research-article

Ant colony optimization algorithm for lifetime maximization in wireless sensor network with mobile sink

Authors:

Jing-hui Zhong,

Jun ZhangAuthors Info & Claims

GECCO '12: Proceedings of the 14th annual conference on Genetic and evolutionary computation

Pages 1199 - 1204

https://doi.org/10.1145/2330163.2330328

Published: 07 July 2012 Publication History

Abstract

In wireless sensor networks (WSNs), sensors near the sink can be burdened with a large amount of traffic, because they have to transmit data generated by themselves and those far away from the sink. Hence the sensors near the sink would deplete their energy much faster than the others, which results in a short network lifetime. Using mobile sink is an effective way to tackle this issue. This paper explores the problem of determining the optimal movements of the mobile sink to maximize the network lifetime. A novel ant colony optimization algorithm (ACO), namely the ACO-MSS, is developed to solve the problem. The proposed ACO-MSS takes advantage of the global search ability of ACO and adopts effective heuristic information to find a near globally optimal solution. Multiple practical factors such as the forbidden regions and the maximum moving distance of the sink are taken into account to facilitate the real applications. The proposed ACO-MSS is validated by a series of simulations on WSNs with different characteristics. The simulation results demonstrate the effectiveness of the proposed algorithms.

References

[1]

D. Puccinelli, M. Haenggi, "Wireless sensor networks: applications and challenges of ubiquitous sensing," IEEE Circuits and Systems Magazine, vol. 5, no. 3, pp. 19--31, 2005.

[2]

A. Pantelopoulos, N. G. Bourbakis, "A Survey on Wearable Sensor-Based Systems for Health Monitoring and Prognosis," IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, vol. 40, no. 1, pp. 1--12, Jan. 2010.

Digital Library

[3]

M. Haenggi, "Opportunities and challenges in Wireless sensor Networks," in Handbook of Sensor Network: Compact Wireless and Wired Sensing Systems, M. llyas and I. Mahgoub, eds., Boca Raton, FL, pp. 1.1--1.14, CRC Press, 2004.

[4]

J. H. Chang, L. Tassiulas, "Maximum lifetime routing in wireless sensor networks," IEEE/ACM Transactions on Networking, vol. 12, no. 4, pp. 609--619, Aug. 2004.

Digital Library

[5]

Y. Lin, J. ZHANG, et al., "An Ant Colony Optimization Approach for Maximizing the Lifetime of Heterogeneous Wireless Sensor Networks", IEEE Transactions on Systems, Man, and Cybernetics - Part C: Applications and and Reviews ( In press).

[6]

J. H. Zhong and J. Zhang, "Energy-efficient local wake-up scheduling in wireless sensor networks," 2011 IEEE Congress on Evolutionary Computation (CEC), pp. 2280--2284, 5-8 June 2011.

[7]

X. M. Hu, J. Zhang, and et al., "Hybrid Genetic Algorithm Using a Forward Encoding Scheme for Lifetime Maximization of Wireless Sensor Networks," IEEE Transactions on Evolutionary Computation, vol. 14, no. 5, pp. 766--781, Oct. 2010.

Digital Library

[8]

R. C. Shah, S. Roy, S. Jain, W. Brunette, "Data MULEs: modeling a three-tier architecture for sparse sensor networks," Proc. 1th IEEE International Workshop on Sensor Network Protocols and Applications 2003, pp. 30--41, May 2003.

[9]

K. H. S. Kim, T. F. Abdelzaher, and W. H. Kwon, "Minimum energy asynchronous dissemination to mobile sinks in wireless sensor networks," in Proceedings of the First International Conference on Embedded Networked Sensor Systems, SenSys 2003, Los Angeles, CA, pp. 193--204, November, 5-7 2003.

