Agent-Based Decentralised Coordination for Sensor Networks using the Max-Sum Algorithm

In this paper, we consider the generic problem of how a network of physically distributed, computationally constrained devices can make coordinated decisions to maximise the effectiveness of the whole sensor network. In particular, we propose a new agent-based representation of the problem, based on the factor graph, and use state-of-the-art DCOP heuristics (i.e., DSA and the max-sum algorithm) to generate sub-optimal solutions. In more detail, we for-mally model a specific real-world problem where energy-harvesting sensors are deployed within an urban environment to detect vehicle movements. The sensors coordinate their sense/sleep schedules, maintaining energy neutral operation while maximising vehicle detection probabil-ity. We theoretically analyse the performance of the sensor network for various coordination strategies and show that by appropriately coordinating their schedules the sensors can achieve significantly improved system-wide performance, detecting up to 50 % of the events that a randomly coordinated network fails to detect. Finally, we deploy our coordination approach in a realistic simulation of our wide area surveillance problem, comparing its performance to a number of benchmarking coordination strategies. In this setting, our approach achieves up to a 57 % reduction in the number of missed vehicles (compared to an uncoordinated network). This performance is close to that achieved by a benchmark centralised algorithm (simulated annealing) and to a continuously powered network (which is an unreachable upper bound for any coordination approach)