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Wireless Personal Communications

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03-02-2017

Multi-Objective WSN Deployment Using Genetic Algorithms Under Cost, Coverage, and Connectivity Constraints

Authors: Mohamed Amin Benatia, M’hammed Sahnoun, David Baudry, Anne Louis, Abdelkhalak El-Hami, Belahcene Mazari

Published in: Wireless Personal Communications | Issue 4/2017

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Abstract

A wireless sensor network (WSN) deployment requires the identification of optimal network nodes (sensor and sink) positions in an area of interest, to ensure the best network performances (Senouci et al. in Smart Communications in Network Technologies (SaCoNeT), 2014 International Conference on, IEEE, pp 1–6, 43). The deployment process can be divided in two main parts: (1) WSN model construction, and (2) placement optimization. Few research works were interested by WSN deployment in indoor environment, even though, most of them consider the objectives (coverage, cost, connectivity) individually without considering the sensors and sink in the same time. This paper proposes a multi-objective deployment strategy (MODS), where all important objectives are integrated. The MODS uses the multi-objective evolutionary algorithms to get near optimal solution for WSN deployment problem. An original coding solution, integrating both network cost and nodes positions is proposed. A comparative study between two evolutionary strategies (classical GA, and NSGA-II) was performed to identify the use case of each one. Obtained results showed the interest of the proposed methodology.

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Abraham, A., & Jain, L. (2005). Evolutionary multiobjective optimisation. Berlin: Springer.CrossRefMATH

Aitsaadi, N., Achir, N., Boussetta, K., & Pujolle, G. (2009). A Tabu Search WSN deployment method for monitoring geographically irregular distributed events. Sensors, 9(3), 1625–1643. doi:10.3390/s90301625.CrossRef

Aitsaadi, N., Achir, N., Boussetta, K., & Pujolle, G. (2011). Artificial potential field approach in WSN deployment: Cost, QoM, connectivity, and lifetime constraints. Computer Networks, 55(1), 84–105. doi:10.1016/j.comnet.2010.07.017.CrossRef

Al-Turjman, F. M., Al-Fagih, A. E., Hassanein, H. S., & Ibnkahla, M. A. (2010). Deploying fault-tolerant grid-based wireless sensor networks for environmental applications. In Local Computer Networks (LCN), 2010 IEEE 35th Conference on, IEEE, pp. 715–722.

Alageswaran, R. (2012). Design and implementation of dynamic sink node placement using particle swarm optimization for life time maximization of WSN applications. In IEEE international conference on advances in engineering, science and management (ICAESM) (pp. 552–555).

Amin, B. M., Anne, L., & Belahcene, M. (2014). Impact of radio propagation in buildings on WSN’s lifetime. In Computer & Information Technology (GSCIT), 2014 Global Summit on IEEE (pp. 1–6).

Ayoub, Z. T., Ouni S., & Kamoun, F. (2012). Energy consumption analysis to predict the lifetime of ieee 802.15. 4 wireless sensor networks. In Communications and Networking (ComNet), 2012 Third International Conference on, IEEE, pp. 1–6.

Barekatain, B., Khezrimotlagh, D., Maarof, M. A., Ghaeini, H. R., Quintana, A. A., & Cabrera, A. T. (2015). Efficient p2p live video streaming over hybrid wmns using random network coding. Wireless Personal Communications, 80(4), 1761–1789.CrossRef

Benatia, M., Louis, A., Baudry, D., Mazari, B., & El Hami, A. (2014). WSN’s modeling for a smart building application. In Energy Conference (ENERGYCON), 2014 IEEE International, pp. 821–827. doi:10.1109/ENERGYCON.2014.6850520.

10.

Bereketli, A., & Akan, O. B. (2009). Communication coverage in wireless passive sensor networks. IEEE Communications Letters, 13(2), 133–135.CrossRef

11.

Cardei, M., Thai, M. T., Li, Y., & Wu, W. (2005). Energy-efficient target coverage in wireless sensor networks. In INFOCOM 2005. 24th Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings IEEE, IEEE (Vol. 3, pp. 1976–1984).

12.

Chen, F., & Li, R. (2013). Sink node placement strategies for wireless sensor networks. Wireless Personal Communications, 68(2), 303–319.CrossRef

13.

Collette, Y., & Siarry, P. (2003). Multiobjective optimization: Principles and case studies. Berlin: Springer Science & Business Media.MATH

14.

Cuomo, F., Cipollone, E., & Abbagnale, A. (2009). Performance analysis of IEEE 802.15.4 wireless sensor networks: An insight into the topology formation process. Computer Networks, 53(18), 3057–3075. doi:10.1016/j.comnet.2009.07.016.CrossRefMATH

15.

