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

作　　者：Jinghui Zhong Zhixing Huang Liang Feng Wan Du Ying Li 

机构地区：[1]School of Computer Science and Engineering,South China University of Technology,Guangzhou 510006,China [2]College of Computer Science,Chongqing University,Chongqing 400044,China [3]Department of Computer Science and Engineering,School of Engineering,The University of California,Merced USA [4]Binxun Technology Co.,Ltd,Shenzhen 518000,China

出　　处：《IEEE/CAA Journal of Automatica Sinica》2020年第1期223-236,共14页自动化学报（英文版）

基　　金：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);the Fundamental Research Funds for the Central Universities(D2191200)

摘　　要：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.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.

关 键 词：Genetic programming HYPER-HEURISTICS static and dynamic networks wireless sensor networks(WSNs) 

分 类 号：TP212.9[自动化与计算机技术—检测技术与自动化装置] TN929.5[自动化与计算机技术—控制科学与工程]