Abstract
Wireless sensor networks (WSNs) consist of a large number of sensor nodes, which are primarily employed for collecting data from an environment of interest. Energy resources of WSN nodes are generally restricted, irreplaceable and non-rechargeable. Hence, lowering the level of energy consumption in such networks to save more energy is the key issue in the literature. Clustering, selecting the best Cluster Head (CH) among candidates, and performing the routing only among cluster heads would be an effective approach to reduce the WSN nodes energy consumption. Therefore, cluster-based routing leads to extending the network’s lifetime through aggregating data in CHs, uniformly distributing the energy among nodes, and, consequently, reducing the number of contributing nodes in the routing procedure. In this paper, an energy-aware cluster-based multi-hop routing algorithm is presented, in which the clusters would, if required, re-formed during the routing procedure. Furthermore, like other multi-hop routing algorithms, it guarantees minimizing the energy consumption through balancing energy within the network. In this paper, we have presented a cluster-based multi-hop routing algorithm. In our proposed approach, a combination of two algorithms, namely K-means and Open Source Development Model Algorithm (ODMA), are employed for clustering, and Genetic Algorithm, is applied for multi-hop routing. The simulation results confirm superiority of our proposed method in comparison with MH-FCM, EEWC, and GAFOR algorithms in terms of several metrics such as average residual energy, residual energy variance, number of packets received, number of dead nodes, and network lifetime.
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Shahidinejad, A., Ghobaei-Arani, M., Souri, A., Shojafar, M., & Kumari, S. (2021). Light-edge: A lightweight authentication protocol for IoT devices in an edge-cloud environment. IEEE Consumer Electronics Magazine. https://doi.org/10.1109/MCE.2021.3053543
Dogra, R., Rani, S., & Sharma, B. (2021). A review to forest fires and its detection techniques using wireless sensor network. In Advances in communication and computational technology (pp. 1339–1350). Singapore: Springer.
Nazib, R. A., & Moh, S. (2021). Energy-efficient and fast data collection in UAV-aided wireless sensor networks for hilly terrains. IEEE Access, 9, 23168–23190.
Amutha, J., Nagar, J., & Sharma, S. (2021). A distributed border surveillance (dbs) system for rectangular and circular region of interest with wireless sensor networks in shadowed environments. Wireless Personal Communications, 117(3), 2135–2155.
Jiang, C., Yuan, D., & Zhao, Y. (2009). Towards clustering algorithms in wireless sensor networks-a survey. In 2009 IEEE wireless communications and networking conference (pp. 1–6). Budapest, Hungary: IEEE.
Sharma, S., Bansal, R. K., & Bansal, S. (2013). Issues and challenges in wireless sensor networks. In 2013 international conference on machine intelligence and research advancement (pp. 58–62). Katra, India: IEEE.
Mehta, K., & Pal, R. (2017). Energy efficient routing protocols for wireless sensor networks: A survey. International Journal of Computer Applications, 165(3), 41–46.
Ghobaei-Arani, M., Souri, A., Safara, F., & Norouzi, M. (2020). An efficient task scheduling approach using moth-flame optimization algorithm for cyber-physical system applications in fog computing. Transactions on Emerging Telecommunications Technologies, 31(2), e3770.
Shahidinejad, A., & Barshandeh, S. (2020). Sink selection and clustering using fuzzy-based controller for wireless sensor networks. International Journal of Communication Systems, 33(15), e4557.
Likas, A., Vlassis, N., & Verbeek, J. J. (2003). The global k-means clustering algorithm. Pattern Recognition, 36(2), 451–461.
Hajipour, H., Rostami, H., BehzadiKhourmuji, H., & Oskouei, R. J. (2012). ODMA: A new metaheuristic optimization algorithm based on open source development model. In 2012 12th international conference on intelligent systems design and applications (ISDA) (pp. 758–763). Kochi, India: IEEE.
Rezaeipanah, A., Matoori, S. S., & Ahmadi, G. (2021). A hybrid algorithm for the university course timetabling problem using the improved parallel genetic algorithm and local search. Applied Intelligence, 51(1), 467–492.
Rezaeipanah, A., Mokhtari, M. J., & Boshkani, M. (2020). Providing a new method for link prediction in social networks based on the meta-heuristic algorithm. International Journal of Cloud Computing and Database Management, 1(1), 28–36.
