IoT medical devices transforming healthcare by changing every aspect of our social and professional lives as billions of pervasive devices enable the acquisition of timely and accurate information about our personal context, the data gathering transforms what doctors can do with actionable knowledge.
The healthcare sector provides an excellent example of the way in which the future billions of IoT devices will introduce disruptive transformation and new paradigms. In an era where population is aging and incidents of chronic diseases are proliferating, healthcare solution providers are increasingly looking into internet connected devices for remote monitoring of elderly and patients’ conditions.
This remote monitoring facilitates preemptive medical interventions, while at the same time increasing the patients’ independence, reducing hospitalization needs and alleviating pressures on the healthcare system. One of the most prominent classes of IoT Medical Devices transforming healthcare today is wearable devices, which are personalized and provide rich and real-time information about an individual’s healthcare related context, such as heart rates, activity patterns, blood pressure or adherence to medication schedules.
Wearable devices play an instrumental role in monitoring patients’ diseases and recovery state, as well as adherence to prescribed practices and medication. A large number of relevant wearable devices are already available in the market such as activity trackers, smartwatches (e.g., Apple or Garmin Watches), pedometers, sleep apnea detector and smart pills (e.g., AdhereTech’s smart wireless pill bottle).
Implant IoT Medical Devices Transforming Healthcare
A less widely known class of wearable IoT medical devices transforming healthcare are implant devices, i.e. devices that are placed inside or on the surface of the human body. The concept of such devices has been around for several years prior to the rise of the IoT paradigm, as prosthetics that were destined to replace missing body parts or even to provide support to organs and tissues.
Therefore, implants were typically made from skin, bone and other body tissues, or from materials (e.g., metal, plastic or ceramic materials). While the distinguishing line between conventional IoT medical devices and wearable / implant devices can sometimes be blurred, we consider as implant medical devices those attached to the skin or placed inside the human body, instead of devices simply worn by the patient.
Impressive examples of implant devices are: (i) Brain implant devices (i.e. electrodes along with a battery empowered devices) used to manipulate the brain and alleviate chronic pain, depression or even schizophrenia; (ii) Electronic chips implanted at the back of the retina in the eye, in order to help sight restoration.
With the advent of IoT medical devices transforming healthcare, implant devices can also become connected and deliver information to cloud computing infrastructures and other applications. In this way, they can become part of the IoT infrastructure and enable the transmission of medical data from the patient to the practitioner on a regular basis. Moreover, with IoT implants patients no longer need to visit their doctor in order download data from their device or even in order to configure the operation of the implant device.
For example, by enhancing devices such as the electronic chip for vision restoration (outlined above) with a small handheld wireless power supply, one can adjust the sensitivity, contrast and frequency as needed in order to yield optimal performance of the device for different environmental settings (e.g., lighting conditions).
Risks with IoT Medical Devices Transforming Healthcare
Despite their benefits, the adoption of implant IoT medical devices is still in its infancy. One of the main reasons is that the development and deployment of implants is associated with several challenges and risks. In particular, implants are associated with surgical risks concerning their placement and removal processes. Although generally safe, these processes could lead to infections or even implant failures, which makes patients reluctant to adopt them. Moreover, several patients have reported allergies and reactions to the materials comprising the implant devices.
Beyond these adoption challenges, there are also IoT technological challenges associated with the need to understand and optimize the placement and operation of the device. For example, there is a need to optimize radio communications between the implanted device and the receiving devices where the information of the implant is destined. In this respect, low power operation is very important as a result of the need to economize on power capacity, while at the same time complying with applicable laws and regulations, including security and safety regulations.
From a technology viewpoint, implant solutions have to resolve trade-offs associated with efficiency and accuracy against antenna size, power use, operating bandwidth and materials costs. Moreover, implant devices should be appropriate for various body and skin morphologies, while at the same time offering security and data protection features that render them immune to malicious parties that may attempt to compromise their operation.