Digital Library

[10]

Y. Z. Bi, L. M. Sun, J. Ma, N. Li, I. A. Khan, and C. F. Chen, "HUMS: An Autonomous Moving Strategy for Mobile Sinks in Data-Gathering Sensor Networks," EURASIP J. Wireless Comm. and Networking, pp. 1--15, 2007.

[11]

M. Marta, M. Cardei, "Improved sensor network lifetime with multiple mobile sinks", Elsevier Journal of Pervasive and Mobile Computing, vol. 5 no. 5, pp. 542--555, October 2009.

Digital Library

[12]

Y. Z. Bi, and et al., "Moving Schemes for Mobile Sinks in Wireless Sensor Networks," Performance, Computing, and Communications Conference, 2007. IPCCC 2007. IEEE Internationa, pp. 101--108, 11-13 April 2007.

[13]

Y. S. Yun, Y. Xia, "Maximizing the Lifetime of Wireless Sensor Networks with Mobile Sink in Delay-Tolerant Applications," IEEE Transactions on Mobile Computing, vol. 9, no. 9, pp. 1308--1318, Sept. 2010.

Digital Library

[14]

Y. Shi and Y. T. Hou, "Theoretical Results on Base Station Movement Problem for Sensor Network," Proc. IEEE INFOCOM, 2008.

[15]

Z. M. Wang, S. Basagni, E. Melachrinoudis, C. Petrioli, "Exploiting Sink Mobility for Maximizing Sensor Networks Lifetime," Proc. 38th Annual Hawaii International Conference on System Sciences 2005 (HICSS '05), pp. 287a, 03-06 Jan. 2005.

Digital Library

[16]

I. Papadimitriou and L. Georgiadis, "Maximum Lifetime Routing to Mobile Sink in Wireless Sensor Networks," Proc. 13th IEEE International Conference on software, Telecommunications and Computer Networks, SoftCOM 2005, Split, Croatia, September 2005.

[17]

M. Gatzianas, L. Georgiadis, "A Distributed Algorithm for Maximum Lifetime Routing in Sensor Networks with Mobile Sink," IEEE Transactions on Wireless Communications, vol. 7, no. 3, pp. 984--994, March 2008.

Digital Library

[18]

S. Basagni, A. Carosi, and et al., "A New MILP Formulation and Distributed Protocols for Wireless Sensor Networks Lifetime Maximization," Proc. IEEE International Conference on Communications 2006, pp. 3517--3524, June 2006.

[19]

M. Dorigo and T. Stutzle, Ant Colony Optimization. Cambridge, MA: MIT Press, 2004.

Digital Library

[20]

M. Dorigo, L. M. Gambardella, "Ant colony system: a cooperative learning approach to the traveling salesman problem," IEEE Transactions on Evolutionary Computation, vol. 1, no. 1, pp. 53--66, Apr 1997.

Digital Library

[21]

W. Heinzelman, A. Chandrakasan, and H. Balakrishnan, "Energy efficient communication protocols for wireless microsensor networks," in Proc, 33rd International Conference on System Sciences (HICSS 2000), vol. 8, pp. 8020--8029, January 2000.

Digital Library

[22]

J. H. Zhong, X. M. Hu, J. Zhang and M. Gu, "Comparison of Performance between Different Selection Strategies on Simple Genetic Algorithms," in Proc. IEEE Computational Intelligence for Modeling, Control and Automation 2005, vol. 2, pp. 1115--1121, Nov. 2005.

Digital Library

[23]

Z. H. Zhan, J. Zhang, and Yun Li, "An Efficient Ant Colony System Based on Receding Horizon Control for the Aircraft Arrival Sequencing and Scheduling Problem", IEEE Transactions on Intelligent Transportation Systems, vol. 11, no. 2, pp. 399--412, June 2010.

Digital Library

[24]

W. N. Chen, J. Zhang, and H. Chung, "Optimizing Discounted Cash Flows in Project Scheduling - An Ant Colony Optimization Approach", IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, vol. 40 No. 5 pp. 64--77, Jan. 2010.

