IoT 2019 Predictions (Part 2)
IoT 2019 Predictions (Part 2)
2019 will bring more connectivity and a greater emphasis on securing devices and data.
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Given how fast technology is changing, we thought it would be interesting to ask IT executives to share their predictions for 2019. Here's what they told us about IoT:
The next wave of the Internet is the Internet of Things or the digitization of tangible products and assets. The Internet of Things will be no different from other markets disrupted by the Internet — except that the change will be across a far broader set of companies. After all, the Internet of Things involves the digitization of physical assets, and that includes pretty much every company. And just like other markets, some of the largest market participants will assume that they have plenty of time to respond, or that they are too big and too entrenched for the change to significantly impact their businesses. But they will be wrong.
Enterprises will replace VPNs with micro-perimeters to secure IoT gateway communications. Making smart products or IoT devices is the new product differentiator — even ovens have IP addresses now. Companies that have been investing in IoT initiatives understand that the IoT gateway layer is the key that unlocks a high return on those IoT investments. IoT gateways manage device connectivity, protocol translation, updating, management, predictive and streaming data analytics, and data flow between devices and the cloud. Improving the security of that high data flow with a Zero-Trust security model will drive enterprises to replace VPNs with micro-perimeters. Micro-perimeters remove an IoT device's network presence eliminating any potential attack surfaces created by using a VPN.
Companies solely focused on IoT security are going to be bought up by bigger security vendors or fold. While the securing IoT devices isn't going away anytime soon, the scope of the solution can’t be limited to IoT devices alone – it must encompass the full network and all entities, people, and artifacts connected to it.
IoT applications will use AI to "know us" — much more organically — in 2019. This will lead to a more natural communication experience between people and their connected technologies, and in a format that is most convenient for users in a given moment. Misunderstood voice commands, multiple steps required for authentication, unintentional device activations, etc. will be replaced by a zero-effort relationship that will be just like how humans communicate between each other now."
Successful businesses of the present and future are built on the sensation of instant gratification, and if they are not already there, in 2019 businesses are going to begin to pivot towards instant gratification. And while the on-demand economy is just one example of businesses driven by the sensation of instant gratification, it’s not just about delivering a good or service in a short period of time. It’s about delivering something (like an experience or bit of information) when the user requests it. Internet of Things do and will continue to play a major role in instantaneous delivery of data from devices to other devices or end users – such as immediately knowing if your door is locked, your sprinklers are leaking, or your baby is crying.
Industrial control systems (ICS) vulnerabilities have already been proven by successful attacks on electrical grid and chlorine plant in Ukraine, to a narrowly avoided disaster at a Saudi petrochemical plant. Security researchers are taking note and ICS (including SATCOM, PLCs, etc.) vulnerabilities were among the top three themes at this year’s Black Hat and DEF CON conferences. We predict that ICS attacks will escalate to the next level in 2019 and result in a major disaster at an industrial plant or a critical infrastructure facility.
IoT – Moore’s Law puts unprecedented compute and storage capacity at the edge. This combined with modern vision algorithms spur the rise of video as the “uber sensor.”
IoT will have a significant positive impact on food production in H2 2019 leading to a massive acceleration of IOT technology.
Smart Cities will continue to be delayed due to privacy, economic, and maturity concerns. IoT will become a major enterprise IT issue as more-and-more IoT devices appear in the workplace. Wi-Fi attacks (among others) will cause major concern and disruption.
2019 will be the year IoT adoption becomes mainstream, with practically most devices and gadgets becoming connected. This is going to create tremendous headaches and challenges for IT teams charged with securing all endpoints as non-corporate and corporate devices increasingly intermingle.
Bluetooth isn't new, but Bluetooth 5.0 + mesh networking will be transformative for the Internet of Things. Devices using Bluetooth 5.0 are just now starting to come onto the market, and they support larger messages at twice the data rate and at much longer distances than current Bluetooth devices — from the current 60m maximum range to 1.5km. New mesh networking capabilities extend this range even farther since information can relay across networks and chains of intermediary Bluetooth devices, which thanks to backward compatibility includes the gigantic installed base of existing Bluetooth devices. This is going to make larger and more complex systems of connected devices much easier to set up, manage, and deploy.
IoT goes from Passive to Active. As devices increasingly take action through automation, IoT will become transactional Big Data. This shift will cause companies to realize they need to do more than just collect and analyze that data offline. They also need to ensure that needed data is available close to the mobile customer to avoid latency and increase the speed of decision making.
IoT application platforms will continue to evolve from automating processes to realizing the value of aggregate data. Current platforms bring together broadly disparate device types, business rules, and analytics to quickly build scalable solutions for known problems. Modern IoT platforms will provide an environment where new applications can be rapidly developed and deployed, all information is stored in a shared data lake with sophisticated permissions management, and data scientists can easily ask new questions integrating multiple existing systems. This simplicity will enable new opportunities for innovation and optimization.
Survival of the smallest. IIoT analytics and ML companies will be heavily measured on how much they can deliver in how little compute. As IIoT projects pivot away from cloud-centric approaches, the next step in the evolution of artificial intelligence and IIoT will address the need to convert algorithms to work at the edge in a dramatically smaller footprint. According to Gartner, within the next four years, 75% of enterprise-generated data will be processed at the edge (versus the cloud), up from <10% today. The move to the edge will be driven not only by the vast increase in data, but also the need for higher fidelity analysis, lower latency requirements, security issues, and huge cost advantages.
