Abstract
Energy efficient scheduling of sensor nodes is one of the most efficient techniques to extend the lifetime of the wireless sensor networks (WSNs). Instead of activating all the deployed sensor nodes, a set of sensor nodes are activated or scheduled to monitor the targeted region. While scheduling with lesser number of sensor nodes, coverage and connectivity of the network should be taken care due to the limited sensing and communication range of the sensor nodes. In this paper, we have proposed an improved genetic algorithm (GA) based scheduling for WSNs. An efficient chromosome representation is given and it is shown to generate valid chromosome after crossover and mutation operation. The fitness function is derived with four conflicting objectives, selection of minimum number of sensor nodes, full coverage, connectivity and energy level of the selected sensor nodes. We have introduced a novel mutation operation for better performance and faster convergence of the proposed GA based approaches. We have also formulated the scheduling problem as a Linear Programming. Extensive simulation is performed on various network scenarios by varying number of deployed sensor nodes, target point and network length. We also perform a popular statistical test, analysis of variance followed by post hoc analysis.
Similar content being viewed by others
References
Yick, J., Mukherjee, B., & Ghosal, D. (2008). Wireless sensor network survey. Computer Networks, 52(12), 2292–2330.
Rault, T., Bouabdallah, A., & Challal, Y. (2014). Energy efficiency in wireless sensor networks: A top-down survey. Computer Networks, 67, 104–122.
Yigitel, M. A., Incel, O. D., & Ersoy, C. (2011). QoS-aware MAC protocols for wireless sensor networks: A survey. Computer Networks, 55(8), 1982–2004.
Kuila, P., & Jana, P. K. (2015). Heap and parameter-based load balanced clustering algorithms for wireless sensor networks. International Journal of Communication Networks and Distributed Systems, 14(4), 413–432.
Kuila, P., & Jana, P. K. (2014). A novel differential evolution based clustering algorithm for wireless sensor networks. Applied Soft Computing, 25, 414–425.
Cardei, I., & Cardei, M. (2008). Energy-efficient connected-coverage in wireless sensor networks. International Journal of Sensor Networks, 3(3), 201–210.
Kuila, P., & Jana, P. K. (2014). Energy efficient clustering and routing algorithms for wireless sensor networks: Particle swarm optimization approach. Engineering Applications of Artificial Intelligence, 33, 127–140.
Zungeru, A. M., Ang, L.-M., & Seng, K. P. (2012). Classical and swarm intelligence based routing protocols for wireless sensor networks: A survey and comparison. Journal of Network and Computer Applications, 35(5), 1508–1536.
Kuila, P., Gupta, S. K., & Jana, P. K. (2013). A novel evolutionary approach for load balanced clustering problem for wireless sensor networks. Swarm and Evolutionary Computation, 12, 48–56.
Kuila, P., & Jana, P. K. (2016). Evolutionary computing approaches for clustering and routing in wireless sensor networks. In J. K. Mandal, S. Mukhopadhyay, & T. Pal (Eds.), Handbook of research on natural computing for optimization problems (pp. 246–266). Hershey: IGI Global.
Musilek, P., Krömer, P., & Bartoň, T. (2015). Review of nature-inspired methods for wake-up scheduling in wireless sensor networks. Swarm and Evolutionary Computation, 25, 100–118.
Renold, A. P., & Chandrakala, S. (2016). Survey on state scheduling-based topology control in unattended wireless sensor networks. Computers & Electrical Engineering, 56, 334–349.
Zhu, C., Zheng, C., Shu, L., & Han, G. (2012). A survey on coverage and connectivity issues in wireless sensor networks. Journal of Network and Computer Applications, 35(2), 619–632.
Yu, J., Wang, N., Wang, G., & Yu, D. (2013). Connected dominating sets in wireless ad hoc and sensor networks—A comprehensive survey. Computer Communications, 36(2), 121–134.
Rebai, M., Le berre, M., Snoussi, H., Hnaien, F., & Khoukhi, L. (2015). Sensor deployment optimization methods to achieve both coverage and connectivity in wireless sensor networks. Computers & Operations Research, 59, 11–21.
Gupta, S. K., Kuila, P., & Jana, P. K. (2015). Genetic algorithm for \( k \)-connected relay node placement in wireless sensor networks. In Proceedings of the second international conference on computer and communication technologies (pp. 721–729). Springer.
Gupta, S. K., Kuila, P., & Jana, P. K. (2016). Genetic algorithm approach for \( k \)-coverage and \( m \)-connected node placement in target based wireless sensor networks. Computers & Electrical Engineering, 56, 544–556.
Liu, X., & He, D. (2014). Ant colony optimization with greedy migration mechanism for node deployment in wireless sensor networks. Journal of Network and Computer Applications, 39, 310–318.
Moro, G., & Monti, G. (2012). W-Grid: A scalable and efficient self-organizing infrastructure for multi-dimensional data management, querying and routing in wireless data-centric sensor networks. Journal of Network and Computer Applications, 35(4), 1218–1234.
