Decentralised control of adaptive sampling and routing in wireless visual sensor networks

The efficient management of the limited energy resources of a wireless visual sensor network is central to its successful operation. Within this context, this paper focuses on the inter-dependent adaptive sampling and routing actions of each node in order to maximise the information value of the data collected. Thus, we develop two optimal decentralised algorithms to solve this distributed constraint optimization problem. The first assumes fixed routing and works in tree-structured networks. The second works in networks with any topologies by using a flexible routing approach. The two algorithms represent a trade-off in optimality, communication cost, and processing time. In an empirical evaluation on loopy sensor networks, we show that the flexible routing algorithm is able to deliver approximately twice the quantity of information compared to the fixed routing algorithm, where an arbitrary choice of route is made. However, this gain comes at a considerable communication and computational cost (increasing both by a factor of 100 times).