Abstract
The Wireless Sensor Network (WSN) personifies vital and active functions in multi-disciplinary research sectors, as it can deploy in harsh and antagonistic atmospheres where the deployment of a wired system is not possible. However, designing an energy efficient and durable WSN is still a key challenge. Though the contribution of the clustering mechanism attempts to augment the network LifeTime, the energy consumption in the Cluster Heads (CHs) is rapidly high. This led to the frequent change of CHs and minimized the network’s lifetime. To diminish these issues, we propose an Energy Efficient LifeTime Maximization (EELTM) approach which utilizes the intelligent techniques Particle Swarm Optimization (PSO) and Fuzzy Inference System (FIS). Further, we propose an optimal CH–CR selection algorithm in our approach which exploits the fitness values calculated by the PSO technique to determine two optimal nodes in each cluster to act as CH and Cluster Router (CR). The selected CH exclusively gathers the information from its cluster members, whereas the CR is liable for receiving the gathered information from its CH and transferring it to the BS. Thus, the overhead of CH is reduced. Another intelligent technique is that FIS figures out the radius for each CH, and thereby it partitions the network into unequal clusters. The performance of our proposed EELTM approach is analyzed, and evaluations are elaborated with well-known existing clustering algorithms. To assess the proficiency of EELTM and to evaluate the endurance of the network, efficiency parameters such as total-remaining-energy, first-node-expires and fifty-percent-expires are exploited. The experimental outcomes justify that the EELTM approach surpasses the existing mechanisms by 14%.
Similar content being viewed by others
References
Yick, J.; Mukherjee, B.; Ghosal, D.: Wireless sensor network survey. Comput. Netw. 52(12), 2292–2330 (2008)
Hodge, V.J.; O’Keefe, S.; Weeks, M.; Moulds, A.: Wireless sensor networks for condition monitoring in the railway industry: a survey. IEEE Trans. Intell. Transp. Syst. 16(3), 1088–1106 (2015)
Arikumar, K. S.; Thirumoorthy, K.: Improved user authentication in wireless sensor networks. In: 2011 International Conference on Emerging Trends in Electrical and Computer Technology, pp. 1010–1015. IEEE (2011)
Arikumar, K.S.; Natarajan, V.: Fuzzy based dynamic clustering in wireless sensor networks. In: 2016 8th International Conference on Advanced Computing (ICoAC), pp. 77–82. IEEE (2017)
Rajesh, G.; Vinayagasundaram, B.; Moorthy, G.S.: Data fusion in wireless sensor network using simpson’s 3/8 rule. In: 2014 International Conference on Recent Trends in Information Technology, pp. 1–5. IEEE (2014)
Dondi, D.; Scorcioni, S.; Bertacchini, A.; Larcher, L.; Pavan, P.: An autonomous wireless sensor network device powered by a rf energy harvesting system. In: IECON 2012-38th Annual Conference on IEEE Industrial Electronics Society, pp. 2557–2562. IEEE (2012)
Rajesh, G.; Sundaram, B.V.; Aarthi, C.: Tree based data aggregation to resolve funneling effect in wireless sensor network. Int. J. Comput. Inf. Eng. 9(3), 860–865 (2015)
Sohrabi, K.; Gao, J.; Ailawadhi, V.; Pottie, G.J.: Protocols for self-organization of a wireless sensor network. IEEE Personal Commun. 7(5), 16–27 (2000)
Heinzelman, W.R.; Chandrakasan, A.; Balakrishnan, H.: Energy-efficient communication protocol for wireless microsensor networks. In: Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, 2000, p. 10. IEEE (2000)
