Abstract
In wireless sensor networks (WSN), computational challenges exist in determining a global energy optimized communication routing in large spaced network. WSN challenges can be handled by applying heuristic bio-inspired computational intelligence optimization methods. In WSN, Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm is most frequently used hierarchical routing algorithm in spite of certain limitations. The proposed work is addressed in this direction, to improve issues of LEACH, such as identification of reliable CH, selection of energy efficient inter and intra route communication using relay nodes, so as to extend the network lifespan. The proposed work applies Particle Swarm Optimization (PSO) and Wolf Search optimization methods to improve the performance of LEACH algorithm. PSO is castoff for cluster formation and Wolf search for identification of two relay nodes: intra and inter relay node. The Spyder-py3 tool is used to simulate the proposed algorithm: LEACH PSO Wolf search based Optimization (LEACH-PWO). The proposed work compared with original LEACH, power-efficient gathering in sensor information systems, Ant Cuckoo optimized using energy-efficient data aggregation, and Genetic Algorithm data Aggregation LEACH protocols indicate prolonged lifetime of network and increased throughput.
Similar content being viewed by others
References
Akkaya K, Younis M (2003) A survey on routing protocols for wireless sensor networks. Elsevier, Oxford, pp 325–349
Nayak A, Stojmenovic I (2010) Wireless sensor and actuator networks: algorithms and protocols for scalable coordination and data communication. John Wiley & Sons, Oxford
Perillo M, Heinzelman W (2005) Fundamental algorithms and protocols for wireless and mobile networks. CRC Hall, Boca Raton
Akyildiz IF, Su W, Sankarasubramaniam Y, Cayirci E (2002) Wireless sensor networks: a survey. Comput Netw 38(4):393–422
Willig A, Karl H (2005) Data transport reliability in wireless sensor network: a survey of issues and solutions. Praxis der Informations verarbeitung und Kommunikation 28(2):86–92
Strunk Jr W, White EB (2010) W-LEACH: Weighted Low Energy Adaptive Clustering Hierarchy aggregation algorithm for data streams in wireless sensor networks. In: Data Mining Workshops (ICDMW), IEEE international conference on. IEEE, The elements of style, 3rd ed. Macmilla, New York
Prasad DR, Naganjaneyulu PV, Satya Prasad K (2017) Bio-inspired approach for energy aware cluster head selection in wireless sensor networks, Computer Communication, Networking and Internet Security. Springer, Singapore, pp 541–550
Devika G, Karegowda AG (2015) A pragmatic study of LEACH and its descendant routing protocols in WSN. Int J Comput Intell Inform 4(4):300–307
Devika G, Karegowda AG (2015) Performance enhancement of LEACH, V-LEACH and MOD-LEACH clustering routing protocols for wireless sensor networks. In: International conference on research in business management & information technology, April 29–30, 2015
Devika G, Nayana S (2015) Cluster based routing protocols in WSN: an energy efficient based comparison. Int J Sci Progress Res 12:1
Naeimi S et al (2017) A survey on the taxonomy of cluster-based routing protocols for homogeneous wireless sensor networks. Sensors 12(6):7350–7409
Ran Ge, Zhang Huazhong, Gong Shulan (2010) Improving on LEACH protocol of wireless sensor networks using fuzzy logic. J Inf Comput Sci 7(3):767–775
Chen J (2011) Improvement of LEACH routing algorithm based on use of balanced energy in wireless sensor networks. In: ICIC, vol 1
Shankar T, Shanmugavel S, Rajesh A (2016) Hybrid HSA and PSO algorithm for energy efficient cluster head selection in wireless sensor networks. Swarm Evol Comput 12:1–10
Jin Wang Yu, Gao Xiang Yin, Li Feng, Kim Hye-Jin (2018) An enhanced PEGASIS algorithm with mobile sink support for wireless sensor networks. Wirel Commun Mob Comput. https://doi.org/10.1155/2018/9472075
Bongale AM, Nirmal CR, Kumar A, Bongale M (2020) Energy efficient intra cluster data aggregation for WSN. Int J Inf Technol. https://doi.org/10.1007/s41870-020-00419-7