Darwin, C. (1837). First notebook on the transmutation of species. The irregularity of the p 26.

16.

Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.CrossRef

17.

Edwards, W. K., & Grinter, R. E. (2001). At home with ubiquitous computing: Seven challenges. In Ubicomp 2001: Ubiquitous Computing, pp. 256–272. Springer.

18.

Fan, J., Jfanlboroacuk, E., & Parish, D. J. (2011). SNDT: A genetic algorithm-based protocol selection tool for wireless network design. In Cognitive Wireless Systems (UKIWCWS), 2009 first UK-India international workshop on. IEEE (pp. 1–5).

19.

Ferentinos, K. P., & Tsiligiridis, T. A. (2007). Adaptive design optimization of wireless sensor networks using genetic algorithms. Computer Networks, 51(4), 1031–1051. doi:10.1016/j.comnet.2006.06.013.CrossRefMATH

20.

Golberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Reading: Addison Wesley.

21.

Guinard, A., McGibney, A., & Pesch, D. (2009). A wireless sensor network design tool to support building energy management. In Proceedings of the First ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings, ACM, pp. 25–30.

22.

Güney, E., Aras, N., Altnel, K., & Ersoy, C. (2010). Efficient integer programming formulations for optimum sink location and routing in heterogeneous wireless sensor networks. Computer Networks, 54(11), 1805–1822. doi:10.1016/j.comnet.2010.02.009.CrossRefMATH

23.

He, D., Mujica, G., Portilla, J., & Riesgo, T. (2014). Modelling and planning reliable wireless sensor networks based on multi-objective optimization genetic algorithm with changeable length. Journal of Heuristics, 21(2), 257–300. doi:10.1007/s10732-014-9261-2.CrossRef

24.

Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In System sciences, 2000. Proceedings of the 33rd annual Hawaii international conference on, IEEE, p. 10.

25.

Heo, N., & Varshney, P. (2005). Energy-efficient deployment of intelligent mobile sensor networks. IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans, 35(1), 78–92. doi:10.1109/TSMCA.2004.838486.CrossRef

26.

Xm, Hu, Member, S., Zhang, J., Member, S., & Yu, Y. (2010). Hybrid genetic algorithm using a forward encoding scheme for lifetime maximization of wireless sensor. Networks, 14(5), 766–781.

27.

Jia, J., Chen, J., Chang, G., & Li J. (2007). Coverage optimization based on improved NSGA-II in Wireless Sensor Network. In Integration technology, 2007. ICIT’07. IEEE International Conference on Integration Technology (pp. 614–618).

28.

Keenan, J., & Motley, A. (1990). Radio coverage in buildings. British Telecom Technology Journal, 8(1), 19–24.

29.

Konstantinidis, A., Yang, K., Zhang, Q., & Zeinalipour-Yazti, D. (2010). A multi-objective evolutionary algorithm for the deployment and power assignment problem in wireless sensor networks. Computer Networks, 54(6), 960–976. doi:10.1016/j.comnet.2009.08.010.CrossRefMATH

30.

Krishna, M. B., & Doja, M. (2015). Multi-objective meta-heuristic approach for energy-efficient secure data aggregation in wireless sensor networks. Wireless Personal Communications, 81(1), 1–16.CrossRef

31.

Kung, H. Y., Huang, C. M., & Ku, H. H. (2008). Efficient sensor deployment control schemes and performance evaluation for obstacle and unknown environments. Wireless Personal Communications, 45(2), 231–263.CrossRef

32.

Lai, C. C., Ting, C. K., & Ko, R. S. (2007) An effective genetic algorithm to improve wireless sensor network lifetime for large-scale surveillance applications. In Evolutionary Computation, CEC 2007. IEEE Congress on, IEEE. pp. 3531–3538.

33.

Lee, J. Y., Seok, J. H., & Lee, J. J. (2012). Multiobjective optimization approach for sensor arrangement in a complex indoor environment. IEEE Transactions on Systems, Man, and Cybernetics, 42(2), 174–186.CrossRef

34.

Lee, K. (2007). An automated sensor deployment algorithm based on swarm intelligence for ubiquitous environment. International Journal of Computer Science and Network Security (IJCSNS), 7(12), 76–79.

35.

Liefooghe, A., Jourdan, L., Legrand, T., Liefooghe, A., Jourdan, L., & Legrand T. (2010). ParadisEO-MOEO: A software framework for evolutionary multi-objective optimization. In International conference on evolutionary multi-criterion optimization (pp. 386–400).

36.