Rajesh, G.; Raajini, X.M.; Vinayagasundaram, B.: Fuzzy trust-based aggregator sensor node election in internet of things. Int. J. Internet Protocol Technol. 9(2–3), 151–160 (2016)
Farooq, M.O.; Dogar, A.B.; Shah, G.A.: Mr-leach: Multi-hop routing with low energy adaptive clustering hierarchy. In: 2010 4th International Conference on Sensor Technologies and Applications, pp. 262–268. IEEE (2010)
Akyildiz, I.F.; Su, W.; Sankarasubramaniam, Y.; Cayirci, E.: Wireless sensor networks: a survey. Comput. Netw. 38(4), 393–422 (2002)
Arikumar, K.S.; Natarajan, V.; Clarence, L.S.; Priyanka, M.: Efficient fuzzy logic based data fusion in wireless sensor networks. In: 2016 Online International Conference on Green Engineering and Technologies (IC-GET), pp. 1–6. IEEE (2016)
Tunca, C.; Isik, S.; Donmez, M.Y.; Ersoy, C.: Ring routing: an energy-efficient routing protocol for wireless sensor networks with a mobile sink. IEEE Trans. Mobile Comput. 14(9), 1947–1960 (2015)
Singh, S.K.; Singh, M.; Singh, D.: A survey of energy-efficient hierarchical cluster-based routing in wireless sensor networks. Int. J. Adv. Network. Appl. (IJANA) 2(02), 570–580 (2010)
Katiyar, V.; Chand, N.; Soni, S.: Clustering algorithms for heterogeneous wireless sensor network: a survey. Int. J. Appl. Eng. Res. 1(2), 273 (2010)
Soro, S.; Heinzelman, W.B.: Prolonging the lifetime of wireless sensor networks via unequal clustering. In: Parallel and Distributed Processing Symposium, 2005. Proceedings. 19th IEEE International, p. 8. IEEE (2005)
Kumar, V.; Jain, S.; Tiwari, S.: Energy efficient clustering algorithms in wireless sensor networks: A survey. Int. J. Comput. Sci. Issues (IJCSI) 8(5), 259 (2011)
Javaid, N.; Mohammad, S.N.; Latif, K.; Qasim, U.; Khan, Z.A.; Khan, M.A.: Heer: Hybrid energy efficient reactive protocol for wireless sensor networks. In: 2013 Saudi International Electronics, Communications and Photonics Conference (SIECPC), pp. 1–4. IEEE (2013)
Dorigo, M.; Gambardella, L.M.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1(1), 53–66 (1997)
Wang, J.; Gu, X.; Liu, W.; Sangaiah, A.K.; Kim, H.-J.: An empower hamilton loop based data collection algorithm with mobile agent for wsns. Human Centric Comput. Inf. Sci. 9(1), 1–14 (2019)
Wang, J.; Gao, Y.; Yin, X.; Li, F.; Kim, H.-J.: An enhanced pegasis algorithm with mobile sink support for wireless sensor networks. Wirel. Commun. Mobile Comput. 2018, (2018)
Wang, J.; Gao, Y.; Liu, W.; Sangaiah, A.K.; Kim, H.-J.: An intelligent data gathering schema with data fusion supported for mobile sink in wireless sensor networks. Int. J. Distrib. Sensor Netw. 15(3), 1550147719839581 (2019)
Ni, Q.; Pan, Q.; Du, H.; Cao, C.; Zhai, Y.: A novel cluster head selection algorithm based on fuzzy clustering and particle swarm optimization. IEEE/ACM Trans. Comput. Biol. Bioinf. 14(1), 76–84 (2015)
Ari, A.A.A.; Damakoa, I.; Gueroui, A.; Titouna, C.; Labraoui, N.; Kaladzavi, G.; Yenké, B.O.: Bacterial foraging optimization scheme for mobile sensing in wireless sensor networks. Int. J. Wirel. Inf. Netw. 24(3), 254–267 (2017)
Ari, A.A.A.; Labraoui, N.; Yenké, B.O.; Gueroui, A.: Clustering algorithm for wireless sensor networks: the honeybee swarms nest-sites selection process based approach. Int. J. Sensor Netw. 27(1), 1–13 (2018)
Kuila, P.; Jana, P.K.: Energy efficient clustering and routing algorithms for wireless sensor networks: particle swarm optimization approach. Eng. Appl. Artif. Intell. 33, 127–140 (2014)
Azharuddin, M.; Jana, P.K.: Particle swarm optimization for maximizing lifetime of wireless sensor networks. Comput. Electr. Eng. 51, 26–42 (2016)