Kuila P, Jana PK (2014) Energy efficient clustering and routing algorithms for wireless sensor networks: particle swarm optimization approach. Eng Appl Artif Intell 164:127–140
Gupta V, Pandey R (2014) Research on energy balance in hierarchical clustering protocol architecture for WSN. In: International conference on parallel, distributed and grid computing (PDGC), 2014. IEEE, pp 115–119
Mehra PS, Alam MND (2017) Zonal based approach for clustering in heterogeneous WSN. Int J Inf Technol:1–9
Han EG, Zhang L (2018) WPO-EECRP: energy-efficient clustering routing protocol based on weighting and parameter optimization in WSN. Wirel Pers Commun 98(1):1171–1205
Kumar R, Kumar D (2015) Multi-objective fractional artificial bee colony algorithm to energy aware routing protocol in wireless sensor network. Wirel Netw 22(5):1461–1474
Pal V, Yogita SG, Yadav RP (2015) Cluster head selection optimization based on genetic algorithm to prolong lifetime of wireless sensor networks. Procedia Comput Sci 57:1417–1423
Yadav VK, Yadav S (2018) Distributed energy efficient clustering algorithm to optimal cluster head by using biogeography based optimization. Mater Today 5(1):145–155
Kurubanshi S, Rathkanthiwar S (2018) Increasing the lifespan of wireless adhoc networking using probabilistic approaches: a survey. Int J Inf Technol 10:537–542
Azharuddin M, Jana PK (2015) A distributed algorithm for energy efficient and fault tolerant routing in wireless sensor networks. Wirel Netw 21(1):251–267
Kuila P, Jana PK (2015) A novel differential evolution based clustering algorithm for wireless sensor networks. Appl Soft Comput 25:414–425
Zhang D, Wang X, Song X, Zhang T, Zhu Y (2015) A new clustering routing method based on PECE for WSN. EURASIP J Wirel Commun Netw 162:1–13
Shanthi G, Sundarambal M (2018) FSO–PSO based multihop clustering in WSN for efficient medical building management system. Cluster Comput:1–12
Kalaikumar K, Baburaj E (2018) FABC-MACRD: fuzzy and artificial bee colony based implementation of MAC, clustering, routing and data delivery by crosslayer approach in WSN. Wirel Pers Commun 103(2):1633–1655
Gavhale M, Saraf PD (2016) Survey on algorithms for efficient cluster formation and cluster head selection in MANET. Procedia Comput Sci 78:477–482
Singh SK, Singh JP (2018) An energy efficient protocol to mitigate hot spot problem using unequal clustering in WSN. Wirel Personal Commun 101(2):799–827
Farmana H, Jan B, Javed H, Ahmad N, Iqbal J, Arshad M, Ali S (2018) Multicriteria based zone head selection in Internet of Things based wireless sensor networks. Future Gen Comput Syst 87:364–371
Rajpoot P, Dwivedi P (2018) Optimized and load balanced clustering for wireless sensor networks to increase the lifetime of WSN using MADM approaches. Wirel Netw:1–37
Sahoo RR, Sardar AR, Singh M, Ray S, Sarkar SK (2016) A bio inspired and trust based approach for clustering in WSN. Nat. Comput 15(3):423–434
Singh T, Pal S, Sharma SC (2018) An improved cluster-based routing algorithm for energy optimisation in wireless sensor networks. Int J Wirel Mob Comput 14(1):82–89
Mechta D et al (2014) LEACH-CKM: Low energy adaptive clustering hierarchy protocol with K-means and MTE. In: Innovations in information technology (INNOVATIONS), 2014 10th international conference on. IEEE
Bakaraniya P, Mehta S (2014) K-LEACH: an improved LEACH protocol for lifetime improvement in WSN. Int J Eng Trends Technol 5(5):1521–1526
Heinzelman WB, Chandrakasan AP, Balakrishnan H (2002) An application-specific protocol architecture for wireless microsensor networks. IEEE Trans Wirel Commun 1(4):660–670
Thakkar A, Kotecha K (2014) Alive nodes based improved low energy adaptive clustering hierarchy for wireless sensor network. Adv Comput Netw Inform 1:51–58
Xiangning F, Yulin S (2007) Improvement on LEACH protocol of wireless sensor network. In: Sensor technologies and applications, Sensor Comm 2007. International Conference, IEEE
Thakkar A, Kotecha K (2014) Alive nodes based improved low energy adaptive clustering hierarchy for wireless sensor network. Adv Comput Netw Inform 2:51–58
