Abstract
In wireless sensor network (WSN), limited energy resources with the nodes is a complex challenge as far as data routing, collecting and aggregating the data is concerned as all these processes are energy demanding. Network lifetime, stability period, and potential of the WSN are some of the parameters which are to be maximized subject to the constraints. The cluster head selection in the heterogeneous wireless sensor network has not been explored much and needs to be improved further to discover the potential of WSN in this area. In this study, optimal cluster head selection in heterogeneous wireless sensor network through Diversity-Driven Multi-Parent Evolutionary Algorithm with Adaptive Non-Uniform Mutation has been suggested. The efficacy of the proposed technique is tested on Classical Benchmark Functions, and obtained results are compared with the state of the art of algorithms. This algorithm is also validated on a heterogeneous wireless sensor network with cluster head selection as a multi-objective optimization problem. The residual energy of sensor node and distance travelled are to be optimized in order to minimize the fitness function. Simulation suggested that the proposed algorithm is found to be reliable and outperforms in terms of remaining energy of nodes, alive nodes versus round, dead nodes versus rounds, the lifespan of network, throughput, and stability period.
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Potthuri, S., Shankar, T., & Rajesh, A. (2018). Lifetime improvement in wireless sensor networks using hybrid differential evolution and simulated annealing ( DESA ). Ain Shams Engineering Journal, 9(4), 655–663. https://doi.org/10.1016/j.asej.2016.03.004.
John, J., & Rodrigues, P. (2019). MOTCO: Multi-objective Taylor crow optimization algorithm for cluster head selection in energy aware wireless sensor network. Mobile Networks and Applications, 24(5), 1509–1525. https://doi.org/10.1007/s11036-019-01271-1.
Kumar, D. (2013). Performance analysis of energy efficient clustering protocols for maximising lifetime of wireless sensor networks. IET Wireless Sensor System, 4(1), 9–16. https://doi.org/10.1049/iet-wss.2012.0150.
Simon, G., et al. (2004). Sensor network-based countersniper system. In Proceedings of second international conference embeded networked sensor systems (Sensys), Balt. MD.
Yick, J., Mukherjee, B., & Ghosal, D. (2005). Analysis of a prediction-based mobility adaptive tracking algorithm. In 2nd international conference broadband networks, BROADNETS (vol. 2005, pp. 809–816). https://doi.org/10.1109/ICBN.2005.1589681.
Castillo-Effen, M., Quintela, D. H., Jordan, R., Westhoff, W., & Moreno, W. (2004). Wireless sensor networks for flash-flood alerting. In Proceedings of IEEE international caracas conference devices, circuits system ICCDCS (pp. 142–146). https://doi.org/10.1109/iccdcs.2004.1393370.
Gao, T., Greenspan, D., Welsh, M., Juang, R. R., & Alm, A. (2005). Vital signs monitoring and patient tracking over a wireless network. In Annual international conference ieee engineering in medicine and biology proceedings (vol. 7, pp. 102–105).
Lorincz, K., et al. (2004). Sensor networks for emergency response: Challenges and opportunities. In IEEE pervasive computing pervasive computing first response (Special Issue).
Werner-Allen, G., et al. (2006). Deploying a wireless sensor network on an active volcano. IEEE Internet Computing, 10(2), 18–25. https://doi.org/10.1109/MIC.2006.26.
Yick, J., Mukherjee, B., & Ghosal, D. (2008). Wireless sensor network survey. Computer Networks, 52(12), 2292–2330. https://doi.org/10.1016/j.comnet.2008.04.002.
Bagci, F. (xxxx). Energy-efficient communication protocol for wireless microsensor networks. In Proceeding of 33rd Hawai international conference system science
Shepard, T. J. (xxxx). A Channel access scheme for large dense packet radio networks. In Proceeding of ACM SIGCOMM (pp. 219–230). https://doi.org/10.1145/248157.248176.
