artificial intelligence can predict crowd sizes and the length of time people wait in line. It will help improve customer service and reduce staff costs during “calm” hours. In other words, AI is able to draw optimal store management plans at various hours of the day with maximum benefit for the business. For example:

  • develop traffic flows
  • optimize display placement and floor planning
  • improve strategic staff distribution
  • draw correlations within the dwell time and purchasing
  • predict products for individual shopping groups

Enhancing Customer Experience

Every business should know as much as possible about its audience to offer the best possible service. AI in grocery stores using video intelligence software gives detailed demographic data with a detailed analysis of shopping habits. This information provides unlimited opportunities for stores to increase profits. By knowing their customers, store managers can maximize the client shopping experience, creating favorable conditions (made specifically for customers’ preferences). Furthermore, AI for grocery stores can help produce the most accurate demand forecasting models of the given target market. 

In addition to working with the target audience, managers can transfer information to the marketing department with the data obtained from video analytics. By exploring other audiences, marketers can develop strategies to attract new customers by creating relevant advertising, promotions, and sales. Additionally, stores can create separate display cases (vegan products or gluten-free) for small shopping groups, satisfying their needs. 

Among all existing technologies of artificial intelligence for grocery stores, video content analytics provides maximum support in almost all activities: merchandising, marketing, advertising, and layout strategies. By optimizing these processes, stores not only save and reduce losses, but also have the opportunity to expand their business by increasing profits. The main goal is not only to satisfy customers, but to strengthen customer retention rate.

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How IoT is Changing Health Informatics

Illustration: © IoT For All

Our current digitally-enhanced era has helped to illustrate just how valuable data can be. When collected and analyzed by experts, it can help guide us to make decisions in business, creative industries, and — perhaps most importantly — healthcare. Health informatics is a vital area of medicine in which data about patients and their illnesses is used to assess the progress of their personal health conditions, drive treatment decisions, and also to build important public health strategies.

Expertise in informatics can have positive outcomes in people’s lives, but there are constant efforts to develop and improve tools that can support this. Over the last several years, the development of the internet of things (IoT) has emerged as a potentially integral solution to various challenges. This connected ecosystem of wearable technology, static devices, and apps has been helping to collect and process data that is vital to informatics.

Diagnosis and Treatment

As patients, we often see diagnosis and treatment from a simplistic perspective. We go to the doctor, they perform some tests, and in the best-case scenario, they tell us what’s wrong and how we can fix it. But those diagnoses and treatments are not just pulled out of the brains of our physicians; they are often the product of huge amounts of data, allowing analysts to identify correlating attributes. Similarly, before reaching the market our medications are subject to tests, providing researchers with volumes of data to help understand their effectiveness.

IoT technologies are helping with the data harvesting and analysis aspects of diagnostic medicine. Using wearable devices that collect data regarding symptoms and patient conditions, alongside information from traditional hospital testing, artificial intelligence (AI) software is then used to undertake speedy and thorough analysis work, using algorithms to help identify the likely diagnosis. In fact, a 2019 study by medical journal The Lancet found that AI analysis of medical imaging was in some ways equivalent to that of human medical professionals.

However, it’s also important to note that the aforementioned study also suggests that relying on deep learning and machines alone is not without its challenges. These technologies are currently best employed when in collaboration with experts in health informatics fields. Machines are unable to provide patients with the reassurance and warmth that a human healthcare professional can bring, nor apply context and empathy to finding solutions. While tech can help provide insight, human perspective can be valuable in interpreting and applying it.

Patient Records

One of the main areas in which health informatics comes into play is with patient records. In order for health professionals to make appropriate choices for patients, they must have access to a patient’s full history, including the results of any scans and tests. In the past, a primarily paper approach has meant that clinicians have not always received patient records in a timely manner, or have had to seek access from various different departments or even offices. This means patients haven’t always been treated with efficiency. The IoT has been instrumental in improving this.