Mohanasundaram R, Periasamy PS (2015) Clustering based optimal data storage strategy using hybrid swarm intelligence in WSN. Wirel Pers Commun 85(3):1381–1397
Kumar N, Ghanshyam C, Sharma AK (2015) Effect of multi-path fading model on T-ANT clustering protocol for WSN. Wirel Netw 21(4):1155–1162
Yadav A, Kumar S, Vijendra S (2018) Network life time analysis of WSNs using particle swarm optimization. Procedia Comput Sci 132:805–815
Bhatia T, Kansal S, Goel S, Verma AK (2016) A genetic algorithm based distance-aware routing protocol for wireless sensor networks. Comput Electr Eng 56:441–455
Murugan TS, Sarkar A (2018) Optimal cluster head selection by hybridisation of firefly and grey wolf optimisation. Int J Wirel Mobi Comput 14(3):296–305
Shankar A, Jaisankar N (2018) Security enabled cluster head selection for wireless sensor network using improved firefly optimization. In: Proceedings of the eighth international conference on soft computing and pattern recognition, pp 176–192
Parwekar P, Rodda S, Vani Mounika S (2018) Comparison between genetic algorithm and PSO for wireless sensor networks. Springer, Singapore, pp 403–411
Singh SP, Sharma SC (2017) PEECA: PSO-based energy efficient clustering algorithm for wireless sensor networks. Int J Comput Netw Inf Secur 2017:31–37
Saleem M, Di Caro GA, Farooq M (2011) Swarm intelligence based routing protocol for wireless sensor networks: survey and future directions. Inf Sci 181(20):4597–4624
Zungeru AM, Ang LM, Seng KP (2012) Classical and swarm intelligence based routing protocols for wireless sensor networks: a survey and comparison. J Netw Comput Appl 35(5):1508–1536
Kulkarni RV, Venayagamoorthy GK (2011) Particle swarm optimization in wireless-sensor networks: a brief survey. IEEE Trans Syst Man Cybern Part C Appl Rev 41(2):262–267
Yadav RK, Kumar V, Kumar R (2015) A discrete particle swarm optimization base clustering algorithm for wireless sensor networks. In: Emerging ICT for bridging the future—Proceedings of 49th annual conventional of the computer society of India CSI, vol 2, pp 137–144
Hashim HA, Ayinde BO, Abido MA (2016) Optimal placement of relay nodes in wireless sensor network using artificial bee colony algorithm. J Netw Comput Appl 1(64):239–248
Han X, Cao X, Lloyd EL, Shen CC (2010) Fault-tolerant relay node placement in heterogeneous wireless sensor networks. IEEE Trans Mob Comput 9(5):643–656
Devika G, Ramesh, Karegowda AG (2017) Bio-inspired ant-cuckoo energy efficient data aggregation algorithm: a solution for routing problem of wireless sensor networks [ACEED]. In: Second IEEE International conference on emerging computation and information technologies [ICECIT], SIT, Tumakuru, Dec-2017
Chugha A, Panda S (2018) Strengthening clustering through relay nodes in sensor networks, sciencedirect, international conference on computational intelligence and data science (ICCIDS 2018), procedia computer science, vol 132, pp 689–695. https://doi.org/10.1016/j.procs.2018.05.072
Chakraborty UK, Das SK, Abbott TE (2012) Energy-efficient routing in hierarchical wireless sensor networks using differential-evolution based memetic algorithm. In: IEEE congress on evolutionary computation (CEC), pp 1–8
Kuila P, Jana PK (2014) Approximation schemes for load balanced clustering in wireless sensor networks. J Supercomput 68(1):87–105
Azharuddin M, Jana PK (2017) PSO-based approach for energy-efficient and energy-balanced routing and clustering in wireless sensor networks. Soft Comput 21(22):6825–6839
Yadav RK, Kumar V, Kumar R (2015) A discrete particle swarm optimization base clustering algorithm for wireless sensor networks. In Satapathy S, Goovardhan A, Raju K, Mandal J (eds) Emerging, ICT for bridging the future proceedings of the 49th annual convention of the computer society of India CSI, Springer, vol 3, pp 137–144
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Devika, G., Ramesh, D. & Karegowda, A.G. Energy optimized hybrid PSO and wolf search based LEACH. Int. j. inf. tecnol. 13, 721–732 (2021). https://doi.org/10.1007/s41870-020-00597-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s41870-020-00597-4