Li, S., Chen, H., Wang, M., Heidari, A. A., & Mirjalili, S. (2020). Slime mould algorithm: A new method for Stochastic optimization. Future Generation Computer Systems, 2, 13.
Mirjalili, S., Gandomi, A. H., Zahra, S., & Saremi, S. (2017). Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 114, 163–191. https://doi.org/10.1016/j.advengsoft.2017.07.002.
Faris, H., Mirjalili, S., Aljarah, I., Mafarja, M., & Heidari, A. A. (2020). Salp Swarm algorithm: Theory, literature review, and application in extreme learning machines. Studies in Computational Intelligence, 811, 185–199. https://doi.org/10.1007/978-3-030-12127-3_11.
Wu, J., Nan, R., & Chen, L. (2019). Improved salp swarm algorithm based on weight factor and adaptive mutation. Journal of Experimental and Theoretical Artificial Intelligence, 00(00), 1–23. https://doi.org/10.1080/0952813X.2019.1572659.
Mirjalili, S. (2016). SCA: A Sine Cosine Algorithm for solving optimization problems. Knowledge-Based System, 96, 120–133. https://doi.org/10.1016/j.knosys.2015.12.022.
Nenavath, H., Kumar, R., & Das, S. (2018). A synergy of the sine-cosine algorithm and particle swarm optimizer for improved global optimization and object tracking. Swarm and Evolutionary Computation, 43, 1–30. https://doi.org/10.1016/j.swevo.2018.02.011.
Gupta, S., Deep, K., Mirjalili, S., & Hoon, J. (2020). A modified sine cosine algorithm with novel transition parameter and mutation operator for global optimization. Expert Systems with Applications, 2020, 113395.
Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems. https://doi.org/10.1016/j.future.2019.02.028.
Kamboj, V. K., Nandi, A., Bhadoria, A., & Sehgal, S. (2020). An intensify Harris Hawks optimizer for numerical and engineering optimization problems. Applied Soft Computing, 89, 106018. https://doi.org/10.1016/j.asoc.2019.106018.
Mirjalili, S., Mohammad, S., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007.
Miao, Z., Yuan, X., Zhou, F., Qiu, X., Song, Y., & Chen, K. (2020). Grey wolf optimizer with an enhanced hierarchy and its application to the wireless sensor network coverage optimization problem. Applied Soft Computing Journal, 96(2020), 106602. https://doi.org/10.1016/j.asoc.2020.106602.
Al-betar, M. A., Awadallah, M. A., Faris, H., Aljarah, I., & Hammouri, A. I. (2018). Natural selection methods for Grey Wolf Optimizer. Expert Systems with Applications, 113, 481–498. https://doi.org/10.1016/j.eswa.2018.07.022.
Golzari, S., Zardehsavar, M. N., Mousavi, A., Saybani, M. R., Khalili, A., & Shamshirband, S. (2018). KGSA: A gravitational search algorithm for multimodal optimization based on k-means niching technique and a novel elitism strategy. Open Mathematics, 16(1), 1582–1606. https://doi.org/10.1515/math-2018-0132.
Mirjalili, S., Mirjalili, S. M., & Hatamlou, A. (2016). Multi-verse optimizer: A nature-inspired algorithm for global optimization. Neural Computing and Applications, 27(2), 495–513. https://doi.org/10.1007/s00521-015-1870-7.
Mirjalili, S., & Lewis, A. (2016). The Whale optimization algorithm. Advances in Engineering Software, 95, 51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008.
Jadhav, A. N., & Gomathi, N. (2018). WGC: Hybridization of exponential grey Wolf optimizer with whale optimization for data clustering. Alexandria Engineering Journal, 57(3), 1569–1584. https://doi.org/10.1016/j.aej.2017.04.013.
Zhou, W., Zhou, P., Wang, Y., & Wang, N. (2019). Station-keeping control of an underactuated stratospheric airship. International Journal of Fuzzy Systems, 21(3), 715–732. https://doi.org/10.1007/s40815-018-0566-4.
