Journal of Heuristics

, Volume 21, Issue 2, pp 257–300 | Cite as

Modelling and planning reliable wireless sensor networks based on multi-objective optimization genetic algorithm with changeable length

  • Danping HeEmail author
  • Gabriel Mujica
  • Jorge Portilla
  • Teresa Riesgo


Wireless sensor networks (WSN) have shown their potentials in various applications, which bring a lot of benefits to users from different working areas. However, due to the diversity of the deployed environments and resource constraints, it is difficult to predict the performance of a topology. Besides the connectivity, coverage, cost, network longevity and service quality should all be considered during the planning procedure. Therefore, efficiently planning a reliable WSN is a challenging task, which requires designers coping with comprehensive and interdisciplinary knowledge. A WSN planning method is proposed in this work to tackle the above mentioned challenges and efficiently deploying reliable WSNs. First of all, the above mentioned metrics are modeled more comprehensively and practically compared with other works. Especially 3D ray tracing method is used to model the radio link and sensing signal, which are sensitive to the obstruction of obstacles; network routing is constructed by using AODV protocol; the network longevity, packet delay and packet drop rate are obtained via simulating practical events in WSNet simulator, which to the best of our knowledge, is the first time that network simulator is involved in a planning algorithm. Moreover, a multi-objective optimization algorithm is developed to cater for the characteristics of WSNs. Network size is changeable during evolution, meanwhile the crossovers and mutations are limited by certain constraints to eliminate invalid modifications and improve the computation efficiency. The capability of providing multiple optimized solutions simultaneously allows users making their own decisions, and the results are more comprehensive optimized compared with other state-of-the-art algorithms. Practical WSN deployments are also realized for both indoor and outdoor environments and the measurements coincident well with the generated optimized topologies, which prove the efficiency and reliability of the proposed algorithm.


Efficient planning method Measurement of WSN Modeling of WSN Multi-objective optimization NSGA-II 



The authors would like to acknowledge the support of ARTEMIS JU and Spanish Ministry of Industry and commerce for WSN DPCM project under grant ART-010000-2011-1.