The adoption of electronic medical records has been instrumental in streamlining the collection, sharing, and organization of health informatics. Providers across various departments using the same platform do not need to request information be sent to them, the patient’s information is already accessible in the system. Digital x-rays can not just be captured quickly and safely, but also emailed to those undertaking diagnosis, and stored in EHRs, to be reviewed when assessing problems in the future. This digital patient information can also be stored, shared, and viewed on portable, secure medical IoT devices such as tablets and data monitoring tools for ease of use even in emergency scenarios.

This method is not only useful for those who use health informatics for diagnosis and treatment, but it’s also a reassuring development for the patient themselves. The ability to securely transfer files to be stored and viewed on IoT devices means that patients have easy access to their own records, and are able to review and distribute them as needed. It also means that, should they move state or country, they don’t need to overcome unnecessary hurdles in giving their new health care provider their full medical history.

Self Management

Healthcare is not only the remit of those who have been educated to work in the field. We each have a responsibility to our own health, and continuing required treatments. After all, physicians can’t be constantly holding our hands to make sure we’re doing the right things. The field of health informatics has played an important role in assisting patients to manage their own wellness and treatments. As health informaticians have a deep insight into understanding patients and what motivates and supports them, they are a key source of education and resources for those with illnesses. The IoT is assisting in this area, too.

Patients can wear wearable IoT technology that provides them with real-time data on their physical condition. Utilizing specialized platforms, such as Quio, patients can connect IoT enabled therapeutic devices to their computer or smartphone and share data on their medication, vital signs, and activities with their physician. This helps to enable a meaningful, data-supported dialogue between doctor and patient, and also allows patients to feel empowered to take control of their treatment.

Perhaps one of the primary ways in which the IoT is assisting health informaticians and patients is its user-friendliness. Patients may be unlikely to engage with tech if they are unable to understand how it works, or how to interact with it. Combining health monitoring tech with smartphones and apps that are easily navigable not only helps to provide patients with insights that encourage them to take control of their own treatment, but can also improve the quality of feedback that can assist health informatics in the future.

Conclusion

As with much in our world today, data can be a valuable resource for medical fields. Health informatics can provide insights into patient diagnosis, ongoing wellness, and treatment management. IoT has started to produce tools that can help support health informatics in monitoring, analysis, and utilization of patient data. By exploring how to collaborate with these tools effectively, the industry and its patients stand to benefit significantly

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April: IoT Connectivity Leads to Better Road Safety

As with any relatively new technology, there are risks to its use. Lynch says the first risk to consider is the privacy of individuals using connected vehicles, especially when it comes to location tracking. Another concern is the security of these systems. “Wireless communications opens a vehicle to the world,” he says. “If cybersecurity is not sufficiently robust, some bad people could access the vehicle and jeopardize its safety.”

According to Na Jiao, technology analyst at IDTechEx, self-driving technologies and connected and autonomous vehicles add another layer of vulnerability to cyberattacks. The concern is not only the vehicles themselves, but also the environment in which the vehicles operate. “The threats to autonomous cars can come through any system connected to the vehicle’s sensors, communication applications, processors, control systems, and external inputs from other cars, infrastructure, and mapping and GPS data systems,” she says.

Chris Greer, director of the NIST (National Institute of Standards and Technology) Smart Grid and Cyber-Physical Systems Program, says by contributing greater and more accurate awareness to vehicles, technology can indeed produce a roadway environment that exhibits better driver and driverless vehicle decisions, but there is also risk. “The key to managing risk is to ensure that we’re able to measure those risks reliably and accurately,” Greer says.

As long as the industry considers risks and continuously looks for ways to mitigate these risks, it’s safe to assume connectivity will revolutionize road safety. It may even shift the transportation paradigm.

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In Spite of Numerous Setbacks, IoT Engineers are Continuing to Break New Ground

Illustration: © IoT For All

Search back just a few years, and you’ll find dozens of claims that the oncoming wave of Internet of Things devices was going to usher in a new industrial revolution. Set the clock forward a little bit and you’ll start to see engineering experts claiming that a number of setbacks have prevented this so-called revolution from ever taking place.

