Maximization of Lifetime for Wireless Sensor Networks Based on Energy Efficient Clustering Algorithm
Authors: Frodouard Minani
Since last decade, wireless sensor networks (WSNs) have been used in many areas like health care, agriculture, defense, military, disaster hit areas and so on. Wireless Sensor Networks consist of a Base Station (BS) and more number of wireless sensors in order to monitor temperature, pressure, motion in different environment conditions. The key parameter that plays a major role in designing a protocol for Wireless Sensor Networks is energy efficiency which is a scarcest resource of sensor nodes and it determines the lifetime of sensor nodes. Maximizing sensor node’s lifetime is an important issue in the design of applications and protocols for Wireless Sensor Networks. Clustering sensor nodes mechanism is an effective topology control approach for helping to achieve the goal of this research. In this paper, the researcher presents an energy efficiency protocol to prolong the network lifetime based on Energy efficient clustering algorithm. The Low Energy Adaptive Clustering Hierarchy (LEACH) is a routing protocol for clusters which is used to lower the energy consumption and also to improve the lifetime of the Wireless Sensor Networks. Maximizing energy dissipation and network lifetime are important matters in the design of applications and protocols for wireless sensor networks. Proposed system is to maximize the lifetime of the Wireless Sensor Networks by choosing the farthest cluster head (CH) instead of the closest CH and forming the cluster by considering the following parameter metrics such as Node’s density, residual-energy and distance between clusters (inter-cluster distance). In this paper, comparisons between the proposed protocol and comparative protocols in different scenarios have been done and the simulation results showed that the proposed protocol performs well over other comparative protocols in various scenarios.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.3299315Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 489
 A Tutorial on Clustering Algorithms. http://home.deib.polimi.it/matteucc/Clustering/tutorial_html/.
 Amodu, O.A.; Mahmood, R.A. Impact of the energy-based and location-based LEACH secondary cluster aggregation on WSN lifetime. Wirel. Netw. 2018, 24, 1379–1402.
 Zaatouri, I.; Guiloufi, A. B.; Alyaoui, N.; Kachouri, A. A Comparative Study of the Energy Efficient Clustering Protocols in Heterogeneous and Homogeneous Wireless Sensor Networks. Wirel. Pers. Commun. 2017, 97, 6453–6468.
 Cao, Y.; Zhang, L. Data fusion of heterogeneous network based on BP neural network and improved SEP. In Proceedings of the 2017 9th International Conference on Advanced Infocomm Technology (ICAIT), Chengdu, China, 22–24 November 2017; pp. 138–142.
 Javaid, N.; Qureshi, T. N.; Khan, A. H.; Iqbal, A.; Akhtar, E.; Ishfaq, M. EDDEEC: Enhanced Developed Distributed Energy-efficient Clustering for Heterogeneous Wireless Sensor Networks. Procedia Comput. Sci. 2013, 19, 914–919.
 Younis, O.; Fahmy, S. HEED: A hybrid, energy-efficient, distributed clustering approach for Ad hoc sensor networks. IEEE Trans. Mob. Comput. 2004, 3, 366–379.
 Lakhlef, H. A multi-level clustering scheme based on cliques and clusters for wireless sensor networks. Comput. Electr. Eng. 2015, 48, 436–450.
 Bozorgi, S. M.; Shokouhi Rostami, A.; Hosseinabadi, A. A. R.; Balas, V. E. A new clustering protocol for energy harvesting-wireless sensor networks. Comput. Electr. Eng. 2017, 64, 233–247.
 Elhabyan, R.; Shi, W.; St-Hilaire, M. A Pareto optimization-based approach to clustering and routing in Wireless Sensor Networks. J. Netw. Comput. Appl. 2018, 114, 57–69.
 Rani, S.; Malhotra, J.; Talwar, R. Energy efficient chain based cooperative routing protocol for WSN. Appl. Soft Comput. 2015, 35, 386–397.
 Saranya, V.; Shankar, S.; Kanagachidambaresan, G.R. Energy Efficient Clustering Scheme (EECS) For Wireless Sensor Network with Mobile Sink. Wirel. Pers. Commun. 2018, 100, 1553–1567.
 Wang, Q.; Guo, S.; Hu, J.; Yang, Y. Spectral partitioning and fuzzy C-means based clustering algorithm forbig data wireless sensor networks. EURASIP J. Wirel. Commun. Netw. 2018, 54, 1–11.
 Heinzelman, W. B.; Chandrakasan, A. P.; Balakrishnan, Application-specific protocol architecture for wireless micro sensor networks. IEEE Trans. Wirel. Commun. 2002, 1, 660–670.
 Xie, W.; Zhang, Q.; Sun, Z.; Zhang, F. A Clustering Routing Protocol for WSN Based on Type-2 Fuzzy Logic and Ant Colony Optimization. Wirel. Pers. Commun. 2015, 84, 1165–1196.
 Arjunan, S.; Sujatha, P. Lifetime maximization of wireless sensor network using fuzzy based unequal clustering and ACO based routing hybrid protocol. Appl. Intell. 2018, 48, 2229–2246.
 Azharuddin, M.; Jana, P. K. PSO-based approach for energy-efficient and energy-balanced routing and clustering in wireless sensor networks. Soft Comput. 2017, 21, 6825–6839.
 Tam, N. T.; Hai, D. T.; Son, L. H.; Vinh, L. T. Improving lifetime and network connections of 3D wireless sensor networks based on fuzzy clustering and particle swarm optimization. Wirel. Netw. 2018, 24, 1477–1490.
 Chidean, M.I.; Morgado, E.; Del Arco, E.; Ramiro-Bargueno, J.; Caamano, A.J. Scalable Data-Coupled Clustering for Large Scale WSN. IEEE Trans. Wirel. Commun. 2015, 14, 4681–4694.
 Lalwani, P.; Das, S.; Banka, H.; Kumar, C. CRHS: Clustering and routing in wireless sensor networks using harmony search algorithm. Neural Comput. Appl. 2018, 30, 639–659.
 Alia, O.M.D. A dynamic harmony search-based fuzzy clustering protocol for energy-efficient wireless sensor networks. Ann. Telecomm. 2018, 73, 353–365.
 Tyagi, S.; Tanwar, S.; Kumar, N.; Rodrigues, J.J.P.C. Cognitive radio-based clustering for opportunistic shared spectrum access to enhance lifetime of wireless sensor network. Pervasive Mob. Comput. 2015, 22, 90–112.
 Zahedi, A.; Arghavani, M.; Parandin, F.; Arghavani, A. Energy Efficient Reservation-Based Cluster Head Selection in WSNs. Wirel. Pers. Commun. 2018, 100, 667–679.
 Sivakumar, B.; Sowmya, B. An Energy Efficient Clustering with Delay Reduction in Data Gathering (EE-CDRDG) Using Mobile Sensor Node. Wirel. Pers. Commun. 2016, 90, 793–806.
 Mehmood, A.; Lloret, J.; Sendra, S. A secure and low-energy zone-based wireless sensor networks routing protocol for pollution monitoring. Wirel. Commun. Mob. Comput. 2016, 16, 2869–2883.
 Banerjee, S.; Khuller, S. A clustering scheme for hierarchical control in multi-hop wireless networks. In Proceedings of the Twentieth Annual Joint Conference of the IEEE Computer and Communications Societies, Anchorage, AK, USA, 22–26 April 2001; Volume 2, pp. 1028–1037.
 Heinzelman,W.B.; Chandrakasan, A.P.; Balakrishnan, Application-specific protocol architecture for wireless micro sensor networks. IEEE Trans. Wirel. Commun. 2002, 1, 660–670.