Disaster zones could soon be salvaged by teams of smart devices – here’s how

Making this ecosystem work in unison requires several levels of organization. At one level, each class of devices needs to interact. The bandwidth of the temporary communications network will probably be very limited, for example, creating the complex problem of deciding which transmissions to prioritize and how to route them efficiently through the network.

The broader network also has to organize, responding to new events as appropriate. A relatively simple current example is this online flooding platform in Jakarta. It pools information for inhabitants based on feeds from a network of street sensors and people on smartphones.

To build a more complex system and make it run smoothly, it becomes harder and harder to rigidly pre-engineer software: no centralized control is possible, especially in a wide disaster area where communications between devices are too slow or multi-layered.

Great inspirations
Software engineers have yet to develop such a system – despite significant advances in algorithms. Our best pre-programmed efforts tend to quickly unravel, coming up against unforeseen variables. Improving these systems is challenging to say the least.

Neither is it just an issue for disaster areas. The world is increasingly instrumented with interconnected devices, each running with some form of algorithmic intelligence. They are managing city traffic light systems, for example. They are managing electricity supply and demand across the grid.

Some programmers have looked beyond computer science to improve such systems. One inspiration is the American political economist Elinor Ostrom. Her Nobel Prize winning work identified how communities in places as diverse as Kenya, Guatemala, Turkey, Nepal and Los Angeles self-govern and share resources while leaving enough for future generations.

Ostrom discerned eight common characteristics, and derived principles that could be applied anywhere. One was that you must ensure those affected by the rules can participate in modifying them, for example. Another was the need for a system for monitoring community members’ behavior carried out by the members themselves.

The German-American philosopher Nicholas Rescher is also helping programmers. Rescher argued that when deciding how to distribute rewards/punishments fairly, seven “canons” should be taken into account: equality, need, ability, effort, productivity, social utility and supply and demand. The idea is to identify which canon is most appropriate in a given situation. Programmers are using these principles to help networks make judgements about allocating scarce resources, for example, and resolving conflicts between different devices.

Jeremy’s recent co-authored work has shown how Ostrom and Rescher’s ideas could be expressed as algorithms for managing device networks. The benefits have been demonstrated in a relatively static environment where the population and number of devices is generally stable or predictable – distributing energy and protecting against overloads within a local community, say. But in a disaster scenario where the number, location and availability of devices is continually changing and almost completely unpredictable, the model needs extending.

To solve this, the final piece of the jigsaw comes from biology, where different animals adapt to changes in their environment. Individual creatures learn over their lifetimes, while a species adapts over many generations through evolution – more successful traits becoming dominant while less successful ones are bred out.

Emma was involved, for example, in creating a system for robotic swarms that handle change far better than alternative approaches. It enables the robots both to “learn” from experience and adapt the parameters of their algorithm, and “evolve” completely new behaviors where the environment has changed so much that existing algorithms won’t suffice.

We recently outlined how these three strands from political theory, social science, and biology could be brought together to develop a new paradigm for complex device networks. We see encouraging signs that such thinking is starting to catch on among researchers.

These ideas should enable us to develop new approaches that will underpin and enhance a wide variety of human activities – not least when the next disaster strikes. It might even mitigate the effects of climate change, making us better at foreseeing catastrophes and taking steps to avert them.

Emma Hart is Chair in Natural Computation, Edinburgh Napier University. Jeremy Pitt is Professor of Intelligent and Self-Organizing Systems, Imperial College London. This article is published courtesy of The Conversation (under Creative Commons-Attribution / No derivative).