Abstract

Distributed algorithm design in wireless sensor networks typically involves creating solutions which are tailored towards a particular problem. It is often not clear which parameters to adjust in order to apply the same algorithm to a slightly different scenario. As an attempt to address this problem, I present a method in which distributed controllers can be automatically created in response to a user’s requirements. Inspired by the way in which cells alter their behaviour in response to diffused protein concentrations, an abstract representation, termed a discrete Gene Regulatory Network (dGRN), is introduced. Each node runs an identical dGRN controller which controls node activity and interaction. The controllers are authored automatically using an evolutionary algorithm. The communication that occurs between nodes is neither specified or designed, but emerges naturally. I will present some simple examples demonstrating how this method works.

As a particular example, I show that the dGRN approach can generate effective strategies for nodes to cooperatively track a moving target. The obtained strategies vary according to the user’s accuracy requirements and the speed of the target, and are similar to those which would be expected from a network engineer. I also show that this resulting controllers can be executed on standard sensor nodes (T-mote SKY ). The dGRN framework thus greatly reduces the amount of effort involved in adjusting a network’s operation for a particular scenario, by evolving application-specific sensor network controllers.

Bio: Andrew is a postdoc in the Sensor Network group, Oxford University Computing Lab, working on the interdisciplinary WildSensing project (along with Cambridge Computing Lab and Oxford’s Wildlife Conservation Research Unit) which monitors badgers above and below ground using a variety of technologies. Prior to joining the comlab, he completed his PhD and BSc in electrical engineering at the University of Cape Town, South Africa, graduating with first class honours. He was the winner of the SAIEEE national student paper competition for his work on underwater wireless sensor networks. Andrew works on problems that lie on the boundary between the natural world and embedded technology, ranging from tagging penguins on remote islands to his more recent work on tracking badgers in woodland.