The above-listed factors render the design of cost-effective implants that adhere to regulations and optimize their operation very challenging. In order to alleviate these challenges, vendors and integrators of IoT implants resort to simulation. Simulation is an ideal tool for modelling the operation of the device and understanding its communication with the body and other devices of the surrounding environment such as gateways or even other implant devices.
Furthermore, vendors are implementing services that aim at increasing the operational efficiency of the devices, such as preventive or predictive maintenance of the device, as well as remote diagnostics and software upgrades (e.g., remote patching). The last batch of challenges concerns the important business issues with IoT medical devices transforming healthcare, especially implants, which are not confined to selling devices.
Rather, it is about innovating digitally and offering a whole range of services as part of the device’s industry ecosystem. Specifically, vendors and integrators of IoT implants need to find novel ways and business models for sharing their data with healthcare services providers and other stakeholders, while at the same time creating new value chains in collaboration with other device vendors, health professionals, home care services providers and other business actors.
The evolution of IoT medical devices transforming healthcare with implants will gradually signal a shift from the offering of an optimal IoT device to the offering of a pool of optimized and personalized healthcare services that will be built by the device’s industry ecosystem. Implant IoT medical devices are here and expected to play a significant role in the on-going IoT-driven transformation of the healthcare landscape. Stay tuned!.
Embedded Wireless devices, once thought to be too small to include their own security, undergo a more thorough analysis beginning with firmware testing. The software inside the chip is just as important as the application controlling it. Both need to be tested for security and quality. Some of the early IoT botnets have leveraged vulnerabilities and features within the device itself.
“Embedded wireless devices really are one of the most common devices on the Internet, and the security of these devices is terrible.” Those were the words of network security expert H.D. Moore, the developer of the penetration testing software Metasploit Framework, when discussing an illicit attempt to survey the entire internet.
Consumer Based Embedded Wireless Devices
Dan Goodin of Ars Technica tells the tale of a guerrilla researcher who collected nine terabytes of data from a scan of 420 million IPv4 addresses across the world. “The vast majority of all unprotected devices are consumer routers or set-top boxes which can be found in groups of thousands of devices,” wrote the anonymous researcher in his 5,000-word report. “A lot of devices and services we have seen during our research should never be connected to the public Internet at all.”
Hackers can do a lot of damage, and with billions of IoT devices forecast to be connected in the next few years, embedded devices security should be more than an afterthought.
In 2015, two white hat hackers demonstrated that they could break into late model Chrysler vehicles through the installed UConnect, an internet-connected feature that controls navigation, entertainment, phone service, and Wi-Fi.
By rewriting firmware on a chip in an electronic control unit (ECU) of a Jeep Cherokee, they were able to use the vehicle’s controller area network (CAN) to remotely play with the radio, windshield wipers, and air conditioning — even kill the engine.
The cybersecurity risks are real. Alan Grau writes on the IEEE Spectrum website about three significant incidents affecting the health care industry. A report by TrapX Labs called “Anatomy of an Attack–Medical Device Hijack (MEDJACK)” describes how hackers were able to target medical devices to gain entry to hospital networks and transmit captured data to locations in Europe and Asia. “Stopping these attacks will require a change of mindset by everyone involved in using and developing medical devices,” says Grau.
Many of the hackable embedded wireless devices now on the market were created without much consideration for security. “Security needs to be architected from the beginning and cannot be made an option,” says Mike Muller, CTO of ARM Semiconductors, at a seminar he gave at the IoT Security Summit 2015. Muller believes that very few developers have any real understanding of security. ·“We cannot take all of the software community and turn them into security experts. It’s not going to work.” The answer is that best practices for embedded security must be established and followed. That includes splitting memory into “private critical and private uncritical” and creating device-specific encryption keys. “You have to build systems on the assumption that you’re going to get hacked,” warns Muller.
