Impact of Topology on Performance and Energy Efficiency in Wireless Sensor Networks for Source Extraction

In this paper, the problem of source extraction in bandwidth-constrained wireless sensor networks is considered. The sensor observations are assumed to be instantaneous linear mixtures of sources in a sensing field, possibly corrupted by noise. Because of the bandwidth constraint, the observations are quantized before being transmitted. Three kinds of sensor network topologies (cluster-based sensor network, sensor network with a fusion center, and concatenated sensor network) are considered in order to show the impact of topology on the performance and energy efficiency. The mixtures are reconstructed based on the received quantized data, and source extraction is performed by using a fast fixed-point algorithm with prewhitening. This algorithm was proposed by Hyvarinen and Oja, published in Neural Computation, and has the advantage of simplicity and fast convergence. The case of source extraction where the sensed data are undistorted is used as the performance benchmark. The results show that in all these sensor networks, the performance can be close to that of the benchmarking case. The energy efficiency and lifetime of these sensor networks are compared when the same source extraction task is executed. The effects of sensor failure and link failure on the performance are also discussed.