Abstract
Internet of things has become an essential principle of human life with the widespread acceptance of intelligent environments, where everyday objects can communicate through the internet. Mobility is the most critical factor in today’s internet of things devices to apply in real-world applications. Also, proper routing protocol plays a vital role in communication and reduces devices’ energy consumption. The clustering approach is one of the efficient routing techniques to improve energy consumption and enhance network life. Due to the NP-Hard nature of clustering, a krill herd optimization algorithm is proposed in this paper to select the cluster head nodes and intermediate nodes required for routing. The simulation results using NS-3 confirmed that the proposed technique performs better than particle swarm optimization and cuckoo search in terms of network lifetime. The proposed technique increases the total network lifetime by at least 11.1% compared to the current clustering methods.









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References
Pourghebleh, B., Navimipour, N.J.: Data aggregation mechanisms in the internet of things: a systematic review of the literature and recommendations for future research. J. Netw. Comput. Appl. 97, 23–34 (2017)
Azad, P., et al.: The role of structured and unstructured data managing mechanisms in the internet of things. Cluster Comput. 23, 1185–1198 (2019)
Pourghebleh, B., Wakil, K., Navimipour, N.J.: A comprehensive study on the trust management techniques in the internet of Things. IEEE Internet Things J. 6(6), 9326–9337 (2019)
Sokolov, S., et al.: IoT security: threats, risks, attacks. In: Mottaeva, A. (ed.) Proceedings of the XIII International Scientific Conference on architecture and construction 2020, pp. 47–56. Springer, Singapore (2020)
Wang, Z., Qin, X., Liu, B.: An energy-efficient clustering routing algorithm for WSN-assisted IoT. In: 2018 IEEE wireless communications and networking conference (WCNC). IEEE, New Jersy (2018)
Ghanbari, Z., et al.: Resource allocation mechanisms and approaches on the internet of things. Cluster Comput. 22(4), 1253–1282 (2019)
Jain, A., et al.: A route selection approach for variable data transmission in wireless sensor networks. Cluster Comput. 23, 1697–1709 (2020)
Pushpalatha, A., Kousalya, G.: A prolonged network life time and reliable data transmission aware optimal sink relocation mechanism. Cluster Comput. 22(5), 12049–12058 (2019)
Hasan, M.Z., Al-Rizzo, H., Al-Turjman, F.: A survey on multipath routing protocols for QoS assurances in real-time wireless multimedia sensor networks. IEEE Commun. Surv. Tutor. 19(3), 1424–1456 (2017)
Ahmed, B.S., et al.: Aspects of quality in internet of things (IoT) solutions: a systematic mapping study. IEEE Access 7, 13758–13780 (2019)
Kiruthika, J., Khaddaj, S.: Software quality issues and challenges of Internet of Things. In: 2015 14th International symposium on distributed computing and applications for business engineering and science (DCABES). IEEE, New Jersy (2015)
Bures, M., Cerny, T., Ahmed, B.S.: Internet of things: current challenges in the quality assurance and testing methods. In: Kim, K.J., Baek, N. (eds.) Information science and applications 2018. Springer, Singapore (2018)
Al-Turjman, F.M.: Information-centric sensor networks for cognitive IoT: an overview. Ann. Telecommun. 72(1–2), 3–18 (2017)
Pourghebleh, B., JafariNavimipour, N.: Towards efficient data collection mechanisms in the vehicular ad hoc networks. Int. J. Commun. Syst. 32(5), e3893 (2019)
Narendran, M., Prakasam, P.: An energy aware competition based clustering for cluster head selection in wireless sensor network with mobility. Cluster Comput. 22, 11019–11028 (2019)
Choudhury, N., et al.: NCHR: a non-threshold-based cluster-head rotation scheme for IEEE 802.15.4 cluster-tree networks. IEEE Int. Things J. 8, 168–178 (2020)
Choudhury, N., et al.: A non-threshold-based cluster-head rotation scheme for IEEE 802.15.4 cluster-tree networks. In: 2018 IEEE global communications conference (GLOBECOM). IEEE, New Jersy (2018)
Sadrishojaei, M., Jafari Navimipour, N., Reshadi, M., Hosseinzadeh, M.: Clustered routing method in the internet of things using a moth-flame optimization algorithm. Int. J. Commun. Syst. https://doi.org/10.1002/dac.4964
Pantazis, N.A., Nikolidakis, S.A., Vergados, D.D.: Energy-efficient routing protocols in wireless sensor networks: a survey. IEEE Commun. Surv. Tutor. 15(2), 551–591 (2012)
Aloise, D., et al.: NP-hardness of Euclidean sum-of-squares clustering. Mach. Learn. 75(2), 245–248 (2009)
Chen, Y., Wang, H.: Evolutionary energy balanced ant colony algorithm based on WSNs. Cluster Comput. 22(1), 609–621 (2019)
Reddy, M.P.K., Babu, M.R.: Implementing self adaptiveness in whale optimization for cluster head section in internet of things. Cluster Comput. 22(1), 1361–1372 (2019)