Ghobaei-Arani, M., Rahmanian, A. A., Aslanpour, M. S., & Dashti, S. E. (2018). CSA-WSC: Cuckoo search algorithm for web service composition in cloud environments. Soft Computing, 22(24), 8353–8378.
Rezaeipanah, A., Nazari, H., & Abdollahi, M. (2020). Reducing energy consumption in wireless sensor networks using a routing protocol based on multi-level clustering and genetic algorithm. International Journal of Wireless and Microwave Technologies, 3(1), 1–16.
Rezaeipanah, A., Nazari, H., & Ahmadi, G. (2019). A hybrid approach for prolonging lifetime of wireless sensor networks using genetic algorithm and online clustering. Journal of Computing Science and Engineering, 13(4), 163–174.
Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd annual Hawaii international conference on system sciences (pp. 1–10). Maui, USA: IEEE.
Daanoune, I., Abdennaceur, B., & Ballouk, A. (2021). A comprehensive survey on LEACH-based clustering routing protocols in wireless sensor networks. Ad Hoc Networks, 114, 102409.
Heinzelman, W. B., Chandrakasan, A. P., & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1(4), 660–670.
Arumugam, G. S., & Ponnuchamy, T. (2015). EE-LEACH: Development of energy-efficient LEACH Protocol for data gathering in WSN. EURASIP Journal on Wireless Communications and Networking, 2015(1), 1–9.
Nguyen, T. G., So-In, C., & Nguyen, N. G. (2014). Two energy-efficient cluster head selection techniques based on distance for wireless sensor networks. In 2014 international computer science and engineering conference (ICSEC) (pp. 33–38). Khon Kaen, Thailand: IEEE.
Neto, J. H. B., Rego, A., Cardoso, A. R., & Celestino, J. (2014). MH-LEACH: A distributed algorithm for multi-hop communication in wireless sensor networks. ICN, 2014, 55–61.
Farooq, M. O., Dogar, A. B., & Shah, G. A. (2010). MR-LEACH: multi-hop routing with low energy adaptive clustering hierarchy. In 2010 fourth international conference on sensor technologies and applications (pp. 262–268). Venice, Italy: IEEE.
Su, S., & Zhao, S. (2018). An optimal clustering mechanism based on fuzzy-C means for wireless sensor networks. Sustainable Computing: Informatics and Systems, 18, 127–134.
Demirci, M. (2012). The order-theoretic duality and relations between partial metrics and local equalities. Fuzzy Sets and Systems, 192, 45–57.
Jain, A., & Goel, A. K. (2020). Energy efficient fuzzy routing protocol for wireless sensor networks. Wireless Personal Communications, 110(3), 1459–1474.
Pal, R., Yadav, S., & Karnwal, R. (2020). EEWC: Energy-efficient weighted clustering method based on genetic algorithm for HWSNs. Complex & Intelligent Systems, 6, 391–400.
Shahzad, M. K., Islam, S. M., Hossain, M., Abdullah-Al-Wadud, M., Alamri, A., & Hussain, M. (2021). GAFOR: Genetic algorithm based fuzzy optimized re-clustering in wireless sensor networks. Mathematics, 9(1), 43–61.
Bhola, J., Soni, S., & Cheema, G. K. (2020). Genetic algorithm based optimized leach protocol for energy efficient wireless sensor networks. Journal of Ambient Intelligence and Humanized Computing, 11(3), 1281–1288.
Panchal, A., & Singh, R. K. (2021). EHCR-FCM: Energy efficient hierarchical clustering and routing using fuzzy C-means for wireless sensor networks. Telecommunication Systems, 76(2), 251–263.
Sharma, N., & Gupta, V. (2020). Meta-heuristic based optimization of WSNs localisation problem—A survey. Procedia Computer Science, 173, 36–45.
Gholami, E., Rahmani, A. M., & Fooladi, M. D. T. (2015). Adaptive and distributed TDMA scheduling protocol for wireless sensor networks. Wireless Personal Communications, 80(3), 947–969.
Shahidinejad, A., & Fathi, S. (2018). Wireless-assisted multiple network on chip using microring resonators. Microprocessors and Microsystems, 63, 190–198.
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Rezaeipanah, A., Amiri, P., Nazari, H. et al. An Energy-Aware Hybrid Approach for Wireless Sensor Networks Using Re-clustering-Based Multi-hop Routing. Wireless Pers Commun 120, 3293–3314 (2021). https://doi.org/10.1007/s11277-021-08614-w
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DOI: https://doi.org/10.1007/s11277-021-08614-w