Today, people mostly talk about how adoption still isn’t anywhere near where certain marketing executives had hoped it would be. Even at the height of IoT device sales, less than 33 percent of homeowners even had a home automation device.

Price and retail availability, however, are only a small part of the many reasons that people avoid investing in this kind of technology. Engineers are hard at work attempting to resolve a number of related issues as well.

Barriers to IoT Device Adoption

Privacy concerns are probably the paramount reason that individual end-users don’t want to invest in their own IoT devices. Consumer-grade equipment has developed a rather unfortunate reputation for distributing private information collected from users. While some people are unaware of these concerns, mainstream media outlets have begun to distribute these stories to a much wider audience and that’s helped to ensure that at least some percentage of the intended market hasn’t bought into these products.

At the same time, power consumption and bandwidth limitations have been a major issue. There are only so many ways you could design a microprocessor that’s small enough to fit into a mounted box and won’t drain a battery that’s light enough to remain rechargeable over USB.

Some companies have attempted switching web hosts and building dedicated server infrastructures in order to try and improve overall data throughput. To some degree, this has alleviated the problem.

The fact remains, however, that collecting and processing data from every installed device is a difficult task. In fact, some computer scientists have estimated that the necessary hardware to process all of the world’s incoming IoT statistics doesn’t currently exist, which means that those who adopt new devices could potentially experience major service disruptions.

These disruptions would eventually cause some devices to cease functioning altogether. Perhaps the most stressing concern, however, is the fact that so many manufacturers have an extremely broad scope that doesn’t translate into product lines that consumers actually want.

For instance, startup companies will sometimes come around with a fair level of investment from outside firms. They’ll bring devices to market that don’t have much of a shot of attracting market attention. Once these firms realize that their products aren’t geared toward any specific market segment and therefore won’t sell, it’s normally too late.

Over time, this sort of boom-bust sales dichotomy has tarnished the industry’s reputation. Just like graphical user interfaces were once thought of as childish before they were completely understood, new devices are quickly being seen more as gimmicks than anything else.

Fortunately, though, there are a number of new innovations that have started to attract more attention than any of these setbacks might have in the past. Many of these involve the radical installation of industrial sensors in various types of devices that previously would have never been connected to a public network.

While these devices might not make waves in the retail market, they’re certainly starting to attract plenty of attention in the public works sector.

Industrial Sensors Revitalizing Public Works

At Hartsfield–Jackson Atlanta International Airport, the bathrooms are now studded with sensors. Urinals and faucets are starting to collect data from every single fixture and relay them to a cloud database. While this might not sound like much and certainly wouldn’t be the area of an airport that most people would focus on calculating statistics for, this technology has helped to identify certain peak times of water usage and drastically reduce waste.

As a result, it’s become popular enough to attract outside investors who might be interested in giving it a try. Currently, there are only a handful of other sites where water flush sensors have been laid out on such a large scale. That number, however, does continue to grow.

Albert Behr, the CEO of BehrTech, talked about how legacy systems are holding some people back in a recent podcast. Engineers are starting to develop ways to get around this problem, however.

Database patches and regular software upgrades can go a long way toward ensuring that people have access to the information they need to make informed decisions. Many IoT sensors allow users to export data as a spreadsheet or text file, which can then be opened by nearly any standard digital device regardless of age. Using this technique, it’s theoretically possible to interface one of the latest smart thermostats or motion detectors to an antiquated DOS-based PC.

Some companies have decided to bypass the entire issue. For instance, firms that currently have some sort of security system in place may elect to replace their current devices with newer smart cameras as their old ones wear out. Over time, this kind of transition can allow for IoT adoption without suffering from any of the sudden issues that normally occur when a firm radically switches the equipment it’s invested in.

Most sensors operate in a headless mode that doesn’t allow for any sort of user interface. As a result, it’s normally not necessary to retrain workers to work with nearly any type of gear. Most new equipment operates almost totally silently, which gives it the freedom to fit into nearly any kind of workshop.

Manufacturing companies stand the most to gain from this paradigm-changing shift.