Wang, J.; Gao, Y.; Liu, W.; Sangaiah, A.K.; Kim, H.-J.: An improved routing schema with special clustering using pso algorithm for heterogeneous wireless sensor network. Sensors 19(3), 671 (2019)
Alizadeh, M.; Fotoohi, E.; Roshanaei, V.; Safavieh, E.: Clustering based fuzzy particle swarm optimization. In: NAFIPS 2009-2009 Annual Meeting of the North American Fuzzy Information Processing Society, pp. 1–6. IEEE (2009)
Wang, J.; Ju, C.; Gao, Y.; Sangaiah, A.K.; Kim, G.-J.: A PSO based energy efficient coverage control algorithm for wireless sensor networks. Comput. Mater. Contin 56(3), 433–446 (2018)
Elhabyan, R.S.; Yagoub, M.C.: Two-tier particle swarm optimization protocol for clustering and routing in wireless sensor network. J. Netw. Comput. Appl. 52, 116–128 (2015)
Zhou, Y.; Wang, N.; Xiang, W.: Clustering hierarchy protocol in wireless sensor networks using an improved pso algorithm. IEEE Access 5, 2241–2253 (2016)
Wang, J.; Gao, Y.; Zhou, C.; Sherratt, S.; Wang, L.: Optimal coverage multi-path scheduling scheme with multiple mobile sinks for wsns. Comput. Mater. Continua 62(2), 695–711 (2020)
Hacioglu, G.; Kand, V.F.A.; Sesli, E.: Multi objective clustering for wireless sensor networks. Expert Syst. Appl. 59, 86–100 (2016)
Shokouhifar, M.; Hassanzadeh, A.: An energy efficient routing protocol in wireless sensor networks using genetic algorithm. Adv. Environ. Biol. 8(21), 86–93 (2014)
Kaushik, A.; Goswami, M.; Manuja, M.; Indu, S.; Gupta, D.: A binary PSO approach for improving the performance of wireless sensor networks. In: Wireless Personal Communications, pp. 1–35 (2020)
Silva Filho, T .M.; Pimentel, B .A.; Souza, R .M.; Oliveira, A .L.: Hybrid methods for fuzzy clustering based on fuzzy c-means and improved particle swarm optimization. Expert Syst. Appl. 42(17–18), 6315–6328 (2015)
Hamidouche, R.; Aliouat, Z.; Ari, A.A.A.; Gueroui, M.: An efficient clustering strategy avoiding buffer overflow in iot sensors: a bio-inspired based approach. IEEE Access 7, 156733–156751 (2019)
Ari, A.A.A.; Yenke, B.O.; Labraoui, N.; Damakoa, I.; Gueroui, A.: A power efficient cluster-based routing algorithm for wireless sensor networks: honeybees swarm intelligence based approach. J. Netw. Comput. Appl. 69, 77–97 (2016)
Nayak, P.; Devulapalli, A.: A fuzzy logic-based clustering algorithm for wsn to extend the network lifetime. IEEE Sens. J. 16(1), 137–144 (2015)
Bagci, H.; Yazici, A.: An energy aware fuzzy approach to unequal clustering in wireless sensor networks. Appl. Soft Comput. 13(4), 1741–1749 (2013)
Li, C.; Ye, M.; Chen, G.; Wu, J.: An energy-efficient unequal clustering mechanism for wireless sensor networks. In: IEEE International Conference on Mobile Adhoc and Sensor Systems Conference, 2005, p. 8. IEEE (2005)
Zahedi, Z.M.; Akbari, R.; Shokouhifar, M.; Safaei, F.; Jalali, A.: Swarm intelligence based fuzzy routing protocol for clustered wireless sensor networks. Expert Syst. Appl. 55, 313–328 (2016)
Baranidharan, B.; Santhi, B.: Ducf: Distributed load balancing unequal clustering in wireless sensor networks using fuzzy approach. Appl. Soft Comput. 40, 495–506 (2016)
Sert, S.A.; Bagci, H.; Yazici, A.: Mofca: multi-objective fuzzy clustering algorithm for wireless sensor networks. Appl. Soft Comput. 30, 151–165 (2015)
Heinzelman, W.B.; Chandrakasan, A.P.; Balakrishnan, H.: An application-specific protocol architecture for wireless microsensor networks. IEEE Trans. Wirel. Commun. 1(4), 660–670 (2002)
