Abstract
Data clustering in wireless sensor network (WSN) is a prominent research area that ensures effective communication through satisfying the energy constraint. The traditional methods engaged themselves in collecting the data from the remote area using WSNs and communicating the data in such a way to enhance the lifetime of the network. However, the energy constraints are not met by the available methods in the literature. The paper concentrates on the hybrid optimization algorithm to tackle the cluster head selection optimally so as to assure the effective communication and energy-aware routing in WSNs. The hybrid optimization algorithm, named dolphin echolocation-based crow search algorithm, is the integration of dolphin echolocation algorithm and crow search algorithm such that the hybrid optimization assures the selection of cluster heads based on the multi-constraints effectively and with high convergence rate. The energy-aware routing is initiated in WSN using the proposed algorithm. Simulation is progressed in the WSN environment using 50, 75, and 100 nodes, and the proposed algorithm offered a better network lifetime with energy remaining in the node to be 0.0476 with 33 alive nodes at the end of 200 rounds.
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References
Negra R, Jemili I, Belghith A (2016) Wireless body area networks: applications and technologies. Procedia Comput Sci 83:1274–1281
Challal Y, Ouadjaout A, Lasla N, Bagaa M, Hadjidj A (2011) Secure and efficient disjoint multipath construction for fault tolerant routing in wireless sensor networks. J Netw Comput Appl 34(4):1380–1397
Omar M, Yahiaoui S, Bouabdallah A (2016) Reliable and energy aware query-driven routing protocol for wireless sensor networks. Ann Telecommun 71(1–2):73–85
Hammoudeh M, Newman R (2015) Adaptive routing in wireless sensor networks: qoS optimisation for enhanced application performance. Inf Fusion 22:3–15
Lee J-S, Cheng W-L (2012) Fuzzy-logic-based clustering approach for wireless sensor networks using energy predication. IEEE Sens J 12(9):2891–2897
Kumar R, Kumar D (2016) Multi-objective fractional artificial bee colony algorithm to energy aware routing protocol in wireless sensor network. Wirel Netw 22(5):1461–1474
Li F, Wang L (2018) Energy-aware routing algorithm for wireless sensor networks with optimal relay detecting. Wirel Pers Commun 98(2):1701–1717
Amgoth T, Jana PK, Thampi S (2015) Energy-aware routing algorithm for wireless sensor networks. Comput Electr Eng 41(C):357–367
Heinzelman WR, Chandrakasan A, Balakrishnan H (2000) Energy-efficient communication protocol for wireless microsensor networks. In: Proceedings of 33rd annual Hawaii international conference on systems science, vol 1
Fersi G, Louati W, Ben Jemaa M (2016) CLEVER: cluster-based energy-aware virtual ring routing in randomly deployed wireless sensor networks. Peer-to-Peer Netw Appl 9(4):640–655
Boulaiche M, Bouallouche-Medjkoune L (2015) EGGR: energy-aware and delivery guarantee geographic routing protocol. Wirel Netw 21(6):1765–1774
Zhang XM, Zhang Y, Yan F, Vasilakos AV (2015) Interference-based topology control algorithm for delay-constrained mobile Ad hoc networks. IEEE Trans Mobile Comput 14(4):742–754
Yang M, Li Y, Jin D, Zeng L, Wu X, Vasilakos AV (2015) Software-defined and virtualized future mobile and wireless networks: a survey. Mobile Netw Appl 20(1):4–18
Haseeb K, Bakar KA, Abdullah AH, Darwish T (2017) Adaptive energy aware cluster-based routing protocol for wireless sensor networks. Wirel Netw 23(6):1953–1966
Kong L, Pan J-S, Snášel V, Tsai P-W, Sung T-W (2017) An energy-aware routing protocol for wireless sensor network based on genetic algorithm. Telecommun, Syst
Shah MA, Abbas G, Dogar AB, Halim Z (2015) Scaling hierarchical clustering and energy aware routing for sensor networks. Complex Adapt Syst Model 3(1):5
Khabiri M, Ghaffari A (2017) “Energy-aware clustering-based routing in wireless sensor networks using cuckoo optimization algorithm. Wirel Pers Commun 98:2473–2495
Purkait R, Tripathi S (2017) Energy aware fuzzy based multi-hop routing protocol using unequal clustering. Wirel Pers Commun 94(3):809–833
Xiao Y, Peng M, Gibson J, Xie G, Du D, Vasilakos A (2011) Tight performance bounds of multi-hop fair-access for MAC protocols in wireless sensor networks and underwater sensor networks. IEEE Trans Mob Comput 11(99):1–1
Meng T, Wu F, Yang Z, Chen G, Vasilakos AV (2016) Spatial reusability-aware routing in multi-hop wireless networks. IEEE Trans Comput 65(1):244–255
Vasilakos AV, Li Z, Simon G, You W (2015) Information centric network: research challenges and opportunities. J Netw Comput Appl 52:1–10
Zhu N, Vasilakos AV (2016) A generic framework for energy evaluation on wireless sensor networks. Wirel Netw 22(4):1199–1220
Allan R (2012) Energy harvesting powers industrial wireless sensor networks. Electon Des Eng Feature 60:22–29
Kumar R, Kumar D (2016) Multi-objective fractional artificial bee colony algorithm to energy aware routing protocol in wireless sensor network. Wirel Netw 22(5):1461–1474
Kumar A, Sachin Y (2016) QMRPRNS: design of QoS multicast routing protocol using reliable node selection scheme for MANETs. Peer-to-Peer Netw, Appl
Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct 169:1–12
Borkar GM, Mahajan AR (2016) “A secure and trust based on-demand multipath routing scheme for self-organized mobile ad-hoc networks. Wirel Netw 23(8):2455–2472
Kaveh A, Farhoudi N (2016) Dolphin echolocation optimization: continuous search space. Adv Comput Des 2(2):175–194
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Mahesh, N., Vijayachitra, S. DECSA: hybrid dolphin echolocation and crow search optimization for cluster-based energy-aware routing in WSN. Neural Comput & Applic 31 (Suppl 1), 47–62 (2019). https://doi.org/10.1007/s00521-018-3637-4
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DOI: https://doi.org/10.1007/s00521-018-3637-4