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
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December 2015