Digital Library

[25]

W. N. Chen, J. ZHANG, and et al., "Optimizing Discounted Cash Flows in Project Scheduling - An Ant Colony Optimization Approach", IEEE Transactions on Systems, Man, and Cybernetics - Part C: Applications and Reviews Vol. 40 No. 1 pp. 64--77, Jan. 2010.

Digital Library

[26]

X. M. Hu, J. ZHANG, and et al., "An Intelligent Testing System Embedded with an Ant Colony Optimization Based Test Composition Method", IEEE Transactions on Systems, Man, and Cybernetics - Part C: Applications and Reviews, Vol. 39, No. 6, pp. 659--669, Dec 2009.

Digital Library

[27]

J. ZHANG, H. Chung, W. L. Lo, and T. Huang, "Extended Ant Colony Optimization Algorithm for Power Electronic Circuit Design", IEEE Transactions on Power Electronic. Vol. 24,No. 1, pp. 147--162, Jan 2009.

[28]

W. N. Chen and J. ZHANG, "Ant Colony Optimization Approach to Grid Workflow Scheduling Problem with Various QoS Requirements", IEEE Transactions on Systems, Man, and Cybernetics - Part C: Applications and Reviews, Vol. 31, No. 1, pp. 29--43, Jan 2009.

Digital Library

[29]

X. M. Hu, J. ZHANG and et al., "Protein Folding in Hydrophobic-Polar Lattice Model: A Flexible Ant-Colony Optimization Approach", Protein and Peptide Letters, Volume 15, Number 5, 2008, Pp. 469--477.

[30]

J. ZHANG, X. M. Hu, and et al., "Implementation of an Ant Colony optimization technique for Job Scheduling Problem", Transactions of the Institute of Measurement and Control 28,1(2006) pp. 93--108.

[31]

J. ZHANG, and et al., "Evolutionary Computation Meets Machine Learning: A Survey", IEEE Computational Intelligence Magazine, pp. 68--75, NOVEMBER 2011.

[32]

Z. H. Zhan, and et al., "Adaptive Particle Swarm Optimization", IEEE Transactions on Systems, Man, and Cybernetics - Part B. VOL. 39, NO. 6, Dec 2009, Page 1362--1381.

Digital Library

[33]

J. ZHANG, H. Chung and W. L. LO, "Clustering-Based Adaptive Crossover and Mutation Probabilities for Genetic Algorithms", IEEE Transactions on Evolutionary Computation Vol. 11, No. 3, June 2007 Page. 326--335.

Digital Library

[34]

W. N. Chen, and et al., "A Novel Set-Based Particle Swarm Optimization Method for Discrete Optimization Problems", IEEE Transactions on Evolutionary Computation, Vol. 14, No. 2, pp. 278--300, April 2010.

Digital Library

Cited By

Kurian NB. RM. S(2022)Mobile Sink Data Gathering and Path Determination in Wireless Sensor Networks: A Review2022 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET)10.1109/WiSPNET54241.2022.9767167(306-310)Online publication date: 24-Mar-2022
https://doi.org/10.1109/WiSPNET54241.2022.9767167
Lin ZKeh HWu RRoy D(2021)Joint Data Collection and Fusion Using Mobile Sink in Heterogeneous Wireless Sensor NetworksIEEE Sensors Journal10.1109/JSEN.2020.301937221:2(2364-2376)Online publication date: 15-Jan-2021
https://doi.org/10.1109/JSEN.2020.3019372
Kurian NPreetha RS JVaijayanthimala J(2021)Mobile Sink Data Gathering and Path Determination in WSN based on P-AACO Approach2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS)10.1109/ICICCS51141.2021.9432251(223-229)Online publication date: 6-May-2021
https://doi.org/10.1109/ICICCS51141.2021.9432251
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Index Terms

Ant colony optimization algorithm for lifetime maximization in wireless sensor network with mobile sink

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cover image ACM Conferences

GECCO '12: Proceedings of the 14th annual conference on Genetic and evolutionary computation

July 2012

1396 pages

ISBN:9781450311779

DOI:10.1145/2330163

Editor:
Terence Soule
University of Idaho, USA
,
General Chair:
Jason H. Moore
Dartmouth College, USA

Copyright © 2012 ACM.

