The IoT of tomorrow

While today's IoT innovations continue to push the envelope identifying and establishing new relationships between objects, systems, and people, our imaginations continuously dream up new capabilities to solve problems at unprecedented scale. When we apply our imaginative prowess, the promises of the IoT becomes boundless. Today, we are barely scratching the surface.

The computer-to-device and device-to-device IoT is poised for staggering growth today and over the coming years, but how will its future security depend on what we do today? Cognition and autonomy research provide us a valuable glimpse into the IoT of tomorrow.

Autonomous systems

The IoT connectivity layer is starting to enable the introduction of pervasive autonomy. We are already seeing how this works in the consumer space, with integrations between vehicles and smart homes as an example. New research in both academia and industry are pushing autonomous systems and capabilities even further. Swarms of drones can work together with no human intervention. Machines can independently process and settle transactions between each other. Self-Driving Vehicles (SDVs) can form platoons that coordinate among themselves on the road. These are just a few examples of the coming age of autonomy.

Different types of autonomous vehicles (cars, drones, ships, and so on) take input from distributed sensors that might include cameras, LIDAR, RADAR, Global Positioning System (GPS), and even intertial measurements. These inputs are transmitted to fusion systems and then processed through navigation, guidance, and mission subsystems, which are integrated with propulsion and other platform sub-systems. Autonomy algorithms that might be employed in a system such as this include sense and avoid, pattern detection, object identifications, vector determination, and collision predictions. 

Machine Learning (ML) is used heavily within autonomous systems. ML algorithms learn over time by training on large datasets. A critical research area for IoT ML is associated with the use of adversarial examples that can train systems to identify malicious inputs into the algorithms. For example, research has shown that it is possible to slightly alter images to fool ML models into thinking that something is not what it really is. Injecting adversarial examples into the ML process can help prepare algorithms to identify and react to attempted abuse.

Cognitive systems

Over a decade ago, Duke University researchers demonstrated cognitive control of a robotic arm by translating neural control signals from electrodes embedded into the parietal and frontal cortex lobes of a monkey's brain. The researchers converted the brain signals into motor servo actuator input. These inputs allowed the monkey—through initial training on a joystick—to control a non-biological, robotic arm using only visual feedback to adjust its own motor-driving thoughts. So-called Brain Computer Interfaces (BCI), or Brain Machine Interfaces (BMI), continue to be advanced by Dr. Miguel Nocolelis' Duke laboratory and others. The technology promises a future in which neuroprosthetics allow debilitated individuals to regain physical function by wearing and controlling robotic systems merely by thought. Research has also demonstrated brain-to-brain functioning, allowing distributed, cognitive problem-solving through brainlets.

Digital conversion of brain-sensed (via neuro encephalography) signals allows the cognition-ready data to be conveyed over data buses, IP networks, and, yes, even the internet. In terms of the IoT, this type of cognitive research implies a future in which some types of smart devices will be smart because there is a human or other type of brain controlling or receiving signals from it across a BMI. Or the human brain is made hyperaware by providing it sensor feeds from sensors located thousands of kilometers away. Imagine a pilot flying a drone as though it were an extension of his body, but the pilot has no joystick. Using only thought signals (controls) and feedback (feeling) conveyed over a communications link, all necessary flight maneuvers and adjustments can be made. Imagine the aircraft's airspeed, as measured by its pitot tube, conveyed in digital form to the pilot's BMI interface and the pilot feeling the speed like wind blowing across his skin. That future of the IoT is not as far off as it may seem.

Now imagine what type of IoT security may be needed in such cognitive systems where the things are human brains and dynamic physical systems. How would one authenticate a human brain, for example, to a device, or authenticate the device back to the brain? What would digital integrity losses entail with the BMI? What could happen if outgoing or incoming signals were spoofed, corrupted, or manipulated in timing and availability? The overarching benefits of today's IoT, as large as they are, are small when we consider such future systems and what they mean to the human race. So too are the threats and risks.