The Advent of Industry 4.0 Firms

Experts from the academy of engineering sciences in Germany have been referring to certain types of industrial sensors as Industry 4.0 equipment since at least 2011. They laid out a road map for using embedded systems technology in nearly every kind of workflow you could imagine. While it might have taken longer than many of these top computer scientists originally thought, this kind of equipment is quickly becoming more common than at any other point in history.

Standalone robots have long been the face of automated manufacturing. Networked cyber-physical systems that feature information-based communication between equipment and human technicians are now all the rage. These can make decisions based on sensor input as well as information collected from human operators.

Machines are now able to communicate with each other and send out warnings in regard to the production process. Whenever one senses that there’s an increased risk for defects, the machinery will put out a warning and inform all of the other pieces of gear they’re in communication with as well as any shop floor overseers.

On top of this, Industry 4.0 equipment is capable of reordering scare material inventories and even predicting product shortages. Smarter factories that utilize additive manufacturing techniques can make things on demand, which has taken just-in-time production workflows to new heights. In fact, small business owners are starting to add just-in-time manufacturing processes to the list of services they offer.

This would have never been possible without this kind of technology. Precision agriculture is also possible as a result of the same equipment. Farmers are able to fly drones at pre-planned times and systems will begin to collect information autonomously. This can give planters heretofore unseen levels of data regarding the best time to put in crops and the location of groundwater.

Considering the renewed focus on land management as well as water and food resources, agribusinesses are sure to start adopting the hardware to do this very shortly.

Admittedly, however, it’s taking longer than it should to get to that point.

Expectations Versus Reality in the IoT World

Engineers from Sewio released an infographic that provides a wealth of information on the differences between actual adoption of IoT devices and original expectations. Marketing experts were absolutely bullish about these numbers for quite some time, meaning that there’s a fairly wide disparity between the two sets.

That being said, there’s also a great deal to be hopeful about when it comes to the industry in spite of these numbers. System-on-chip (SoC) designs make it easy to fashion entire circuit boards around a single piece of a silicon wafer. As a result, devices made with them in mind use far less power than older pieces of gear.

Companies that are concerned about electrical consumption are replacing existing pre-IoT equipment with equipment that’s designed to take advantage of the latest developments in SoC architecture. At the same time, the industry is also starting to develop new security protocols that can help reduce the risk of data leaks.

Smart cities were unable to deploy certain types of sensors on a large scale due in part to concerns over privacy and security. These new protocols will help to reduce the risk of data leaks happening and therefore help to assuage these concerns to some degree.

Technicians will also continue to develop radical new uses for these devices.

New IoT Devices Set for Release

Workplace monitors that automatically start and stop equipment when they sense the presence of human employees are already on the market. There are now talks about installing these sensors in the education market as well as in private residences.

At the same time, some technicians are finalizing plans for roadside sensors that can help ease the transition toward autonomous car adoption. Once consumers start to accept these other devices into their daily lives, they’ll be far more ready to invest in all the other IoT gadgets that are needed to keep them working over the long term.

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Emerging Technologies in the Pandemic Crisis: 10 Use Cases and Future Outlook

Illustration: © IoT For AllAs professionals in the emerging tech space, we are well aware of the many benefits of the Internet of Things (IoT), augmented and virtual reality (AR, VR), artificial intelligence (AI), and drones and robotics. Therefore, we cannot help but wonder: What if most of the emerging …

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Smart Building Initiatives are the Building Blocks of a Smart City

Illustration: © IoT For AllTo paraphrase a well-known saying, the journey to a complete smart city begins with a single building. No matter the size of the city, the extent of the technology or the most helpful use cases, a prospective smart city can integrate into — or branch off of — …

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5G and Cybersecurity Risk: Two Opposing Trends in Manufacturing

In just a few years, by 2023, the IoT (Internet of Things) in the manufacturing market is expected to reach $994 billion, according to Allied Market Research. Emerging technologies will encourage this growth, including in areas like AI (artificial intelligence), AR (augmented reality) and VR (virtual reality), and, importantly, 5G. However, cybersecurity …

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