Identifying potential IoT vulnerabilities requires robust testing before putting devices into production. In 2014, the Open Web Application Security Project (OWASP) published a list called Internet of Things Top Ten: A Complete IoT Review. They recommend testing your IoT device for:
Insecure Web Interface (OWASP I1)
Poor Authentication/Authorization (OWASP I2)
Insecure Network Services (OWASP I3)
Lack of Transport Encryption (OWASP I4)
Privacy Concerns (OWASP I5)
Insecure Cloud Interface (OWASP I6)
Insufficient Security Configurability (OWASP I8)
Insecure Software/Firmware (OWASP I9)
Poor Physical Security (OWASP I10)
As with any testing, well-written test cases will help manufacturers ensure the security of embedded wireless devices. Better to run through possible scenarios in the lab that to have major issues with customers later. In November 2016, Dan Goodin of Ars Technica reported that a “New, more-powerful IoT botnet infects 3,500 devices in 5 days”. Goodin writes that “Linux/IRCTelnet is likely only the beginning of what could be a long line of next-generation malware that steadily improves its capabilities.” And he laments the defenselessness of IoT devices that proliferate across the web. It’s a sentiment that’s shared by many.
What about your experiences with IoT security and embedded wireless devices? Any stories to tell? What are your recommendations for making things safer? Feel free to post your comments here.
Curious – can LTE and 5G compete or compliment IoT networks or the other way around? The big cellular companies have heavily invested in Long-Term Evolution (LTE) networks and the coming 5G network. They are saying it can compete with the Internet of Things (IoT) network that smaller companies are putting their bets on.
“Despite the prospect of new networks that reach farther than cells and let IoT devices communicate for years on one battery charge, many of the power-sipping networked objects to be deployed in the coming years will use LTE and future 5G cellular systems,” reports Stephen Lawson in Computerworld. Lawson’s article depends largely on information from the LTE and 5G network developers..
ZDNet took a look at IoT investments stating that “Investors in Sigfox’s fund raising included major cellular network operators NTT Docomo, SK Telecom, and Telefonica, so it seems that some at least are hedging their bets,” wrote Stuart Corner. Verizon has not made that kind of investment, but it is investing in its own IoT tech. Looking at the Category M1 tech Verizon is working on, it’s hard to see major differences between that and the IoT networks under development, and in place, by the LORA Alliance, Sigfox and others. Cat M1 runs on a 1.4mhz bandwidth with speeds capped at one meg a second. It promises to come in under $10 for consumers.
Verizon is saying LTE and 5G compete or compliment IoT networks and in fact they will exist together. Rosemary McNally, Verizon’s VP for mobile devices and operating system technology, told RCR Wireless that “the Cat M1 network they have in mind will run on the LTE. It will offer more security than IoT”, she promises. So the question needs to be reframed. Instead of asking if the two networks can compete, ask instead do LTE and 5G have to compete on the same grounds as IoT? No, because they don’t have to.
Will LTE and 5G compete or compliment IoT networks?
The IIoT and 5G merge in places like over-the-road shipping. IIoT sensors inside the truck feed data into the 5G and LTE networks, which hand it over to controllers and monitors. Decisions can be made within minutes.
The agriculture industry is also using the IoT. Modern tractors are embedded with sensors that provide regular feedback to the manufacturer. A farmer in South Georgia recently got a call from the tractor dealership. The sales rep said he’d received a message that whoever was driving one of the farm’s tractors was “riding the clutch.” Riding the clutch can cause it burn out, a costly repair. By having IoT in the tractor, the maker was able to monitor use and save the owner money.
Another reason LTE and 5G compete or compliment IoT networks is radio frequencies. The Verizon Cat M1 is going to run on licensed bands. Once those bands hit maximum transmission traffic, Verizon is either going to have to get new bandwidth, which can run to the millions of dollars, or scale back some traffic. If that happens, will Verizon continue to support Cat M1, which appears to have low profit margins? Or, will the company discontinue its IoT investments?
Where 5G and LTE have an advantage is security. Current IoT is running on unlicensed spectrum. Anyone can use it. Turf wars may erupt. Two companies next to each other decide to use the same frequency for their IoT. The signals interfere with each other, causing minor to major problems. With licensed frequencies, this is not a problem.
