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Optimization of Multi-function Sensor Placement Satisfying Detection Coverage

  • Qingzhong Liang
  • Yuanyuan FanEmail author
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 221)

Abstract

Wireless Sensor Networks (WSNs) have become essential parts in Industrial Internet of Things (IIoT). However, owing to the type associated with data acquisition and the large scale of monitoring, sensors are often installed at a lot of locations and a wide variety of sensors make WSN topology more complex. To address these limitations, a complementary promising solution, known as software defined wireless sensor network (SDWSN), is proposed. SDWSN acquires desired information based on users’ demands from large-scale sensor networks by dynamically customizing its function. Thanks to the SDWSN, multi-type data sensing is able to enlarge the sensing scale and reduce the cost. Existing sensor placement techniques are usually focus on simple function sensor or multi-type sensor. Witness the development of SDWSN, it is ideal to explore such abilities such that the multi-type sensing functions can be conducted in a same node. Because each area covered by different multi-function sensor nodes has different detection requirements, multi-function sensor nodes placement faces many challenges. In this paper, based on multi-objective decomposition, we study the number and function redundancy of all nodes minimization problem in multi-function sensor nodes placement. Specially, we propose an improved MOEA/D-DE algorithms based on orthogonal experiment design. Simulation and evaluations validate the efficiency of our proposal.

Keywords

WSN Placement Multi-objective Optimization 

Notes

Acknowledgment

This research was supported by the NSF of China (Grant No. 61673354, 61672474, 61402425, 61272470, 61305087, 61440060, 61501412), the Provincial Natural Science Foundation of Hubei (Grant No. 2015CFA065). This paper has been subjected to Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan 430074, China. It was also supported by Open Research Project of The Hubei Key Laboratory of Intelligent Geo-Information Processing (KLIGIP201603 and KLIGIP201607).

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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  1. 1.School of Computer ScienceChina University of GeosciencesWuhanChina
  2. 2.Hubei Key Laboratory of Intelligent Geo-Information ProcessingWuhanChina

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