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Typical Wireless Sensor Networks (WSNs) use static sinks to collect data from all sensor nodes via multihop forwarding. This results in the hot spot problem since the nodes close to the sink have a tendency to consume more energy in relaying data from other nodes. Many approaches using mobile sinks have been proposed to prevent this problem, but these approaches still suffer from the buffer overflow problem due to the limited memory capacity of the sensor nodes. This paper proposes an approach in which the mobile sink traverses a subset of nodes. Given the characteristics of wireless communication, such an approach can effectively alleviate the buffer overflow problem without incurring additional energy consumption. To further alleviate the buffer overflow problem, we propose the Allotment Mechanism which allows nodes with different data sampling rates to share their memory and, thus, extend the overflow deadline. Finally, the effectiveness of the proposed approach is verified via the GloMoSim network simulator. The results show that our approach incurs fewer buffer overflows than other data-gathering schemes.

An important scheme for extending sensor network lifetime is to divide sensor nodes into disjoint groups such that each group covers all targets and works alternatively. This scheme can be transformated to the Disjoint Set Covers (DSC) problem, which is proved to be NP-complete. Existing heuristic algorithms either get barely satisfactory solutions or take exponential time complexity. In this paper, we present a genetic algorithm to solve the DSC problem. Simulation results show that the proposed genetic algorithm can improve the most constrained-minimum constraining heuristic algorithm (MCMCC) in solution quality by 99% with only polynomial computation time complexity.

SUMMARY Since sensor networks integrate virtual and physical worlds, spatial deployment of sensor nodes may have a significant impact on operation costs and performance. This paper investigates the problem of redeploying mobile sensor networks, in which the objective is to design an efficient algorithm which optimizes the global performance of networks. We take a novel approach in analyzing the problem from a macroscopic perspective, modeling the sensor deployment as a density distribution and the redeployment algorithm as an integral transform of that distribution. Despite lacking the details for individual node, we may still derive insightful results, such as number of moves, stability, and transitional behavior. We also derive the bound of total moving distance difference between an arbitrary one‐move algorithm and the optimum redeployment algorithm, allowing for the estimation of the minimum moving distance from the initial deployment region. In summary, we present an theoretical analysis of the redeployment problem from a macroscopic perspective, and believe the results can serve as a basis for further algorithm development and analysis. Copyright © 2011 John Wiley & Sons, Ltd.

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