While the cloud is a good place to store data and train ML models, it cannot deliver high fidelity real-time streaming data analysis. In contrast, edge technology can analyze all raw data and deliver the highest-fidelity analytics, and increase the likelihood of detecting anomalies, enabling immediate reaction. A test of success will be the amount of “power” or compute capability that can be achieved in the smallest footprint possible.
The market understands "real" versus "fake" edge solutions. As with all hot new technologies, the market has run away with the term “edge computing” without clear boundaries around what it constitutes in IIoT deployments. “Fake” edge solutions claim they can process data at the edge, but really rely on sending data back to the cloud for batch or micro batch processing. When reading about edge computing, the fakes are recognized as those without a complex event processor (CEP), which means latency is higher and the data remains “dirty,” making analytics much less accurate and machine learning (ML) models are significantly compromised.
“Real” edge intelligence starts with a hyper-efficient CEP that cleanses, normalizes, filters, contextualizes and aligns “dirty” or raw streaming industrial data as it’s produced. In addition, a “real” edge solution includes integrated ML and AI capabilities, all embedded into the smallest (and largest) compute footprints. The CEP function should enable real-time, actionable analytics onsite at the industrial edge, with a user experience optimized for fast remediation by operational technology (OT) personnel. It also prepares the data for optimal ML/AI performance, generating the highest quality predictive insights to drive asset performance and process improvements.
Real edge intelligence can yield enormous cost savings, as well as improved efficiencies and data insights for industrial organizations looking to embark on a true path toward digital transformation.
ML/AI models get skinny with edgification. Moving ML to the edge is not simply a matter of changing where the processing happens. The majority of ML models in use today were designed with the assumption of cloud computing capacity, run time and compute. Since these assumptions do not hold true at the edge, ML models must be adapted for the new environment. In other words, they need to be “edge-ified”. In 2019, “real edge” solutions will enable relocating the data pre- and post-processing from the ML models to a complex event processor, shrinking them by up to 80% and enabling the models to be pushed much closer to the data source. This process is called edgification, which will drive adoption of more powerful edge computing and IIoT applications overall.
Closed-loop edge to cloud machine learning will become a true operational solution. As ML and AI algorithms become “edgified” for use close to sensors or within IoT gateways or other industrial compute options, new best practices will emerge on how to train and further iterate on these models. What industrial organizations will find is that edge devices generating analytics on live streaming data (including audio and video) should regularly send insights back to the cloud, but only those that represent anomalous activity warranting a shift in the core algorithms. These edge insights enhance the model, significantly improving its predictive capabilities. The tuned models are then pushed back to the end in a constant closed loop, reacting quickly to changing conditions and specifications, and generating much higher quality predictive insights to improve asset performance and process improvements.
Production IIoT applications will go into implementation only with edge computing solutions supporting multi- and hybrid-cloud deployments. Hybrid- and multi-cloud solutions will dominate the industrial IoT deployments – a recent report found that the hybrid-cloud market will reach $97.64B USD by 2023. As industrial organizations look to bring multi-cloud environments together to provide more cost-effective approaches and flexibility, it will be important for edge solutions to be cloud agnostic. Vendor-exclusive solutions will likely begin to fall by the wayside as companies look for more flexibility and freedom of choice when building their edge-to-cloud environments. Google, AWS, Microsoft, C3IoT, Uptake and other leading cloud providers will establish more collaborative partnerships with edge computing companies to help businesses as they continue to improve and expand their offerings.
IoT video and audio sensors take off, driving the need for deep learning at the edge. There is industry-wide excitement about the capabilities that audio and video sensors can bring to the IIoT. Edge computing technology can play an important role in the further deployment of audio and video data in commercial and industrial IoT systems. The fusing of asset data with audio and video analytics will allow for faster and more accurate device and machine maintenance (including updates on systems health and more), and a whole host of new innovative applications. One such example of the video analytics is the use of flare monitoring at oil and gas operations to track environmental compliance and flare state remotely for large volumes of flare stack towers.
Predictive maintenance gives way to prescriptive maintenance. One of the big promises IIoT edge solutions deliver is predictive maintenance, offering insight into what is likely to happen to a connected asset (like manufacturing equipment or an oil rig) in the future. While many organizations still lag in implementing predictive maintenance as a first step, even more advanced technology will be available to early adopters in 2019.
Prescriptive maintenance is a step forward to enable businesses to not only predict problems but also produce outcome-focused recommendations for operations and maintenance using data analytics.
For example, elevator manufacturers want to put an end to routine problems, such as friction in elevator doors. As part of this effort, they partner with Foghorn to create a predictive maintenance solution. By analyzing sensor data at the source, they can now determine maintenance needs well in advance, without the cost, latency, security and other issues associated with the transfer of large amounts of data outside of the building. Thus, it can schedule service before anomalies impact performance in a highly efficient manner. As prescriptive maintenance becomes available, before the manufacturers roll a truck to provide maintenance on an elevator, they will have data available to suggest areas most likely to need repairs and have verified the repair staff person the expertise, tools and parts available for the repair.
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