So can LTE and 5G compete or compliment 5G and LTE complement Iot networks? In truth they compliment each other. Each has strengths and each has weaknesses. Using each system’s strong points to cover the other’s weak points will create a much stronger network than either could be independently.
WHAT THE FUTURE HOLDS
Doug Brake takes a long and hard look at IoT, 5G, LTE and nextgen wireless in a report for the Information Technology and Innovation Foundation. The industry has gone from 1G (analog) in the 80s to 2G, 3G and now 4G in the past few years. He points out the industry goes through a major upgrade every 10 years. Each upgrade has required big investments. With 2020 a short four years away and 5G already being discussed, AT&T, Sprint and the rest are planning major investments to upgrade the wireless network. The smart ones are planning upgrades that allow IoT.
Can LTE and 5G compete or compliment IoT networks?
The questions that should be asked are:
How can IoT be merged into higher-speed transmissions to let on-site and remote operators make better decisions? SugarCreek is one example of how this merger works. Modern tractors are another.
What will be the standard? IoT must have a standard just as smartphones do today. A Verizon phone can call, SMS, MMS and so forth to an AT&T phone. Consumers will demand the same for IoT. A homeowner will buy a fridge from General Electric, get an HVAC from Trane and a home entertainment system from Crutchfield. He will demand all the systems function seamlessly on the same IoT network. The IIoT is making inroads on standards, but much more work needs to be done. Equipment needs to move seamlessly from plant to plant. Just installing the hardware is expensive enough. The wireless controls should be plug and play.
Is a frequency “land grab” ahead as regulators look at the unlicensed frequencies and increasing demand for them? How much is needed?
What kind of security protocols are needed? Yes, it may take a day to hack into a microwave, but someone is going to do it. That’s an annoyance. Hacking into the smokers at SugarCreek could shut down production for a day or more and cost the company plenty. How can this be stopped? Since IoT is going to be largely low-speed, small data, could each device have a limiter? Perhaps once a certain amount of data is sent, the device takes an action to alert the owner or disconnection from the IoT.
It’s been more than a decade since the time when the number of internet-connected devices exceeded the number of people on the planet. This milestone signaled the emergence and rise of the Internet of Things (IoT) paradigm, smart objects, which empowered a whole new range of applications that leverage data and services from the billions of connected devices. Nowadays IoT applications are disrupting entire sectors in both consumer and industrial settings, including manufacturing, energy, healthcare, transport, public infrastructures and smart cities.
Evolution of IoT Deployments
During this past decade IoT applications have evolved in terms of size, scale and sophistication. Early IoT deployments involved the deployment of tens or hundreds of sensors, wireless sensor networks and RFID (Radio Frequency Identification) systems in small to medium scale deployments within an organization. Moreover, they were mostly focused on data collection and processing with quite limited intelligence. Typical examples include early building management systems that used sensors to optimize resource usage, as well as traceability applications in RFID-enabled supply chains.
Over the years, these deployments have given their place to scalable and more dynamic IoT systems involving many thousands of IoT devices of different types known as smart objects. One of the main characteristic of state-of-the-art systems is their integration with cloud computing infrastructures, which allows IoT applications to take advantage of the capacity and quality of service of the cloud. Furthermore, state of the art systems tends to be more intelligent, as they can automatically identify and learn the status of their surrounding environment to adapt their behavior accordingly. For example, modern smart building applications are able to automatically learn and anticipate resource usage patterns, which makes them more efficient than conventional building management systems.
Overall, we can distinguish the following two phases of IoT development:
Phase 1 (2005-2010) – Monolithic IoT systems: This phase entailed the development and deployment of systems with limited scalability, which used some sort of IoT middleware (e.g., TinyOS, MQTT) to coordinate some tens or hundreds of sensors and IoT devices.
