Prof. Peng Guo Profile

Prof. Peng Guo

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Proceedings Article | 20 October 2023 Paper

Efficient routing algorithms for maximizing network lifetime in chain-type wireless sensor networks using multiple sinks

Renjie Guo, Honglie Li, Mi Yan, Yiyi Zhang, Peng Guo

Proceedings Volume 12814, 128140I (2023) https://doi.org/10.1117/12.3010419

KEYWORDS: Sensors, Sensor networks, Data transmission, Head, Data communications, Computer simulations, Environmental monitoring, Telecommunications, Systems modeling, Mathematical optimization

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Data gathering is an important research area in wireless sensor networks (WSNs), where network lifetime is an important factor affecting data gathering in WSNs. In recent years, a type of WSNs with a special structure that is fundamentally different from traditional WSNs has emerged in order to monitor infrastructure extending over hundreds of kilometers, because sensor nodes in such networks are deployed along narrow geographical areas, thus forming a network called chain-type WSNs. In order to maximize the lifetime of chain-type WSNs with multiple sink nodes, this paper proposes two routing algorithms: MMRE_EN, Chains_search. The case of data gathering without data aggregation is considered in the selection of sensor nodes, and this paper proposes an improved version of the existing MMRE algorithm: the MMRE_EN algorithm. Meanwhile, this paper proposes the Chains_search algorithm with linear computational complexity, whose core idea is to convert the problem of global route optimization into the problem of route independent optimization of multiple links. Extensive simulation results show that the two algorithms proposed in this paper can extend the lifetime of chain-type WSNs with multiple sink nodes by 18.5% and 12%, respectively, compared with the existing MMRE algorithm, while the Chains_search algorithm with linear computational complexity has only 1/200 of the running time of the MMRE algorithm.

Proceedings Article | 7 October 2016 Paper

S-TSP: a novel routing algorithm for In-network processing of recursive computation in wireless sensor networks

Tingfang Tang, Peng Guo, Xuefeng Liu

Proceedings Volume 9902, 99020A (2016) https://doi.org/10.1117/12.2262052

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In-network processing is an efficient way to reduce the transmission cost in wireless sensor networks (WSNs). The in-network processing of many domain-specific computation tasks in WSNs usually requires to losslessly distribute the computation of the tasks into the sensor nodes, which is however usually not easy. In this paper we are concerned with such kind of tasks whose computation can only be partitioned into recursive computation mode. To distribute the recursive computations into WSNs, it is required to design an appropriate single in-network processing path, along which the intermediate data is forwarded and updated in the WSNs. We address the recursive computation with constant size of computation result, e.g., distributed least square estimation (D-LSE). Finding the optimal in-network processing path to minimize the total transmission cost in WSNs, is a new problem and seldom studied before. To solve it, we propose a novel routing algorithm called as S-TSP, and compare it with some other greedy algorithms. Extensive simulations are conducted, and the results show the good performance of the proposed S-TSP algorithm.

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