Singh, M., & Dhillon, J. S. (2016). Multiobjective thermal power dispatch using opposition-based greedy heuristic search. International Journal of Electrical Power and Energy Systems, 82, 339–353. https://doi.org/10.1016/j.ijepes.2016.03.016.
Yassein, L. (2009). Improvement on LEACH protocol of wireless sensor network (VLEACH). International Journal of Digital Content Technology and its Applications, 3(2), 132–136. https://doi.org/10.4156/jdcta.vol3.issue2.yassein.
Mu, T., & Tang, M. (2010). LEACH-B: An improved LEACH protocol for wireless sensor network. In 2010 6th international conference wireless communication network mobile computing WiCOM 2010 (pp. 2–5). https://doi.org/10.1109/WICOM.2010.5601113.
Younis, O., & Fahmy, S. (2004). HEED: A hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Transactions on Mobile Computing, 3(4), 366–379. https://doi.org/10.1109/TMC.2004.41.
Mirzaie, M., & Mazinani, S. M. (2017). Adaptive MCFL: An adaptive multi-clustering algorithm using fuzzy logic in wireless sensor network. Computer Communications, 111, 56–67. https://doi.org/10.1016/j.comcom.2017.07.005.
Mahajan, S., Malhotra, J., & Sharma, S. (2014). An energy balanced QoS based cluster head selection strategy for WSN. Egyptian Informatics Journal, 15(3), 189–199. https://doi.org/10.1016/j.eij.2014.09.001.
Shankar, T. (xxxx). Whale optimization based energy-efficient cluster head selection algorithm for wireless sensor networks (pp. 1–22).
Guo, L., Li, Q., & Chen, F. (2011). A novel cluster-head selection algorithm based on hybrid Genetic Optimization for wireless sensor networks. Journal Networks, 6(5), 815–822. https://doi.org/10.4304/jnw.6.5.815-822.
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. https://doi.org/10.1016/j.engappai.2014.04.009.
Zeng, B., & Dong, Y. (2016). An improved harmony search based energy-efficient routing algorithm for wireless sensor networks. Applied Soft Computing Journal, 41, 135–147. https://doi.org/10.1016/j.asoc.2015.12.028.
Karaboga, D., Okdem, S., & Ozturk, C. (2012). Cluster based wireless sensor network routing using artificial bee colony algorithm. Wireless Networks, 18(7), 847–860. https://doi.org/10.1007/s11276-012-0438-z.
Chen, R. C., Chang, W. L., Shieh, C. F., & Zou, C. C. (2012). Using hybrid artificial bee colony algorithm to extend wireless sensor network lifetime. In Proceeding 3rd international conference innovation bio-inspired computing application IBICA (pp. 156–161). https://doi.org/10.1109/IBICA.2012.27.
Kumar, R., & Kumar, D. (2016). Multi-objective fractional artificial bee colony algorithm to energy aware routing protocol in wireless sensor network. Wireless Networks, 22(5), 1461–1474. https://doi.org/10.1007/s11276-015-1039-4.
Vinodhini, R., & Gomathy, C. (2020). MOMHR: A dynamic multi-hop routing protocol for WSN using Heuristic based multi-objective function. Wireless Personal Communication, 111(2), 883–907. https://doi.org/10.1007/s11277-019-06891-0.
Ghugar, U., Pradhan, J., Bhoi, S. K., & Sahoo, R. R. (2019). LB-IDS: Securing wireless sensor network using protocol layer trust-based intrusion detection system. Journal Computing Networks Communication., 5, 71.
Ghugar, U., Pradhan, J., & Kumar, S. (2018). PL-IDS: physical layer trust based intrusion detection system for wireless sensor networks. International Journal Information Technology, 10(4), 489–494. https://doi.org/10.1007/s41870-018-0147-7.