Phase 2 (2011-2016) – Cloud-based IoT systems: This period is characterized by the integration and convergence between IoT and cloud computing, which enabled the delivery of IoT applications based on utility-based models such as Platform-as-a-Service (PaaS) and Software-as-a-Service (SaaS). During this phase major IT vendors such as Amazon, Microsoft and IBM have established their own IoT platforms and ecosystems based on their legacy cloud computing infrastructures. The latter have alleviated the scalability limitations of earlier IoT deployments, which provided opportunities for cost-effective deployments. At the same time the wave of Big Data technologies have opened new horizons in the ability of IoT applications to implement data-driven intelligence functionalities.
AI: The Dawn of Smart Objects using IoT applications
Despite their scalability and intelligence, most IoT deployments tend to be passive with only limited interactions with the physical world. This is a serious set-back to realizing the multi-trillion value potential of IoT in the next decade, as a great deal of IoT’s business value is expected to stem from real-time actuation and control functionalities that will intelligently change the status of the physical world.
In order to enable these functionalities we are recently witnessing the rise and proliferation of IoT applications that take advantage of Artificial Intelligence and Smart Objects. Smart objects are characterized by their ability to execute application logic in a semi-autonomous fashion that is decoupled from the centralized cloud.
In this way, they are able to reason over their surrounding environments and take optimal decisions that are not necessarily subject to central control. Therefore, smart objects can act without the need of being always connected to the cloud. However, they can conveniently connect to the cloud when needed, in order to exchange information with other passive objects, including information about their state / status of the surrounding environment.
Prominent examples of smart objects follow:
Socially assistive robots, which provide coaching or assistance to special user groups such as elderly with motor problems and children with disabilities.
Industrial robots, which complete laborious tasks (e.g., picking and packing) in warehouses, manufacturing shop floors and energy plants.
Smart machines, which predict and anticipate their own failure modes, while at the same time scheduling autonomously relevant maintenance and repair actions (e.g., ordering of spare parts, scheduling technicians visits).
Connected vehicles, which collect and exchange information about their driving context with other vehicles, pedestrians and the road infrastructure, as a means of optimizing routes and increasing safety.
Self-driving cars, which will drive autonomously with superior efficiency and safety, without any human intervention.
Smart pumps, which operate autonomously in order to identify and prevent leakages in the water management infrastructure.
The integration of smart objects within conventional IoT/cloud systems signals a new era for IoT applications, which will be endowed with a host of functionalities that are hardly possible nowadays. AI is one of the main drivers of this new IoT deployment paradigm, as it provides the means for understanding and reasoning over the context of smart objects. While AI functionalities have been around for decades with various forms (e.g., expert systems and fuzzy logic systems), AI systems have not been suitable for supporting smart objects that could act autonomously in open and dynamic environments such as industrial plants and transportation infrastructures.
This is bound to change because of recent advances in AI based on the use of deep learning that employs advanced neural networks and provides human-like reasoning functionalities. During the last couple of years we have witnessed the first tangible demonstrations of such AI capabilities applied in real-life problems. For example, last year, Google’s Alpha AI engine managed to win a Chinese grand-master in the Go game. This signaled a major milestone in AI, as human-like reasoning was used instead of an exhaustive analysis of all possible moves, as was the norm in earlier AI systems in similar settings (e.g., IBM’s Deep Blue computer that beat chess world champion Garry Kasparov back in 1997).
Implications of AI and IoT Convergence for Smart Objects
This convergence of IoT and AI signals a paradigm shift in the way IoT applications are developed, deployed and operated. The main implications of this convergence are:
Changes in IoT architectures: Smart objects operate autonomously and are not subject to the control of a centralized cloud. This requires revisions to the conventional cloud architectures, which should become able to connect to smart objects in an ad hoc fashion towards exchanging state and knowledge about their status and the status of the physical environment.
Expanded use of Edge Computing: Edge computing is already deployed as a means of enabling operations very close to the field, such as fast data processing and real-time control. Smart objects are also likely to connect to the very edge of an IoT deployment, which will lead to an expanded use of the edge computing paradigm.
