Abstract
The Internet of Things (IoT) has led to the deployment of many battery-powered sensors in various applications to gather, process, and analyze meaningful data. Clusters of sensors provide for more efficient data collection and increased scalability in such contexts. A low-latency, long-lived routing strategy is described for WSNs that can connect to the Internet of Things. In this research, we present a neuro-fuzzy approach to energy-efficient routing (NFEER) for IoT-enabled WSNs. The novelty of the proposed algorithms is the multiple parameters for the routing in IoT-enabled WSN as consideration of CH distance to sink, cluster size, and residual energy of CH. These variables are used to find the most efficient path across the network, which will help mitigate the hotspot issue. During the operation on the condition “consider only those nodes which have energy greater than the pre-defined threshold energy,” the NFEER relies on energy thresholds to restrict the set of candidate nodes. Extensive simulations are performed to specify the effectiveness of the NFEER, and it elongates stability period by 27.98%, 13.97%, and 10.91% as compared to existing protocols. The stability duration, residual energy, network lifetime, and throughput are enhanced by the proposed method as compared to PSO-Kmean, BMHGA, and FSO-PSO.
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References
Behera TM, Mohapatra SK, Samal UC, Khan MS, Daneshmand M, Gandomi AH (2019) Residual energy-based cluster-head selection in WSNs for IoT application. IEEE Internet Things J 6(3):5132–5139
Sahoo BM, Pandey HM, Amgoth T (2021) A whale optimization (WOA): meta-heuristic based energy improvement clustering in wireless sensor networks. In: 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence), IEEE. (pp 649–654)
Kaur G, Chanak P, Bhattacharya M (2021) Energy-efficient intelligent routing scheme for IoT-enabled WSNs. IEEE Internet Things J 8(14):11440–11449
Sahoo BM, Amgoth T, Pandey HM (2021) Enhancing the network performance of wireless sensor networks on meta-heuristic approach: Grey Wolf Optimization. In: Applications of Artificial Intelligence and Machine Learning. Springer, Singapore. (pp 469–482)
Bara’a AA, Khalil EA (2012) A new evolutionary based routing protocol for clustered heterogeneous wireless sensor networks. Appl Soft Comput 12(7):1950–1957
Sahoo BM, Amgoth T, Pandey HM (2020) Particle swarm optimization based energy efficient clustering and sink mobility in heterogeneous wireless sensor network. Ad Hoc Netw 106:102237
Akkaya K, Younis M (2005) A survey on routing protocols for wireless sensor networks. Ad Hoc Netw 3(3):325–349
Kumar Kashyap P, Kumar S, Dohare U, Kumar V, Kharel R (2019) Green computing in sensors-enabled internet of things: neuro fuzzy logic-based load balancing. Electronics 8(4):384
Nguyen T-T, Pan J-S, Dao T-K, Chu S-C (2018) Load balancing for mitigating hotspot problem in wireless sensor network based on enhanced diversity pollen. J Inf Telecommun 2(1):91–106
Singh SK, Kumar P, Singh JP (2018) An energy efficient protocol to mitigate hot spot problem using unequal clustering in WSN. Wirel Pers Commun 101(2):799–827
Rani S, Ahmed SH, Rastogi R (2020) Dynamic clustering approach based on wireless sensor networks genetic algorithm for Iot applications. Wirel Netw 26:2307–2316
Li J, Luo Z, Xiao J (2020) A hybrid genetic algorithm with bidirectional mutation for maximizing lifetime of heterogeneous wireless sensor networks. IEEE Access 8:72261–72274
Shanthi G, Sundarambal M (2019) FSO–PSO based multihop clustering in WSN for efficient medical building management system. Clust Comput 22(5):12157–12168
Badi A, Mahgoub I (2021) ReapIoT: Reliable, energy-aware network protocol for large-scale internet-of-things (IoT) applications. IEEE Internet Things J 8(17):13582–13592
Lipare A, Edla DR, Dharavath R (2021) Energy efficient fuzzy clustering and routing using BAT algorithm. Wireless Netw 27(4):2813–2828
Nandhini P, Suresh A (2021) Energy efficient cluster based routing protocol using charged system harmony search algorithm in WSN. Wireless Pers Commun 121(3):1457–1470