Agrawal, D., et al.: GWO-C: grey wolf optimizer-based clustering scheme for WSNs. Int. J. Commun. Syst. 33(8), e4344 (2020)
Gandomi, A.H., Alavi, A.H.: Krill herd: a new bio-inspired optimization algorithm. Commun. Nonlinear Sci. Numer. Simul. 17(12), 4831–4845 (2012)
Abualigah, L.M., Khader, A.T., Hanandeh, E.S.: Hybrid clustering analysis using improved krill herd algorithm. Appl. Intell. 48(11), 4047–4071 (2018)
Kafi, M.A., et al.: A study of wireless sensor networks for urban traffic monitoring: applications and architectures. Procedia Comput. Sci. 19, 617–626 (2013)
FaizanUllah, M., Imtiaz, J., Maqbool, K.Q.: Enhanced three layer hybrid clustering mechanism for energy efficient routing in IoT. Sensors 19(4), 829 (2019)
Halder, S., Ghosal, A., Conti, M.: LiMCA: an optimal clustering algorithm for lifetime maximization of internet of things. Wireless Netw. 25(8), 4459–4477 (2019)
Priyan, M., Devi, G.U.: Energy efficient node selection algorithm based on node performance index and random waypoint mobility model in internet of vehicles. Cluster Comput. 21(1), 213–227 (2018)
Madhurikkha, S., Sabitha, R.: A smart power saving protocol for IoT with wireless energy harvesting technique. Cluster Comput. 22(2), 3313–3324 (2019)
El Alami, H., Najid, A.: ECH: an enhanced clustering hierarchy approach to maximize lifetime of wireless sensor networks. IEEE Access 7, 107142–107153 (2019)
Morsy, N.A., AbdelHay, E.H., Kishk, S.S.: Proposed energy efficient algorithm for clustering and routing in WSN. Wireless Pers. Commun. 103(3), 2575–2598 (2018)
Adnan, M.A., et al.: A novel cuckoo search based clustering algorithm for wireless sensor networks. In: Advanced computer and communication engineering technology, pp. 621–634. Springer, Cham (2016)
Rao, P.S., Jana, P.K., Banka, H.: A particle swarm optimization based energy efficient cluster head selection algorithm for wireless sensor networks. Wireless Netw. 23(7), 2005–2020 (2017)
Hofmann, E.E., et al.: Lagrangian modelling studies of Antarctic krill (Euphausia superba) swarm formation. ICES J. Mar. Sci. 61(4), 617–631 (2004)
Nicol, S.: Living krill, zooplankton and experimental investigations: a discourse on the role of krill and their experimental study in marine ecology. Mar. Fresh. Behav. Physiol. 36(4), 191–205 (2003)
Murphy, E.J., et al.: Scales of interaction between Antarctic krill and the environment. In: Antarctic ocean and resources variability, pp. 120–130. Springer, Berlin (1988)
Gandomi, A.H., Alavi, A.H.: An introduction of krill herd algorithm for engineering optimization. J. Civ. Eng. Manag. 22(3), 302–310 (2016)
Bolaji, A., et al.: A comprehensive review: krill herd algorithm (KH) and its applications. Appl. Soft Comput. 49, 437–446 (2016)
Wang, G.-G., et al.: A hybrid method based on krill herd and quantum-behaved particle swarm optimization. Neural Comput. Appl. 27(4), 989–1006 (2016)
Shopon, M., Adnan, M.A., Mridha, M.F.: Krill herd based clustering algorithm for wireless sensor networks. In: 2016 International workshop on computational intelligence (IWCI). IEEE, New Jersy (2016)
Li, Q., Liu, B.: Clustering using an improved krill herd algorithm. Algorithms 10(2), 56 (2017)
Jiang, P., et al.: Dynamic layered dual-cluster heads routing algorithm based on krill herd optimization in UWSNs. Sensors 16(9), 1379 (2016)
Abualigah, L.M., et al.: A new hybridization strategy for krill herd algorithm and harmony search algorithm applied to improve the data clustering. In: 1st EAI International Conference on computer science and engineering. European Alliance for Innovation (EAI), Belgium (2016)
Sadrishojaei, M., et al.: A new preventive routing method based on clustering and location prediction in the mobile internet of things. IEEE Int. Things J. 8, 10562–10664 (2021)
Riley, G.F., Henderson, T.R.: The ns-3 network simulator. In: Modeling and tools for network simulation, pp. 15–34. Springer, Berlin (2010)
Carneiro, G.: NS-3: Network simulator 3. In: UTM Lab Meeting, vol. 20, pp. 4–5 (2010)
Taheri, H., et al.: An energy-aware distributed clustering protocol in wireless sensor networks using fuzzy logic. Ad Hoc Netw. 10(7), 1469–1481 (2012)
Sharma, M., Shaw, A.K.: Transmission time and throughput analysis of EEE LEACH, LEACH and direct transmission protocol: a simulation based approach. Adv. Comput. 3(5), 97 (2012)
Murali, S., Jamalipour, A.: Mobility-aware energy-efficient parent selection algorithm for low power and lossy networks. IEEE Int. Things J. 6(2), 2593–2601 (2018)
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Sadrishojaei, M., Navimipour, N.J., Reshadi, M. et al. A new clustering-based routing method in the mobile internet of things using a krill herd algorithm. Cluster Comput 25, 351–361 (2022). https://doi.org/10.1007/s10586-021-03394-1
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DOI: https://doi.org/10.1007/s10586-021-03394-1