Multi-objective Sink Nodes Coverage Algorithm Based on Quantum Wolf Pack Evolution

JIN Shan; JIN Zhigang

doi:10.11999/JEIT160693

Volume 39 Issue 5

May 2017

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Article Navigation > Journal of Electronics & Information Technology > 2017 > 39(5): 1178-1184

JIN Shan, JIN Zhigang. Multi-objective Sink Nodes Coverage Algorithm Based on Quantum Wolf Pack Evolution[J]. Journal of Electronics & Information Technology, 2017, 39(5): 1178-1184. doi: 10.11999/JEIT160693

Citation:

JIN Shan, JIN Zhigang. Multi-objective Sink Nodes Coverage Algorithm Based on Quantum Wolf Pack Evolution[J]. Journal of Electronics & Information Technology, 2017, 39(5): 1178-1184. doi: 10.11999/JEIT160693

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Multi-objective Sink Nodes Coverage Algorithm Based on Quantum Wolf Pack Evolution

doi: 10.11999/JEIT160693

JIN Shan¹,
JIN Zhigang²

1.
2.
(Electrical, Automation and Information Engineering College, Tianjin University, Tianjin 300072, China)

Funds:

The National Natural Science Foundation of China (61571318), The Qinghai Province Science and Technology Program (2015-ZJ-904), The Hainan Province Science and Technology Program (ZDYF2016153)

Received Date: 2016-07-04
Rev Recd Date: 2016-12-09
Publish Date: 2017-05-19

Abstract

Abstract

Satisfying non-repeated coverage, connectedness, and energy balance of sink layer are critical problems in multi-layers Wireless Sensor Networks (WSNs). They are overall planed as a Multi-objective Optimization Problem (MOP). For resolving it, the Quantum Wolf Pack Evolutionary Algorithm (QWPEA) is proposed, which actualizes the Candidate Leader Wolf (CLW) selection, sliding mode crossing, quantum rotating gate, and NOR gate mutation are used to obtain the more accurate wolfs location. Simulation results show that QWPEA can minus the number of sink nodes, promote the steadiness, and balance the energy consumption in a huge scale of WSNs effectively. While 1000 sensors are deployed on 800 m800 m with QWPEA, the sink effective coverage ratio is higher than either MOPSO as 29.55% or NSGA-II as 25.93%. And the sink communication energy consumption ratio is higher than the latter two methods as 15.27% and 18.63% separately. Also, the sink occupied ratio is lower than them as 14.01% and 15.46% severally.
- Wireless sensor networks (WSNs),
- Quantum Wolf Pack Evolutionary Algorithm (QWPEA),
- Coverage,
- Multi-objective,
- Sink node

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References(16)

References

罗旭, 柴利, 杨君. 异构传感器网络多目标多重覆盖策略[J]. 电子与信息学报, 2014, 36(3): 690-695. doi: 10.3724/SP. J.1146.2013.00667.

LUO Xu, CHAI Li, and YANG Jun. Multi-objective strategy of multiple coverage in heterogeneous sensor networks[J]. Journal of Electronics Information Technology, 2014, 36(3): 690-695. doi: 10.3724/SP.J.1146.2013.00667.

ZHU Yanmin, XUE Cuiyao, CAI Haibin, et al. On deploying relays for connected indoor sensor networks[J]. Journal of Communications and Networks, 2014, 16(3): 335-343. doi: 10.1109/JCN.2014.000054.

ARIVUDAINAMBI D, SREEKANTH G, and BALAJI S. Energy efficient sensor scheduling for target coverage in wireless sensor network[C]. International Conference on Wireless Communications, Networking and Applications (WCNA), Shenzhen, China, 2014: 693-705. doi: 10.1007/ 978-81-322-2580-5_62.

TIAN Jingwen, GAO Meijuan, and GE Guangshuang. Wireless sensor network node optimal coverage based on improved genetic algorithm and binary ant colony algorithm [J]. Eurasip Journal on Wireless Communications and Networking, 2016, 2016(1): 1-11. doi: 10.1186/s13638-016- 0605-5.

OZDEMIR S, ATTEA B A, and KHALIL O A. Multi-objective evolutionary algorithm based on decomposition for energy efficient coverage in wireless sensor networks[J]. Wireless Personal Communications, 2013, 71(1): 195-215. doi: 10.1007/s11277-012-0811-3.

CHEN Zhi, LI Shuai, and YUE Wenjing. Memetic algorithm- based multi-objective coverage optimization for wireless sensor networks[J]. Sensors, 2014, 14(11): 20500-20518. doi: 10.3390/s141120500.

李旭, 尹华锐, 卫国. 区域覆盖下的最优中继部署与功率分配[J]. 电子与信息学报, 2015, 37(10): 2446-2451. doi: 10.11999/ JEIT141444.

LI Xu, YIN Huarui, and WEI Guo. Optimal relay deployment and power allocation for extending wireless coverage[J]. Journal of Electronics Information Technology, 2015, 37(10): 2446-2451. doi: 10.11999/JEIT141444.

HE Yong, DENG Yun, and LUO Mingxing. The improved evolution paths to speedup quantum evolution[J]. International Journal of Theoretical Physics, 2016, 55(4): 1977-1987. doi: 10.1007/s10773-015-2838-1.

吴虎胜, 张凤鸣, 战仁军, 等. 利用改进的二进制狼群算法求解多维背包问题[J]. 系统工程与电子技术, 2015, 37(5):

WU Husheng, ZHANG Fengming, ZHAN Renjun, et al. Improved binary wolf pack algorithm for solving multidimensional knapsack problem[J]. Systems Engineering and Electronics, 2015, 37(5): 1084-1091. doi: 10.3969/ =j.issn. 1001-506X.2015.05.17.

ZHAO Zhijin, PENG Zhen, ZHENG Shilian, et al. Cognitive radio spectrum allocation using evolutionary algorithms[J]. IEEE Transactions on Wireless Communications, 2009, 8(9): 4421-4425. doi: 10.1109/TWC.2009.080939.

COELLO C A C and LECHUGA M S. MOPSO: A proposal for multiple objective particle swarm optimization[C]. IEEE World Congress on Computational Intelligence (WCCI2002), Honolulu, USA, 2002: 1051-1056.

DEB K, PRATAP A, AGARWAL S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197. doi: 10.1109/4235.996017.

-1091. doi: 10.3969/j.issn.1001-506X.2015.05.17.

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