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.
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.
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.
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.
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.
Bereketli, A., & Akan, O. B. (2009). Communication coverage in wireless passive sensor networks. IEEE Communications Letters, 13(2), 133–135.
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).
Chen, F., & Li, R. (2013). Sink node placement strategies for wireless sensor networks. Wireless Personal Communications, 68(2), 303–319.
Collette, Y., & Siarry, P. (2003). Multiobjective optimization: Principles and case studies. Berlin: Springer Science & Business Media.
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.
Darwin, C. (1837). First notebook on the transmutation of species. The irregularity of the p 26.
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.
Edwards, W. K., & Grinter, R. E. (2001). At home with ubiquitous computing: Seven challenges. In Ubicomp 2001: Ubiquitous Computing, pp. 256–272. Springer.
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).
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.
Golberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Reading: Addison Wesley.
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.
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.
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.
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.
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.
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.
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).
Keenan, J., & Motley, A. (1990). Radio coverage in buildings. British Telecom Technology Journal, 8(1), 19–24.
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.
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.
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.
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.
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.
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.
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).
Mahmood, M. A., Seah, W. K., & Welch, I. (2015). Reliability in wireless sensor networks: A survey and challenges ahead. Computer Networks, 79, 166–187.
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.
Mostafaei, H., & Shojafar, M. (2015). A new meta-heuristic algorithm for maximizing lifetime of wireless sensor networks. Wireless Personal Communications, 82(2), 723–742.
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.
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.
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.
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).
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.
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).
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.
Waldner, J. B. (2013). Nanocomputers and swarm intelligence. London: Wiley.
Wan, P. J., & Yi, C. W. (2006). Coverage by randomly deployed wireless sensor networks. IEEE/ACM Transactions on Networking (TON), 14(SI), 2658–2669.
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.
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.
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).
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.
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.
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Acknowledgement is made to European Union for the support of this research through the European Program INTERREG IVA France-Channel-UK by funding CREST (Community REtrofit through Sustainable Technology) Project.
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Benatia, M.A., Sahnoun, M., Baudry, D. et al. Multi-Objective WSN Deployment Using Genetic Algorithms Under Cost, Coverage, and Connectivity Constraints. Wireless Pers Commun 94, 2739–2768 (2017). https://doi.org/10.1007/s11277-017-3974-0
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DOI: https://doi.org/10.1007/s11277-017-3974-0