Abstract
Wireless Sensor Network (WSN) is very effective for research community in recent years in demand and adaptive applications. The nodes of WSN are limited with their energy which requires the energy to be utilized in efficient manner. The factor of energy depletion would be reduced by grouping the nodes to support routing and data collection. Different algorithms recommended towards the energy management in different conditions. This research work proposes an Energy Efficient Cluster based Multilevel Hierarchical Routing (EECMHR) technique to support effective data collection in WSN. The method splits the entire network region into number of regions and clusters where each cluster has single Cluster Head (CH). The nodes of WSN are grouped in hierarchical level which performs routing through the CH identified which is rotated at each timestamp according to energy conditions. EECMHR approach has been simulated by using the Network Simulator 2 under different scenarios, and its performance is compared with existing clustering algorithms.
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20 June 2022
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s12652-022-04174-4
References
Ansa Sherman S (2015) Enhancing the lifetime of sensor network with energy awareness and clutter adaptability. In: IEEE, vol 5, p 2
Bithas PS et al (2013) Mitigating shadowing effects through cluster-head cooperation techniques. IET Netw 2(2):71–80
Jia D et al (2016) Dynamic cluster head selection method for wireless sensor network. IEEE Sensor J 16(8):2746–2754
Jiang H, Jin S, Wang C (2011) Prediction or not? An energy-efficient framework for clustering-based data collection in wireless sensor networks. IEEE Trans Parallel Distrib Syst 22(6):1064–1071
Kalaiselvi K et al (2018) Genetic algorithm based sensor node classifications in wireless body area networks (WBAN). Cluster Comput 22(5):12849–12855
Krishnakumar A, Anuratha V (2017) An energy-efficient cluster head selection of leach protocol for wireless sensor networks. In: IEEE, vol 4, p 1
Kumar P (2014) Life time enhancement of wireless sensor network using fuzzy c means clustering algorithm. In: IEEE, vol 3, no 1
Kumaresan K, Krishnan A (2014) A new approach of data gathering using mobile sink and support-in cluster head in WSN. Adv Nat Appl Sci 8(17):83–91
Lee JS, Cheng WL (2012) Fuzzy-logic-based clustering approach for wireless sensor networks using energy prediction. IEEE Sensor J 12(9):2891–2897
Lin H et al (2015) Energy efficient clustering protocol for large-scale sensor networks. IEEE Sensors J 15(12):7150–7160
Liu X et al (2017) Joint design of energy-efficient clustering and data recovery for wireless sensor networks. IEEE Access 5:3646–3656
Mansouri M et al (2013) Secure and robust clustering for quantized target tracking in wireless sensor networks. J Commun Netw 15(2):164–172
Mezghani M (2019) An efficient multi-hops clustering and data routing for WSNs based on Khalimsky shortest paths. J Ambient Intell Humaniz Comput 10:1275–1288
Nayak P, Devulapalli A (2015) A fuzzy logic based clustering algorithm for WSN to extend the network lifetime. IEEE Sensor J 16(1):137–144
Nehra NK et al. (2009) Neural network based energy efficient clustering and routing in wireless sensor networks. In: IEEE, vol 3, no 10
Park GY et al. (2013) A novel cluster head selection method based on a k-means algorithm for energy efficient wireless sensor network. In: IEEE, vol 3, no 1
Rohin R, Ravi S (2017) Detection of residual nodes in wireless sensor networks by node weighting algorithm. IJPPAS 5(1)
Shah GA, Alagoz F et al (2014) A spectrum-aware clustering for efficient multimedia routing in cognitive radio sensor networks. IEEE Trans Veh Technol 63(7):3369–3380
Sharma DK et al (2019) kROp: k-Means clustering based routing protocol for opportunistic networks. J Ambient Intell Humaniz Comput 10(4):1289–1306
Sun ZG et al. (2009) An efficient routing protocol based on two-step cluster head selection for wireless sensor networks. In: IEEE, vol 3, no 1
Thakkar A, Kotecha K (2014) Cluster head election for energy and delay constraint applications of wireless sensor network. IEEE Sensor J 14(8):2658–2664
Thenmalar K, Ramesh R (2017) Self adaptive hybrid differential evolution algorithm (SAHDEA) for dynamic economic emission power dispatch (EEPD) with valve point effects. IJPPAAS 5(1):192–205
Thenmalar K et al (2015) Opposition based differential evolution algorithm for dynamic economic emission load dispatch (EELD) with emission constraints and valve point effects. JEET 10(4):1508–1517
Torshizi ES, Yousefi S, Bagherzadeh J (2012) Life time maximization for connected target coverage in wireless sensor networks with sink mobility. In: 6th international symposium on telecommunications (IST). https://doi.org/10.1109/istel.2012.6483084
Twayej W, Khan M (2017) Network performance evaluation of M2M with self-organizing cluster head to sink mapping. IEEE Sensor J 17(15):4962–4974
Ullah F et al (2019) Traffic priority based delay-aware and energy efficient path allocation routing protocol for wireless body area network. J Ambient Intell Humaniz Comput 10:3775–3794
Xu Z et al (2016) Joint clustering and routing design for reliable and efficient data collection in large-scale wireless sensor networks. IEEE Internet Things J 3(4):520–532
Younis O, Fahmy S (2004) HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Trans Mob Comput 3(4):366–379
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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s12652-022-04174-4
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Kumaresan, K., Kalyani, S.N. RETRACTED ARTICLE: Energy Efficient Cluster based Multilevel Hierarchical Routing for multi-hop Wireless Sensor Network. J Ambient Intell Human Comput 12, 3821–3830 (2021). https://doi.org/10.1007/s12652-020-01700-0
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DOI: https://doi.org/10.1007/s12652-020-01700-0