Networked and Embedded Systems Laboratory

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Motion may be used in sensor networks to change the network configuration for improving the sensing performance. We consider the problem of controlling motion in a distributed manner for a mobile sensor network for a specific form of motion capability. Mobility itself may have a high resource overhead, hence we exploit a constrained form of mobility which has very low overheads but provides significant reconfiguration potential. We present an architecture which allows each node in the network to learn the medium and phenomenon characteristics. We describe a quantitative metric for sensing performance which is concretely tied to real sensor and medium characteristics, rather than assuming an abstract range based model. The problem of determining
the desirable network configuration is expressed as an optimization of this metric. We present a distributed optimization algorithm which computes a desirable network configuration, and adapts 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 our 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 laboratory test-bed.

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