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Mobile Networks and Applications

, Volume 24, Issue 6, pp 1975–1984 | Cite as

Healing Coverage Holes for Big Data Collection in Large-Scale Wireless Sensor Networks

  • Jie Feng
  • Hongbin ChenEmail author
Article
  • 93 Downloads

Abstract

The quality of service is severely degraded by coverage holes in wireless sensor networks. This paper focuses on the coverage hole healing (CHH) problem for big data collection in a large-scale wireless sensor network (LS-WSN) where the LS-WSN containing both static sensors and mobile sensors with the topology control of LEACH algorithm. Meanwhile, the data volume transmitted by each sensor node may be inconsistent. Specifically, the target of the CHH problem is to find an optimal subset of mobile nodes from all mobile nodes while maximizing the transmission times (TT) that all dispatched mobile nodes can transmit in their lifetime. Hence, from the data-centric perspective, we propose a greedy healing algorithm (GHA) via the greedy-based heuristic strategy with low computational complexity to solve this CHH problem. Simulation results show that the proposed GHA can efficiently heal the coverage holes which significantly prolongs the network lifetime and observably enhances the quality of service (QoS) of WSNs while increasing the TT, transmitted data volume (TDV) and average residual energy of all dispatched mobile nodes.

Keywords

Coverage holes Large-scale wireless sensor networks Big data Coverage hole healing Greedy healing algorithm 

Notes

Acknowledgments

This research was supported by the National Natural Science Foundation of China (61671165, 6176060053), the Guangxi Natural Science Foundation (2016GXNSFGA380009), the Fund of Key Laboratory of Cognitive Radio and Information Processing (Guilin University of Electronic Technology), Ministry of Education, China and the Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing (CRKL170101), and the Innovation Project of GUET Graduate Education (2017YJCX27).

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Key Laboratory of Cognitive Radio and Information ProcessingGuilin University of Electronic TechnologyGuilinChina

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