Al-Turjman, F. M., Hassanein, H. S., & Ibnkahla, M. (2013). Quantifying connectivity in wireless sensor networks with grid-based deployments. Journal of Network and Computer Applications, 36(1), 368–377.
Yang, C., & Chin, K.-W. (2017). On nodes placement in energy harvesting wireless sensor networks for coverage and connectivity. IEEE Transactions on Industrial Informatics, 13(1), 27–36.
Yang, C., & Chin, K.-W. (2014). Novel algorithms for complete targets coverage in energy harvesting wireless sensor networks. IEEE Communications Letters, 18(1), 118–121.
Deif, D., & Gadallah, Y. (2017). An ant colony optimization approach for the deployment of reliable wireless sensor networks. IEEE Access, 5, 10744–10756.
Wang, Y., Wu, S., Chen, Z., Gao, X., & Chen, G. (2017). Coverage problem with uncertain properties in wireless sensor networks: A survey. Computer Networks, 123, 200–232.
Han, G., Liu, L., Jiang, J., Shu, L., & Hancke, G. (2017). Analysis of energy-efficient connected target coverage algorithms for industrial wireless sensor networks. IEEE Transactions on Industrial Informatics, 13(1), 135–143.
Jia, J., Chen, J., Chang, G., & Tan, Z. (2009). Energy efficient coverage control in wireless sensor networks based on multi-objective genetic algorithm. Computers & Mathematics with Applications, 57(11), 1756–1766.
Zhang, H., & Hou, J. C. (2005). Maintaining sensing coverage and connectivity in large sensor networks. Ad Hoc & Sensor Wireless Networks, 1(1–2), 89–124.
Lee, J.-W., Choi, B.-S., & Lee, J.-J. (2011). Energy-efficient coverage of wireless sensor networks using ant colony optimization with three types of pheromones. IEEE Transactions on Industrial Informatics, 7(3), 419–427.
Dong, Y., Xu, J., & Zhang, X. (2013). Energy-efficient target coverage algorithm for wireless sensor networks. In IEEE 10th international conference on mobile ad-hoc and sensor systems (MASS), 2013 (pp. 415–416). IEEE.
Sakai, K., Sun, M., Ku, W., Lai, T., & Vasilakos, A. (2015). A framework for the optimal \( k \)-coverage deployment patterns of wireless sensors. IEEE Sensors, 15, 7273–7283.
Carrabs, F., Cerulli, R., DAmbrosio, C., & Raiconi, A. (2017). Exact and heuristic approaches for the maximum lifetime problem in sensor networks with coverage and connectivity constraints. RAIRO-Operations Research, 51(3), 607–625.
Lersteau, C., Rossi, A., & Sevaux, M. (2018). Minimum energy target tracking with coverage guarantee in wireless sensor networks. European Journal of Operational Research, 265(3), 882–894.
Heinzelman, W. B., Chandrakasan, A. P., & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1(4), 660–670.
Gupta, S. K., Kuila, P., & Jana, P. K. (2016). Energy efficient multipath routing for wireless sensor networks: A genetic algorithm approach. In International conference on advances in computing, communications and informatics (ICACCI), 2016 (pp. 1735–1740). IEEE.
Kruppa, J., Lepenies, B., & Jung, K. (2018). A genetic algorithm for simulating correlated binary data from biomedical research. Computers in Biology and Medicine, 92, 1–8.
Chen, W.-H., Wu, P.-H., & Lin, Y.-L. (2018). Performance optimization of thermoelectric generators designed by multi-objective genetic algorithm. Applied Energy, 209, 211–223.
Gong, X., Plets, D., Tanghe, E., De Pessemier, T., Martens, L., & Joseph, W. (2018). An efficient genetic algorithm for large-scale planning of dense and robust industrial wireless networks. Expert Systems with Applications, 96, 311–329.
Konak, A., Coit, D. W., & Smith, A. E. (2006). Multi-objective optimization using genetic algorithms: A tutorial. Reliability Engineering & System Safety, 91(9), 992–1007.
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.
Goldberg, D. E. (2006). Genetic algorithms. Bengaluru: Pearson Education India.
Azharuddin, M., Kuila, P., & Jana, P. K. (2015). Energy efficient fault tolerant clustering and routing algorithms for wireless sensor networks. Computers & Electrical Engineering, 41, 177–190.
Singh, D., Kuila, P., & Jana, P. K. (2014). A distributed energy efficient and energy balanced routing algorithm for wireless sensor networks. In 3rd International conference on advances in computing, communications and informatics (ICACCI-2014) (pp. 1657–1663). IEEE .
Shapiro, S. S., & Wilk, M. B. (1965). An analysis of variance test for normality (complete samples). Biometrika, 52(3/4), 591–611.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Harizan, S., Kuila, P. Coverage and connectivity aware energy efficient scheduling in target based wireless sensor networks: an improved genetic algorithm based approach. Wireless Netw 25, 1995–2011 (2019). https://doi.org/10.1007/s11276-018-1792-2
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11276-018-1792-2