Mobility for Sensing Uncertainty Reduction in Sensor Networks

Опубликовано 6 сентября 2016, 6:18

One of the major design concerns in sensor networks is their sensing performance. This talk considers the problem of discovering desirable network configurations in a distributed manner for improving the sensing performance of a sensor network. Mobility is used as a means of adapting network configuration. Mobility itself may have a high resource overhead, and hence a constrained form of mobility is exploited, which has low overheads but provides significant reconfiguration potential. An architecture that allows each node in the network to learn the medium and phenomenon characteristics is presented. A quantitative metric for sensing performance is defined based on real sensor and medium characteristics, rather than on abstract range based models. The problem of determining the desirable network configuration is expressed as an optimization of this metric. A distributed optimization algorithm is developed to compute a desirable network configuration and adapt it to environmental changes. The relationship of the proposed algorithm to simulated annealing and incremental sub-gradient descent based methods is discussed. A key property of the proposed algorithm is that convergence to a desirable configuration can be proved even though no global coordination is involved. A network protocol to implement this algorithm is discussed, followed by simulations and experiments on a prototype system.