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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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Published: 07 July 2012

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GECCO '12

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SIGEVO

GECCO '12: Genetic and Evolutionary Computation Conference

July 7 - 11, 2012

Pennsylvania, Philadelphia, USA

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Kurian NB. RM. S(2022)Mobile Sink Data Gathering and Path Determination in Wireless Sensor Networks: A Review2022 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET)10.1109/WiSPNET54241.2022.9767167(306-310)Online publication date: 24-Mar-2022
https://doi.org/10.1109/WiSPNET54241.2022.9767167
Lin ZKeh HWu RRoy D(2021)Joint Data Collection and Fusion Using Mobile Sink in Heterogeneous Wireless Sensor NetworksIEEE Sensors Journal10.1109/JSEN.2020.301937221:2(2364-2376)Online publication date: 15-Jan-2021
https://doi.org/10.1109/JSEN.2020.3019372
Kurian NPreetha RS JVaijayanthimala J(2021)Mobile Sink Data Gathering and Path Determination in WSN based on P-AACO Approach2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS)10.1109/ICICCS51141.2021.9432251(223-229)Online publication date: 6-May-2021
https://doi.org/10.1109/ICICCS51141.2021.9432251
Wang HLi KPedrycz W(2020)An Elite Hybrid Metaheuristic Optimization Algorithm for Maximizing Wireless Sensor Networks Lifetime With a Sink NodeIEEE Sensors Journal10.1109/JSEN.2020.297103520:10(5634-5649)Online publication date: 15-May-2020
https://doi.org/10.1109/JSEN.2020.2971035
Byun H(2020)Mobile Collector-Based Cost Balancing Scheme for Uniform Data Gathering Delay and Energy Consumption in Wireless Sensor Actuator Networking SystemsIEEE Sensors Journal10.1109/JSEN.2019.296219320:8(4260-4268)Online publication date: 15-Apr-2020
https://doi.org/10.1109/JSEN.2019.2962193
Huang ZLu CZhong J(2019)A Multi-Objective Hyper-Heuristic for Unmanned Aerial Vehicle Data Collection in Wireless Sensor Networks2019 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI44817.2019.9002862(1614-1621)Online publication date: Dec-2019
https://doi.org/10.1109/SSCI44817.2019.9002862
Mechta DHarous S(2018)LBSNN: Neural Networks-based Moving Sink2018 9th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)10.1109/UEMCON.2018.8796548(475-481)Online publication date: Nov-2018
https://doi.org/10.1109/UEMCON.2018.8796548
Sapre SMini S(2018)Optimized Positioning of Relay Nodes Using Bat Algorithm in Heterogeneous Wireless Sensor Networks2018 International Conference on Communication and Signal Processing (ICCSP)10.1109/ICCSP.2018.8524543(0214-0218)Online publication date: Apr-2018
https://doi.org/10.1109/ICCSP.2018.8524543
Primeau NFalcon RAbielmona RPetriu E(2018)A Review of Computational Intelligence Techniques in Wireless Sensor and Actuator NetworksIEEE Communications Surveys & Tutorials10.1109/COMST.2018.285022020:4(2822-2854)Online publication date: Dec-2019
https://doi.org/10.1109/COMST.2018.2850220
D. PAmgoth TAnnavarapu C(2018)ACO-based mobile sink path determination for wireless sensor networks under non-uniform data constraintsApplied Soft Computing10.1016/j.asoc.2018.05.00869(528-540)Online publication date: Aug-2018
https://doi.org/10.1016/j.asoc.2018.05.008
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