What Is Cognitive IoT?

The IoT market is expected to be worth well over 450 billion dollars by 2020, rising at a 30 percent annual rate, with Smart Cities, HealthCare, Manufacturing, and Smart Homes accounting for more than 75 percent of the market.

Two major technologies, “Resilient IoT Systems” and “Cognitive IoT Systems,” appear to be driving the next generation of IoT. While Resilient IoT systems will arise naturally as IoT technology advances, Cognitive IoT Systems will have the ability to disrupt the industry via a revolutionary union of IoT Sensing and Artificial Intelligence.

What is Cognitive IoT and Why is it Important?

Cognitive IoT is defined as an IoT system that incorporates elements of human cognitive activities into the system design to achieve diverse levels of autonomy in perception, analysis, and actuation, as well as self-x capabilities such as learning, resilience, optimization, re-configuration, and management, with substantial human context-awareness.

What does it mean to have a cognitive Internet of Things (C-IoT) system?

A C-IoT system is expected to identify system component failures and reconfigure itself to allow graceful degradation via self-healing. If a sensor fails, the system should investigate whether it can be rectified using a soft-sensor or by inferring from the remaining sensor data.

When a security breach is detected on a section of the network, appropriate countermeasures must be implemented, such as changing security keys, isolating the compromised network branch, or notifying a human operator, to make the system impervious to attacks. Cognitive IoT intelligence should be reflected in the ways it interacts with humans. Humans’ five senses could be used to interact with humans.

Outstanding Use Cases

Let’s have a look at a few fascinating C-IoT examples.

The first application we’ll look at is for industrial safety. Tracking the vital signs of workers in a dangerous environment and responding immediately to any unusual events can be important and even lifesaving for workers’ protection.

This can be accomplished by the employee wearing a smartwatch which may continuously track activity and heartbeats and relay the data to a back-end. A back-end cognitive analytics engine can perform knowledge-driven assessments on the collected data and raise appropriate warnings as needed.

To create the necessary alarms, the cognitive engine can seek for recognizing events such as immobility, sudden fall, or significant variations in heartbeats.

The second example is a factory-based IoT-driven resilient process control system. In this Industrial Age, many factory machines are equipped with sensors that transfer data to a backend via an IoT-driven framework for analytics and actionable insights.

In the event of sensor failure, a C-IoT implementation of such a system can automatically transition to other sensor data or synthesize soft sensor outputs incorporating other sensor data and metadata.

In the event that the factory wireless network is disrupted, such a system can also construct alternative networking paths and/or process most of the information at the network edge to reduce bandwidth utilization and relax latency restrictions. In both scenarios, the cognitive engine can aid in strengthening the IoT system’s resilience and reliability.

Our third scenario is of a drone assessing a crucial outdoor facility. For utility firms with extensive electrical and water distribution systems, this is a serious issue. Take, for example, an energy company that wants to assess the structural stability of its powerline transmission towers as part of standard maintenance or as a health inspection after a disaster such as a hurricane.

It takes a long time and is dangerous to send individuals to accomplish this job. Instead, we can have a driverless drone that can independently go to the transmission tower by tracing the powerlines and processing the images captured by its downward-facing camera (such sensor-driven automated navigation qualifies it as a cognitive IoT system). It may use camera vision to evaluate the tower from an altitude and look for high-level anomalies.

If it detects an issue (for example, a damaged insulator cap on a high-tension line), it can go nearer to that area of the tower, zoom in, analyze the damage, and send real-time results to the back office, which can dispatch a professional to repair it. To ensure that it can maximize its mission objectives, the drone may even self-monitor its resources (such as battery life) or on-board defects.