Distributed probabilistic routing for sensor network lifetime optimization

Abstract

A probabilistic and distributed routing approach for multi-hop sensor network lifetime optimization is presented in this paper. In particular, each sensor self-adjusts their routing probabilities locally to their forwarders based on its neighborhood knowledge, while aiming at optimizing the overall network lifetime (defined as the elapsed time before the first node runs out of energy). The theoretical feasibility and a practical routing algorithm are presented. Specifically, a sufficient distributed condition regarding the neighborhood state for distributed probabilistic routing to achieve the optimal network lifetime is presented theoretically. Based on it, a distributed adaptive probabilistic routing (DAPR) algorithm, which considered both the transmission scheduling and the routing probability evolvement is developed. We prove quantitatively that DAPR could lead the routing probabilities of the distributed sensors to converge to an optimal state which optimizes the network lifetime. Further, when network dynamics happen, such as topology changes, DAPR can adjust the routing probabilities quickly to converge to a new state for optimizing the remained network lifetime. We presented the convergence speed of DAPR. Extensive simulations verified its convergence and near-optimal properties. The results also showed its quick adaptation to both the network topology and data rate dynamics.

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“…Erdun et al in [40] proposed a solution for calculating the probability values depending on the distance of the nodes from the sink and the number of sensors in adjacent slices for clustered wireless sensor networks. Wang and Tan in [41] presented a Distributed Adaptive Probabilistic Routing algorithm (DAPR). The sensors can self adjust their routing probability values locally to their next hop forwarder based on their neighborhood information and converge to an optimal value.…”