Singh, M.; Soni, S.; Kumar, V. et al.: Clustering using fuzzy logic in wireless sensor networks. In: 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), pp. 1669–1674. IEEE (2016)
Logambigai, R.; Kannan, A.: Fuzzy logic based unequal clustering for wireless sensor networks. Wirel. Netw. 22(3), 945–957 (2016)
Mao, S.; Zhao, C.; Zhou, Z.; Ye, Y.: An improved fuzzy unequal clustering algorithm for wireless sensor network. Mobile Netw. Appl. 18(2), 206–214 (2013)
Gajjar, S.; Sarkar, M.; Dasgupta, K.: Famacrow: fuzzy and ant colony optimization based mac/routing cross-layer protocol for wireless sensor networks. Procedia Comput. Sci. 46, 1014–1021 (2015)
Tam, N.T.; Hai, D.T.; et al.: Improving lifetime and network connections of 3d wireless sensor networks based on fuzzy clustering and particle swarm optimization. Wirel. Netw. 24(5), 1477–1490 (2018)
Bara’a, A.A.; Khalil, E.A.: A new evolutionary based routing protocol for clustered heterogeneous wireless sensor networks. Appl. Soft Comput. 12(7), 1950–1957 (2012)
Poli, R.; Kennedy, J.; Blackwell, T.: Particle swarm optimization. Swarm Intell. 1(1), 33–57 (2007)
Marini, F.; Walczak, B.: Particle swarm optimization (pso). a tutorial. Chemom. Intell. Lab. Syst. 149, 153–165 (2015)
Das, S.; Abraham, A.; Konar, A.: Particle swarm optimization and differential evolution algorithms: technical analysis, applications and hybridization perspectives. In: Advances of Computational Intelligence in Industrial Systems, pp. 1–38. Springer (2008)
Sun, S.; Kim, K.-Y.; Shin, O.-S.; Shin, Y.: Device-to-device resource allocation in lte-advanced networks by hybrid particle swarm optimization and genetic algorithm. Peer Peer Netw. Appl. 9(5), 945–954 (2016)
Ouyang, H.-B.; Gao, L.-Q.; Li, S.; Kong, X.-Y.: Improved global-best-guided particle swarm optimization with learning operation for global optimization problems. Appl. Soft Comput. 52, 987–1008 (2017)
Marler, R.T.; Arora, J.S.: The weighted sum method for multi-objective optimization: new insights. Struct. Multidiscip. Optim. 41(6), 853–862 (2010)
Mendel, J.M.: Fuzzy logic systems for engineering: a tutorial. Proc. IEEE 83(3), 345–377 (1995)
Ross, T.J.: Fuzzy Logic with Engineering Applications. Wiley, Hoboken (2005)
Bai, Y.; Wang, D.: Fundamentals of fuzzy logic control—fuzzy sets, fuzzy rules and defuzzifications. In Advanced Fuzzy Logic Technologies in Industrial Applications, pp. 17–36. Springer (2006)
De Silva, C.W.: Intelligent Control: Fuzzy Logic Applications. CRC Press, Boca Raton (2018)
Mylsamy, R.; Sankaranarayanan, S.: A preference-based protocol for trust and head selection for cluster-based manet. Wirel. Personal Commun. 86(3), 1611–1627 (2016)
Zhou, H.-Y.; Luo, D.-Y.; Gao, Y.; Zuo, D.-C.: Modeling of node energy consumption for wireless sensor networks. Wirel. Sensor Netw. 3(1), 18 (2011)
Rault, T.; Bouabdallah, A.; Challal, Y.: Energy efficiency in wireless sensor networks: a top-down survey. Comput. Netw. 67, 104–122 (2014)
Ali, Q.I.; Abdulmaowjod, A.; Mohammed, H.M.: Simulation & performance study of wireless sensor network (WSN) using matlab. In: 2010 1st International Conference on Energy, Power and Control (EPC-IQ), pp. 307–314. IEEE (2010)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Arikumar, K.S., Natarajan, V. & Satapathy, S.C. EELTM: An Energy Efficient LifeTime Maximization Approach for WSN by PSO and Fuzzy-Based Unequal Clustering. Arab J Sci Eng 45, 10245–10260 (2020). https://doi.org/10.1007/s13369-020-04616-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13369-020-04616-1