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Modeling and extending lifetime of wireless sensor networks using genetic algorithm

Published: 12 June 2009 Publication History

Abstract

To extend the lifetime of the sensor networks as far as possible while maintaining the quality of network coverage is a major concern in the research of coverage control. A systematical analysis on the relationship between the network lifetime and cover sets alternation is given, and by introducing the concept of time weight factor, the network lifetime maximization model is presented. Through the introduction of the solution granularity T, the network lifetime optimization problem is transformed into the maximization of cover sets. A solution based on NSGA-II is proposed. Compared with the previous method, which has the additional requirement that the cover sets being disjoint and results in a large number of unused nodes, our algorithm allows the sensors to participate in multiple cover sets, and thus makes fuller use of the whole sensor nodes to further increase the network lifetime. Simulation results are presented to verify these approaches.

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  • (2023)Genetic Algorithm with Heuristic Mutation for Wireless Sensor Network OptimizationAdvances in Intelligent Networking and Collaborative Systems10.1007/978-3-031-40971-4_17(177-189)Online publication date: 29-Aug-2023
  • (2019)Extending K-cover genetic algorithm for efficient energy consumption in WSNs2019 18th RoEduNet Conference: Networking in Education and Research (RoEduNet)10.1109/ROEDUNET.2019.8909581(1-6)Online publication date: Oct-2019
  • (2017)An Evolutionary Graph-Based Approach for Managing Self-Organized IoT NetworksProceedings of the 15th ACM International Symposium on Mobility Management and Wireless Access10.1145/3132062.3132068(113-119)Online publication date: 21-Nov-2017
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cover image ACM Conferences
GEC '09: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
June 2009
1112 pages
ISBN:9781605583266
DOI:10.1145/1543834
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 12 June 2009

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Author Tags

  1. coverage
  2. genetic algorithm
  3. network lifetime
  4. solution granularity
  5. wireless sensor network

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Cited By

View all
  • (2023)Genetic Algorithm with Heuristic Mutation for Wireless Sensor Network OptimizationAdvances in Intelligent Networking and Collaborative Systems10.1007/978-3-031-40971-4_17(177-189)Online publication date: 29-Aug-2023
  • (2019)Extending K-cover genetic algorithm for efficient energy consumption in WSNs2019 18th RoEduNet Conference: Networking in Education and Research (RoEduNet)10.1109/ROEDUNET.2019.8909581(1-6)Online publication date: Oct-2019
  • (2017)An Evolutionary Graph-Based Approach for Managing Self-Organized IoT NetworksProceedings of the 15th ACM International Symposium on Mobility Management and Wireless Access10.1145/3132062.3132068(113-119)Online publication date: 21-Nov-2017
  • (2016)Metaheuristics for the Lifetime of WSN: A ReviewIEEE Sensors Journal10.1109/JSEN.2016.252306116:9(2812-2831)Online publication date: May-2016
  • (2016)Optimization of sensor deployment using multi-objective evolutionary algorithmsJournal of Reliable Intelligent Environments10.1007/s40860-016-0030-x2:4(209-220)Online publication date: 18-Oct-2016
  • (2015)Evolutionary-Based Wireless Sensor Deployment for Target CoverageProceedings of the 2015 11th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)10.1109/SITIS.2015.62(739-745)Online publication date: 23-Nov-2015
  • (2015)Review of nature-inspired methods for wake-up scheduling in wireless sensor networksSwarm and Evolutionary Computation10.1016/j.swevo.2015.07.00725(100-118)Online publication date: Dec-2015
  • (2013)ReferencesWireless Ad Hoc and Sensor Networks10.1201/b15325-17(321-341)Online publication date: 22-Jul-2013
  • (2012)Solving coverage problem in three-dimensional area using Wireless Visual Sensor NetworksProceedings of 2012 2nd International Conference on Computer Science and Network Technology10.1109/ICCSNT.2012.6526159(1294-1298)Online publication date: Dec-2012
  • (2011)A Target Coverage Scheduling Scheme Based on Genetic Algorithms in Directional Sensor NetworksSensors10.3390/s11020188811:2(1888-1906)Online publication date: 1-Feb-2011
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