Ranjan, R., Sudhabindu, S., Souvik, R., Sourav, S., & Bhoi, K. (2018). Guard against trust management vulnerabilities in Wireless Sensor Network. Arabian Journal for Science and Engineering, 43(12), 7229–7251. https://doi.org/10.1007/s13369-017-3052-7.
Bhoi, S. K., Panda, S. K., & Khilar, P. M. (2013). A density-based clustering paradigm to detect faults in wireless sensor networks. In International conference on advances in computing (pp. 865–871).
Bhoi, S. K., Obaidat, M. S., Puthal, D., Singh, M., & Hsiao, K.-F. (2018). Software defined network based fault detection in industrial wireless sensor networks. In IEEE global communication conference (GLOBECOM) (pp. 1–6).
Singh, M., Bhoi, S. K., & Khilar, P. M. (2017). Geometric constraint-based range-free localization scheme for wireless sensor networks. IEEE Sensors Journal, 17(16), 5350–5366.
Swain, R. R., Khilar, P. M., & Bhoi, S. K. (2018). Heterogeneous fault diagnosis for wireless sensor networks. Ad Hoc Networks, 69, 15–37. https://doi.org/10.1016/j.adhoc.2017.10.012.
Chauhan, S., Singh, M., & Aggarwal, A. K. (2020). Diversity driven multi-parent evolutionary algorithm with adaptive non-uniform mutation. Journal of Experimental and Theoretical Artificial Intelligence, 2020, 1–32.
Ali, M. Z., Awad, N. H., Suganthan, P. N., Shatnawi, A. M., & Reynolds, R. G. (2018). An improved class of real-coded Genetic Algorithms for numerical optimization✰. Neurocomputing, 275, 155–166. https://doi.org/10.1016/j.neucom.2017.05.054.
Wang, H., Wang, W., & Wu, Z. (2013). Particle Swarm optimization with adaptive mutation for multimodal optimization. Applied Mathematics and Computation, 221, 296–305. https://doi.org/10.1016/j.amc.2013.06.074.
Jun, T., & Xiaojuan, Z. (2009). Particle swarm optimization with adaptive mutation. In 2009 WASE international conference information engineering ICIE 2009 (Vol. 2, No. 1, pp. 234–237). https://doi.org/10.1109/ICIE.2009.59.
Verma, S., Sood, N., & Sharma, A. K. (2019). Genetic algorithm-based Optimized Cluster Head selection for single and multiple data sinks in Heterogeneous Wireless Sensor Network. Applied Soft Computing Journal, 85, 105788. https://doi.org/10.1016/j.asoc.2019.105788.
Dhillon, J. S., Parti, S. C., & Kothari, D. P. (2001). Fuzzy decision making in multiobjective long-term scheduling of hydrothermal system.
Eberhart, R., & Kennedy, J. (1995). A new optimizer using particle swarm theory. In Proceeding of sixth international symposium micro machine human science IEEE (pp. 39–43). https://doi.org/10.1109/mhs.1995.494215.
Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2011). Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems. Computing Design, 43(3), 303–315. https://doi.org/10.1016/j.cad.2010.12.015.
Mirjalili, S. (2015). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based System, 89, 228–249. https://doi.org/10.1016/j.knosys.2015.07.006.
Storn, R. (1997) Differrential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces. In Technical report, international computing science institution (Vol. 11).
Askarzadeh, A. (2016). A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm. Computers and Structures, 169, 1–12. https://doi.org/10.1016/j.compstruc.2016.03.001.
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Chauhan, S., Singh, M. & Aggarwal, A.K. Cluster Head Selection in Heterogeneous Wireless Sensor Network Using a New Evolutionary Algorithm. Wireless Pers Commun 119, 585–616 (2021). https://doi.org/10.1007/s11277-021-08225-5
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DOI: https://doi.org/10.1007/s11277-021-08225-5