Killer Applications: AI will enable a whole range of new IoT applications, including some “killer” applications like autonomous driving and predictive maintenance of machines. It will also revolutionize and disrupt existing IoT applications. As a prominent example, the introduction of smart appliances (e.g., washing machines that maintain themselves and order their detergent) in residential environments holds the promise to disrupt the smart home market.
Security and Privacy Challenges: Smart objects increase the volatility, dynamism and complexity of IoT environments, which will lead to new cyber-security challenges. Furthermore, they will enable new ways for compromising citizens’ privacy. Therefore, new ideas for safeguarding security and privacy in this emerging landscape will be needed.
New Standards and Regulations: A new regulatory environment will be needed, given that smart objects might be able to change the status of the physical environment leading to potential damage, losses and liabilities that do not exist nowadays. Likewise, new standards in areas such as safety, security and interoperability will be required.
Market Opportunities: AI and smart objects will offer unprecedented opportunities for new innovative applications and revenue streams. These will not be limited to giant vendors and service providers, but will extend to innovators and SMBs (Small Medium Businesses).
AI is the cornerstone of next generation IoT applications, which will exhibit autonomous behavior and will be subject to decentralized control. These applications will be driven by advances in deep learning and neural networks, which will endow IoT systems with capabilities far beyond conventional data mining and IoT analytics. These trends will be propelled by several other technological advances, including Cyber-Physical Systems (CPS) and blockchain technologies. CPS systems represent a major class of smart objects, which will be increasingly used in industrial environments.
They are the foundation of the fourth industrial revolution through bridging physical processes with digital systems that control and manage industrial processes. Currently CPS systems feature limited intelligence, which is to be enhanced based on the advent and evolution of deep learning. On the other hand, blockchain technology (inspired by the popular Bitcoin cryptocurrency) can provide the means for managing interactions between smart objects, IoT platforms and other IT systems at scale. Blockchains can enable the establishment, auditing and execution of smart contracts between objects and IoT platforms, as a means of controlling the semi-autonomous behavior of the smart object.
This will be a preferred approach to managing smart objects, given that the latter belong to different administrative entities and should be able to interact directly in a scalable fashion, without a need to authenticating themselves against a trusted entity such as a centralized cloud platform.
In terms of possible applications the sky is the limit. AI will enable innovative IoT applications that will boost automation and productivity, while eliminating error prone processes. Are you getting ready for the era of AI in IoT?
Industrial IoT predictive maintenance is expected to generate the large scope of B2B transactions that require data analysis. Indeed, IIoT is on such a growth pattern many of the billions of connected things in the coming years will be industrial assets, which will be deployed in settings like factories, agricultural, oil refineries and energy plants.
According to McKinsey the Industrial Internet has the potential to deliver up to $11.1 trillion on an annual basis by 2025 and 70% of this is likely to concern industrial and business-to-business solutions i.e. the Industrial IoT is expected to be worth more than twice the value of the consumer internet.
The Industrial IoT is at the heart of the fourth industrial revolution (Industry 4.0), which is driven by the interconnection of all industrial assets and the ability to collect and analyze data from them. In the scope of the Industrial IoT, assets are cyber-physical systems, which enable the control of physical devices through their cyber representations and the processing of digital data about them.
The applications of cyber-physical systems span a very broad range, including production control, process optimization, asset management, integration of new technologies (such as 3D printing & additive manufacturing), as well as various industrial automation tasks. Nevertheless, the most prominent application is the ability to continually monitor, predict and anticipate the status of assets, with emphasis on industrial IoT predictive maintenance using predictions about when a piece of equipment should be maintained or repaired.
Industrial IoT Predictive Maintenance Key to Industry 4.0
Maintenance and Repair Operations (MROs) are at the heart of industrial operations, as they involve repairing mechanical, electrical, plumbing, or other devices as a means of ensuring the continuity of operations. Nowadays, the majority of MRO operations are carried out on the basis of a preventive maintenance paradigm, which aims at replacing components, parts or other pieces of equipment, prior to their damage that could catastrophic consequences such as low production quality and cease of operations for a considerable amount of time. However, in most cases preventive maintenance fails to lead to the best usage of equipment (i.e. optimal Operating Equipment Efficiency (OEE)), as it maintenance is typically scheduled earlier than actually required.