  1. Akbarzadeh, V., Gagne, C., Parizeau, M., Argany, M., Mostafavi, M.: Probabilistic sensing model for sensor placement optimization based on line-of-sight coverage. IEEE Trans. Instrum. Meas. 62(2), 293–303 (2013). doi: 10.1109/TIM.2012.2214952 CrossRefGoogle Scholar
  2. Beutel, J., Kasten, O., Ringwald, M.: Poster abstract: Btnodes—a distributed platform for sensor nodes. In: Proceedings of the 1st International Conference on Embedded Networked Sensor Systems, ACM Press, Los Angeles, California, USA, pp. 292–293 (2003)Google Scholar
  3. Bluetooth.: (1998)
  4. Cheng, X., Du, D.Z., Wang, L., Xu, B.: Relay sensor placement in wireless sensor networks. Wirel Netw 14(3), 347–355 (2008). doi: 10.1007/s11276-006-0724-8 CrossRefGoogle Scholar
  5. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: Nsga-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002). doi: 10.1109/4235.996017 CrossRefGoogle Scholar
  6. Efrat, A., Har-Peled, S., Mitchell, J.S.B.: Approximation algorithms for two optimal location problems in sensor networks. In: Proceedings of the 2nd International Conference on Broadband Networks (BroadNets 2005), vol 1, pp. 714–723 (2005). doi: 10.1109/ICBN.2005.1589677
  7. Guinard, A., Aslam, M., Pusceddu, D., Rea, S., McGibney, A., Pesch, D.: Design and deployment tool for in-building wireless sensor networks: A performance discussion. In: IEEE 36th Conference on Local Computer Networks (LCN), pp. 649–656 (2011). doi: 10.1109/LCN.2011.6115530
  8. He, D., Liang, G., Portilla, J., Riesgo, T.: A novel method for radio propagation simulation based on automatic 3D environment reconstruction. In: Proceedings of the 6th European Conference Antennas and Propagation (EUCAP), pp. 1445–1449 (2012)Google Scholar
  9. He, D., Portilla, J., Riesgo, T.: A 3d multi-objective optimization planning algorithm for wireless sensor networks. In: IECON (2013)Google Scholar
  10. Huang, Y.K., Hsiu, P.C., Chu, W.N., Hung, K.C., Pang, A.C., Kuo, T.W., Di, M., Fang, H.W.: An integrated deployment tool for zigbee-based wireless sensor networks. In: IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, 2008. EUC ’08, vol 1, pp. 309–315, 2008, doi: 10.1109/EUC.2008.87
  11. Johnson, D., Hu, Y., Maltz, D.: The dynamic source routing protocol (DSR) for mobile ad hoc networks for ipv4 (2007)Google Scholar
  12. Jourdan, D., de Weck, O.: Layout optimization for a wireless sensor network using a multi-objective genetic algorithm. In: Vehicular Technology Conference, 2004. VTC 2004-Spring. 2004 IEEE 59th, vol 5, pp. 2466–2470 (2004). doi: 10.1109/VETECS.2004.1391366
  13. Kim, S., Ko, J.G., Yoon, J., Lee, H.: Multiple-objective metric for placing multiple base stations in wireless sensor networks. In: 2nd International Symposium on Wireless Pervasive Computing, 2007. ISWPC ’07, 2007, doi: 10.1109/ISWPC.2007.342679
  14. Kling, R.M.: Intel mote: An enhanced sensor network node. In: Proceedings of the International Workshop on Advanced Sensors, Structural Health Monitoring, and Smart Structures, Raiosha, Japan, pp. 1–6 (2003)Google Scholar
  15. Langendoen, K., Baggio, A., Visser, O.: Murphy loves potatoes: experiences from a pilot sensor network deployment in precision agriculture. In: Proceedings of the 20th international conference on Parallel and distributed processing, IEEE Computer Society, Washington, DC, USA, IPDPS’06, pp, 174–174 (2006)Google Scholar
  16. Lau, S.Y., Chang, T.H., Hu, S.Y., Huang, H.J., de Shyu, L., Chiu, C.M., Huang, P.: Sensor networks for everyday use: the bl-live experience. In: IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing, 2006, vol 1, pp. 7, 2006, doi: 10.1109/SUTC.2006.1636198
  17. Lee, S., Lee, M.: Qrmsc: Efficient qos-aware relay node placement in wireless sensor networks using minimum steiner tree on the convex hull. In: International Conference on Information Networking (ICOIN), 2013, pp. 36–41, 2013, doi: 10.1109/ICOIN.2013.6496348
  18. Liu, L., Ma, H.: On coverage of wireless sensor networks for rolling terrains. IEEE Trans. Parallel Distrib. Syst. 23(1), 118–125 (2012). doi: 10.1109/TPDS.2011.69 CrossRefGoogle Scholar
  19. Kouakou, M.T., Yamamoto, S., Yasumoto, K., Ito, M.: Cost-efficient deployment for full-coverage and connectivity in indoor 3d wsns. In: Proceedings of the IPSJ Dicomo 2010 (2010)Google Scholar
  20. Mafuta, M., Zennaro, M., Bagula, A., Ault, G., Gombachika, H., Chadza, T.: Successful deployment of a wireless sensor network for precision agriculture in malawi. In: IEEE 3rd International Conference on Networked Embedded Systems for Every Application (NESEA), 2012, pp. 1–7, 2012, doi: 10.1109/NESEA.2012.6474009
  21. McGibney, A., Guinard, A., Pesch, D.: Wi-design: A modelling and optimization tool for wireless embedded systems in buildings. In: IEEE 36th Conference on Local Computer Networks (LCN), 2011, pp. 640–648, 2011, doi: 10.1109/LCN.2011.6115529
  22. Misra, S., Hong, S.D., Xue, G., Tang, J.: Constrained relay node placement in wireless sensor networks: formulation and approximations. IEEE/ACM Trans. Netw. 18(2), 434–447 (2010). doi: 10.1109/TNET.2009.2033273 CrossRefGoogle Scholar
  23. Mujica, G., Rosello, V., Portilla, J., Riesgo, T.: Hardware-software integration platform for a wsn testbed based on cookies nodes. In: IECON 2012–38th Annual Conference on IEEE Industrial Electronics Society, pp. 6013–6018, 2012, doi: 10.1109/IECON.2012.6389099
  24. Perkins, C., Belding-Royer, E., Das, S.: Ad hoc on-demand distance vector (AODV) routing (2003)Google Scholar
  25. Poduri, S., Pattem, S., Krishnamachari, B., Sukhatme, G.S.: Sensor network configuration and the curse of dimensionality (2006)Google Scholar
  26. Polastre, J., Szewczyk, R., Culler, D.: Telos: enabling ultra-low power wireless research. In: Fourth International Symposium on Information Processing in Sensor Networks, 2005. IPSN 2005, pp. 364–369, 2005, doi: 10.1109/IPSN.2005.1440950
  27. Portilla, J., de Castro, A., de la Torre, E., Riesgo, T.: A modular architecture for nodes in wireless sensor networks. J. UCS 12(3), 328–339 (2006)Google Scholar
  28. Romer, K., Mattern, F.: The design space of wireless sensor networks. IEEE Wirel. Commun. 11(6), 54–61 (2004). doi: 10.1109/MWC.2004.1368897 CrossRefGoogle Scholar
  29. Shams, S., Chowdhury, A., Kim, K.H., Lee, N.B.: A fast approximation algorithm for relay node placement in double-tiered wireless sensor network. In: Military Communications Conference, 2008. MILCOM 2008. IEEE, pp. 1–6, 2008, doi: 10.1109/MILCOM.2008.4753648
  30. UWB.: Iso/iec 26907:2009 information technology—telecommunications and information exchange between systems—high-rate ultra-wideband phy and mac standard (2009)Google Scholar
  31. Waspmote.: Waspmote—wireless sensor networks 802.15.4 zigbee mote. (2013)
  32. Xiong, S., Yu, L., Shen, H., Wang, C., Lu, W.: Efficient algorithms for sensor deployment and routing in sensor networks for network-structured environment monitoring. In: INFOCOM, 2012 Proceedings IEEE, pp. 1008–1016, 2012, doi: 10.1109/INFCOM.2012.6195455
  33. ZigBee.: (2007)

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Danping He
    • 1
    Email author
  • Gabriel Mujica
    • 1
  • Jorge Portilla
    • 1
  • Teresa Riesgo
    • 1
  1. 1.Centro de Electronica IndustrialUniversidad Politecnica de MadridMadridSpain

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