IIC Journal of Innovation | Page 13

Three Main Themes in the Industrial Internet of Things among them in proximity. This is the theme of collaborative Local Autonomy. In this regard, we have much to learn from the apparent global intelligence and resilience that emerges from swarm-like collaboration in schools of fish, flocks of birds, colonies of ants or bees in which the autonomous constituents execute only simple rules with peers in proximity. Along this line of thought, as we pursue the next level of effectiveness and efficiency in operations, we may want to reexamine our current automated systems. There may be opportunities to extend the level of flexibility and adaptability of these systems to deal with conditions unforeseen when the automation systems were created. Additionally, many systems, though largely automated, still involve a human-in-the-loop in the operation workflow. The need for a human-in-the-loop could be due to situations in which cognitive capability may be required to solve specific, complex problems. As we become more apt in applying cognitive capabilities in these systems, we may want to reduce the reliance on humans in operations to increase reliability, efficiency and safety. Think about the examples ranging from robotic aids to human workers in warehouses, underground autonomous mining machines and the well-publicized autonomous vehicles. In these autonomous systems, we elevate the human role from the operational level to the mission control level – setting objectives and handling exceptions. These systems would have an increasingly higher level of capability to learn and adapt – capable of extrapolating parameters possibly outside the range of the original test set, trying out solutions according to its risk evaluation and confidence level. In the process, they would learn and expand the range of the test set for safe operations, autonomously. As the level of autonomy grows at the asset level, we will improve resilience, security and safety of operations in the overall system. Each asset no longer always depends on the global network or system to continue its operations and at the same time becomes more capable to deal with and recover from localized adversary conditions making the operations more resilient. With more decision-making shifted to them, the intelligent assets now have a stronger ability to assess the validity of requests from the centralized system or from its peers so to accept only those compatible with its a priori objectives, making the operation more secure and safer. To build collaborative systems with distributed autonomy, in other words, to achieve distributed problem solving and decision-making, we need appropriate system architectures with the right world modeling, analytics and computation platforms, well-distributed and well-interconnected computation capabilities, many of them, closest to the physical assets. Fortunately, the continuing advances in computation and communication that have ushered us into the era of the Internet of Things are also enabling us to achieve distributed and collaborative autonomous problem solving. The greater embeddable computational capabilities, increasingly packaged in miniaturizing sizes, consuming less energy and available at lower cost, are enabling the execution– closer to the assets – of more advanced analytics and better modeling of the world. This will help to transition the assets from automation to autonomy. At the same time, the ubiquitous connectivity among the assets and from them to the broader systems is making it possible for the autonomous assets to collaborate with each other seamlessly within proximity - 12 - December 2015