In industrial IoT predictive maintenance (PdM) alleviates the limitations of preventive approaches. PdM is based on predictions about the future state of assets, with particular emphasis on anticipating the time when an asset will fail in order to appropriately schedule its maintenance.
PdM is empowered by models that estimate when the cost of maintenance becomes (statistically) lower that the cost that is associated with the risk of equipment failure.
Based on an optimal scheduling of maintenance, PdM leads to improved OEE, enhanced employee productivity, increased production quality, reduced equipment downtime, as well as a safer environment where failures are anticipated and repairs proactively planned. McKinsey & Co. estimates that the economic savings of predictive maintenance could total from $240 to $630 billion in 2025.
Nevertheless, there are still many industries that dispose with preventive maintenance, since they have no easy way to integrate and analyze data sets from thousands of heterogeneous sensors that are typically available in their plant floors. As a result only a fraction (i.e. 1% according to McKinsey & Co) of the available data is used, which is a serious setback to unlocking the potential of predictive maintenance applications, such as maintenance as a service, on-line calculation of OEE risk, maintenance driven production schedules and more.
The advent of Industrial IoT predictive maintenance is gradually unlocking the potential of PdM technologies facilitate the collection and integration of data from thousands of different sensors, while at the same time providing the means for unifying the semantics of the diverse data sets. Furthermore, IoT analytics technologies (notably predictive analytics) facilitate the processing of IoT data streams with very high ingestion rates based on machine learning and statistical processing techniques that can predict the future condition of components and equipment.
In several cases, IoT data are processed by Artificial Intelligence based techniques such as deep learning, in order to identify hidden patterns about the degradation of assets. Deep learning techniques are capable of leveraging (multimedia) data from multiple maintenance modalities such as vibration sensing, oil analysis, thermal imaging, acoustic sensors and more. Moreover, advanced deployments of industrial IoT predictive maintenance are not limited to deriving predictions about the future state of assets. Rather, they are able to close the loop down to the plant floor, through for example changing configurations in production schedules, altering the operational rates of machines or even driving automation functions.
Rise of Industrial IoT Predictive Maintenance Products and Services
PdM is looming as one of the killer applications for the Industrial IoT, which is evident not only on its potential savings but also on the rise of relevant IoT-based products and services. Most vendors have been recently releasing IoT-based solutions for PdM. In addition to empowering data collection and analytics, vendors are striving to enhance their products with added-value functionalities that help them stand out in the market. For example:
IBM predictive maintenance solution is able to perform root cause analysis in a holistic way, including predictions about where, when and why asset failures occur.
Software AG’s solution for industrial IoT predictive maintenance integrates with ERP and human resources systems to automatically plan the optimal allocation of tasks to technicians.
SAP integrates predictive maintenance information with business information (e.g., CRM and ERP systems) and enterprise asset management (EAM) systems. To this end, it benefits from its strong presence and installed base in the ERP market.
Microsoft offers PdM solutions over its Azure IoT suite in a way that offers preconfigured solutions (templates) for monitoring assets and analyzing their usage in real-time.
Recently, the DataRPM platform has been also established by a consortium of different vendors and manufacturers. DataPRM claims ability to deliver Cognitive Predictive Maintenance (CPdM) for Industrial IoT, based on the use of Artificial Intelligence for automating predictions of asset failures and closing the loop to ERP, CRM, and other business information systems.
Other major players in industrial engineering and automation, such as SIEMENS and BOSCH are offering their own platforms, while all major IT consulting enterprises have relevant services in their portfolio. Nevertheless, it is indicative of the market momentum of PdM and its positioning as one of the most prominent applications in the growing Industrial IoT predictive maintenance market.