Journal
IEEE INTERNET OF THINGS JOURNAL
Volume 7, Issue 11, Pages 11223-11237Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2020.2996514
Keywords
Internet of Things; Genetic algorithms; Wireless sensor networks; Optimization; Heuristic algorithms; Computational modeling; Peer-to-peer computing; Area coverage problem; genetic algorithm (GA); SIL deployment problem (SILDP); solar insecticidal lamps Internet of Things (SIL-IoTs); wireless sensor networks (WSNs)
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Funding
- Research Start-Up Fund for Talent Introduction of Nanjing Agricultural University [77H0603]
- International Cooperation Program of Nanjing Agricultural University [2018-AF-23]
- National Natural Science Foundation of China [61902188]
- Science and Technology Innovation Project of Xuzhou [KC18004]
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Solar insecticidal lamp (SIL) is a green prevention and control technology for pests. With the development of wireless sensor networks (WSNs), the combination of SILs and WSNs forms a novel agricultural Internet of Things-SIL Internet of Things (SIL-IoTs). However, the complex geographical characteristic of actual farmland has a great impact on SIL deployment. In this article, we study the SIL deployment problem (SILDP) with characteristics of full coverage, penetrable obstacles, irregular boundary, and partition structure. According to the partition structure caused by natural physiognomy feature, the actual farmland is divided into many subareas by ridges, and each subarea can be considered as a separate partition. Then, we formulate the SILDP in the scenario with the partition structure as the quadratic assignment problem. After that, we propose two deployment methods based on the genetic algorithm to address the SILDP. These two methods are the same in optimization objectives, but different in deployment sequence. The experimental results show that the proposed deployment methods equips better performance in terms of deployment cost compared with the other six peer algorithms.
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