Gaikwad, M., Umbarkar, A., & Bamane, S. (2020). Large-scale data clustering using improved artificial bee colony algorithm. In: ICT systems and sustainability. Springer. (pp 467–475)
Amiri E, Dehkordi MN (2018) Dynamic data clustering by combining improved discrete artificial bee colony algorithm with fuzzy logic. Int J BioInspired Comput 12(3):164–172
Betzler A, Gomez C, Demirkol I, Paradells J (2016) CoAP congestion control for the internet of things. IEEE Commun Mag 54(7):154–160
Das DK, Dey S (2018) A modified bee colony optimization (mbco) and its hybridization with k-means for an application to data clustering. Appl Soft Comput 70:590–603
Sahoo BM, Pandey HM, Amgoth T (2022) A genetic algorithm inspired optimized cluster head selection method in wireless sensor networks. Swarm Evol Comput 75:101151
Wang J, Cao Y, Li B, Kim H-J, Lee S (2017) Particle swarm optimization-based clustering algorithm with mobile sink for wsns. Future Gener Comput Syst 76:452–457
Pan X, Lu Y, Sun N, Li S (2019) A hybrid artificial bee colony algorithm with modified search model for numerical optimization. Clust Comput 22(2):2581–2588
Karami A, Guerrero-Zapata M (2015) A fuzzy anomaly detection system based on hybrid PSO-Kmeans algorithm in content-centric networks. Neurocomputing 149:1253–1269
El-Shafeiy E, Sallam KM, Chakrabortty RK, Abohany AA (2021) A clustering-based Swarm intelligence optimization technique for the internet of medical things. Expert Syst Appl 173:114648
Sahoo BM, Pandey HM, Amgoth T (2021) GAPSO-H: a hybrid approach towards optimizing the cluster based routing in wireless sensor network. Swarm Evol Comput 60:100772
Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61
Agrawal D, Qureshi MHW, Pincha P, Srivastava P, Agarwal S, Tiwari V, Pandey S (2020) GWOC: grey wolf optimizerâbased clustering scheme for WSNs. Int J Commun Syst 33(8):e4344
Zahedi ZM, Akbari R, Shokouhifar M, Safaei F, Jalali A (2016) Swarm intelligence based fuzzy routing protocol for clustered wireless sensor networks. Expert Syst Appl 55:313–328
Sahoo BM, Amgoth T (2021) An improved bat algorithm for unequal clustering in heterogeneous wireless sensor networks. SN Comput Sci 2(4):1–10
Zhang W, Han G, Feng Y, Lloret J (2017) IRPL: an energy efficient routing protocol for wireless sensor networks. J Syst Architect 75:35–49
Renold AP, Chandrakala S (2017) MRL-SCSO: multi-agent reinforcement learning-based self-configuration and self-optimization protocol for unattended wireless sensor networks. Wirel Pers Commun 96(4):5061–5079
Guo W, Yan C, Lu T (2019) Optimizing the lifetime of wireless sensor networks via reinforcement-learning-based routing. Int J Distrib Sens Netw 15(2):1550147719833541
Nagarajan SM, Deverajan GG, Chatterjee P, Alnumay W, Muthukumaran V (2022) Integration of IoT based routing process for food supply chain management in sustainable smart cities. Sustain Cities Soc 76:103448
Manuel AJ, Deverajan GG, Patan R, Gandomi AH (2020) Optimization of routing-based clustering approaches in wireless sensor network: review and open research issues. Electronics 9(10):1630
Gopal DG, Jerlin MA, Abirami M (2019) A smart parking system using IoT. World Rev Entrep Manag Sustain Dev 15(3):335–345
Gopal DG, Gao XZ, Alzubi JA (2021) IoT and big data impact on various engineering applications. Recent Pat Eng 15(2):119–120
Gopal G (2020) Emerging trends and the importance of network evolution in big data. Recent Adv Comput Sci Commun (Former: Recent Patents Comput Sci) 13(2):158–158
Palanisamy S, Sankar S, Somula R, Deverajan GG (2021) Communication trust and energy-aware routing protocol for WSN using DS theory. Int J Grid High-Perform Comput (IJGHPC) 13(4):24–36
Punia SK, Kumar M, Stephan T, Deverajan GG, Patan R (2021) Performance analysis of machine learning algorithms for big data classification: Ml and ai-based algorithms for big data analysis. Int J E-Health Med Commun (IJEHMC) 12(4):60–75
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Tewari, P., Tripathi, S. An energy efficient routing scheme in internet of things enabled WSN: neuro-fuzzy approach. J Supercomput 79, 11134–11158 (2023). https://doi.org/10.1007/s11227-023-05091-9
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DOI: https://doi.org/10.1007/s11227-023-05091-9