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Volume 7 Issue 1
Jan.  2020

IEEE/CAA Journal of Automatica Sinica

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Jinghui Zhong, Zhixing Huang, Liang Feng, Wan Du and Ying Li, "A Hyper-Heuristic Framework for Lifetime Maximization in Wireless Sensor Networks With A Mobile Sink," IEEE/CAA J. Autom. Sinica, vol. 7, no. 1, pp. 223-236, Jan. 2020. doi: 10.1109/JAS.2019.1911846
Citation: Jinghui Zhong, Zhixing Huang, Liang Feng, Wan Du and Ying Li, "A Hyper-Heuristic Framework for Lifetime Maximization in Wireless Sensor Networks With A Mobile Sink," IEEE/CAA J. Autom. Sinica, vol. 7, no. 1, pp. 223-236, Jan. 2020. doi: 10.1109/JAS.2019.1911846

A Hyper-Heuristic Framework for Lifetime Maximization in Wireless Sensor Networks With A Mobile Sink

doi: 10.1109/JAS.2019.1911846
Funds:  This work was supported by the National Natural Science Foundation of China (61602181,61876025), Program for Guangdong Introducing Innovative and Entrepreneurial Teams (2017ZT07X183), Guangdong Natural Science Foundation Research Team (2018B030312003), the Guangdong–Hong Kong Joint Innovation Platform (2018B050502006), and the Fundamental Research Funds for the Central Universities (D2191200)
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  • Maximizing the lifetime of wireless sensor networks (WSNs) is an important and challenging research problem. Properly scheduling the movements of mobile sinks to balance the energy consumption of wireless sensor network is one of the most effective approaches to prolong the lifetime of wireless sensor networks. However, the existing mobile sink scheduling methods either require a great amount of computational time or lack effectiveness in finding high-quality scheduling solutions. To address the above issues, this paper proposes a novel hyperheuristic framework, which can automatically construct high-level heuristics to schedule the sink movements and prolong the network lifetime. In the proposed framework, a set of low-level heuristics are defined as building blocks to construct high-level heuristics and a set of random networks with different features are designed for training. Further, a genetic programming algorithm is adopted to automatically evolve promising high-level heuristics based on the building blocks and the training networks. By using the genetic programming to evolve more effective heuristics and applying these heuristics in a greedy scheme, our proposed hyper-heuristic framework can prolong the network lifetime competitively with other methods, with small time consumption. A series of comprehensive experiments, including both static and dynamic networks, are designed. The simulation results have demonstrated that the proposed method can offer a very promising performance in terms of network lifetime and response time.

     

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    Highlights

    • A hyper-heuristic framework that designs heuristics to optimize wireless sensor networks is proposed.
    • The computer-designed heuristics are competitive with other algorithms in the term of network lifetime.
    • The heuristics designed by our method have a short response time (even in dynamic networks).

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