Mahmood, M. A., Seah, W. K., & Welch, I. (2015). Reliability in wireless sensor networks: A survey and challenges ahead. Computer Networks, 79, 166–187.

37.

Molina, G., Alba, E., & Talbi, E. G. (2008). Optimal Sensor Network Layout using multi-objective metaheuristics. Journal of Universal Computer Science (J.UCS), 14(15), 2549–2565.

38.

Mostafaei, H., & Shojafar, M. (2015). A new meta-heuristic algorithm for maximizing lifetime of wireless sensor networks. Wireless Personal Communications, 82(2), 723–742.CrossRef

39.

Perez, A. J., Labrador, M. A., & Wightman, P. M. (2011). A multiobjective approach to the relay placement problem in WSNS. In Wireless Communications and Networking Conference (WCNC), 2011 IEEE, IEEE, pp. 475–480.

40.

Pinto, A., D’Angelo, M., Fischione, C., Scholte, E., & Sangiovanni-Vincentelli, A. (2008). Synthesis of embedded networks for building automation and control. In 2008 American Control Conference, pp. 920–925. doi:10.1109/ACC.2008.4586610, http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4586610.

41.

Ray, A. (2009). Planning and analysis tool for large scale deployment of wireless sensor network. International Journal of Next-Generation Networks (IJNGN), 1(1), 29–36.

42.

Sahnoun, M., Godsiff, P., Baudry, D., Louis, A., & Belahcen, M. (2014). Modelling of maintenance strategy of offshore wind farms based multi-agent system. In CIE44 & ISSM14 (44th international conference on computers & industrial engineering & 9th international symposiom on intelligent manufacturing and service systems) (Vol. 591, pp. 2406–2420).

43.

Senouci, M. R., Yazid Boudaren, M. E., Senouci, M. A., & Mellouk, A. (2014). A smart methodology for deterministic deployment of wireless sensor networks. In Smart Communications in Network Technologies (SaCoNeT), 2014 International Conference on, IEEE, pp. 1–6.

44.

Slijepcevic, S., & Potkonjak, M. (2001). Power efficient organization of wireless sensor networks. In Communications, 2001. ICC 2001. IEEE International Conference on, IEEE (Vol. 2, pp. 472–476).

45.

Song, Y., Gui, C., Lu, X., Chen, H., & Sun, B. (2015). A genetic algorithm for energy-efficient based multipath routing in wireless sensor networks. Wireless Personal Communications, 85(4), 2055–2066.CrossRef

46.

Waldner, J. B. (2013). Nanocomputers and swarm intelligence. London: Wiley.

47.

Wan, P. J., & Yi, C. W. (2006). Coverage by randomly deployed wireless sensor networks. IEEE/ACM Transactions on Networking (TON), 14(SI), 2658–2669.MathSciNetMATH

48.

Wang, H., Roman, H. E., Yuan, L., Huang, Y., & Wang, R. (2014). Connectivity, coverage and power consumption in large-scale wireless sensor networks. Computer Networks, 75, 212–225. doi:10.1016/j.comnet.2014.10.008.CrossRef

49.

Wu, Y. C., & Tuan, C. C. (2015). K-hop coverage and connectivity aware clustering in different sensor deployment models for wireless sensor and actuator networks. Wireless Personal Communications, 85(4), 2565–2579.CrossRef

50.

Xue, Y., Lee, H. S., Yang, M., Kumarawadu, P., Ghenniwa, H. H., & Shen, W. (2007). Performance evaluation of NS-2 simulator for wireless sensor networks, In Electrical and computer engineering. Canadian conference on (CCECE 2007) (pp. 1372–1375).

51.

Yick, J., Mukherjee, B., & Ghosal, D. (2008). Wireless sensor network survey. Computer Networks, 52(12), 2292–2330. doi:10.1016/j.comnet.2008.04.002.CrossRef

52.

Yoon, Y., & Kim, Y. H. (2013). An efficient genetic algorithm for maximum coverage deployment in wireless sensor networks. IEEE Transactions on Cybernetics, 43(5), 1473–1483.CrossRef

Title: Multi-Objective WSN Deployment Using Genetic Algorithms Under Cost, Coverage, and Connectivity Constraints
Authors: Mohamed Amin Benatia
M’hammed Sahnoun
David Baudry
Anne Louis
Abdelkhalak El-Hami
Belahcene Mazari
Publication date: 03-02-2017
Publisher: Springer US
Published in: Wireless Personal Communications / Issue 4/2017
Print ISSN: 0929-6212
Electronic ISSN: 1572-834X
DOI: https://doi.org/10.1007/s11277-017-3974-0

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