Energy efficient routing for improving lifetime in MWSN: A clustering approach

Yıl 2024, Cilt: 8 Sayı: 4, 666 - 676, 31.10.2024

Ranadeep Dey , Parag Kumar Guha Thakurta

https://doi.org/10.31127/tuje.1481281

Öz

A Mobile wireless sensor network (MWSN) consists of mobile sensor nodes, which can be deployed in any specific environment, and due to its’ mobility it can perform with rapid topological transformations of a network. The sensor nodes having limited battery power are used to collect specific data and this raw data is sent to a static sink node of the network. Under such a scenario, to avoid frequent disconnections due to topological change in the network and can avail more reliable data transmissions in energy awareness perspective, an energy efficient routing protocol for MWSNs to improve its lifetime is proposed here by utilizing a clustering approach. A MWSN with random number of sensor nodes are initially considered and then, clustering algorithm K-means is used to determine a predefined number of clusters with their initial cluster heads (CHs) and centroid locations of these clusters is also determined. The role of these CHs is to elect our DDBLACH (distance to sink and cluster centroid with battery level aware cluster head) nodes from each cluster, by sending and receiving intra-cluster messages among other member sensor nodes. A DDBLACH node is determined by using three factors, such as minimum distance from cluster centroid location, minimum distance from sink and the maximum battery level of the node from each cluster. These DDBLACH nodes are used to collect data from intra-cluster sensors and thereafter, send those towards sink node for further processing using tree-based hierarchical routes. Finally, an energy efficient routing technique for MWSNs is proposed for data transmission from DDBLACH nodes of clusters to sink of the network. Simulation results indicate the superiority of our proposed scheme over other existing methods in various aspects, such as improving more data packet transmission by 14%-23%, presence of alive nodes and subsequently average network lifetime by 5%-24%.

Anahtar Kelimeler

MWSN, Energy, Clustering, Routing, Network lifetime

Etik Beyan

The authors sincerely declare there are no conflicts of interest.

Teşekkür

The authors wish to express their gratitude to the editor and anonymous reviewers for their constructive suggestions and comments that have helped in significantly shaping the manuscripts.

Kaynakça

• Luo, R. C., & Chen, O. (2012). Mobile sensor node deployment and asynchronous power management for wireless sensor networks. IEEE Transactions on Industrial Electronics, 59(5), 2377-2385.
• Temene, N., Sergiou, C., Georgiou, C., & Vassiliou, V. (2022). A survey on mobility in wireless sensor networks. Ad Hoc Networks, 125, 102726.
• Yıldız, A. (2019). Predicting the energy production of a rooftop PV plant by using differential evolution algorithm. Turkish Journal of Engineering, 3(3), 106-109. https://doi.org/10.31127/tuje.466953
• Ramasamy, V. (2017). Mobile wireless sensor networks: An overview. Wireless Sensor Networks - Insights and Innovations. InTech.
• Mohamed, S. M., Hamza, H. S., & Saroit, I. A. (2017). Coverage in mobile wireless sensor networks (M-WSN): A survey. Computer Communications, 110, 133-150.
• Liu, X. (2015). Atypical hierarchical routing protocols for wireless sensor networks: A review. IEEE Sensors Journal, 15(10), 5372-5383.
• Sabor, N., Sasaki, S., Abo-Zahhad, M., & Ahmed, S. M. (2017). A comprehensive survey on hierarchical-based routing protocols for mobile wireless sensor networks: Review, taxonomy, and future directions. Wireless Communications and Mobile Computing, Hindawi. https://doi.org/10.1155/2017/2818542
• Hassan, A. A.-H., Shah, W. M., Habeb, A.-H. H., Othman, M. F. I., & Al-Mhiqani, M. N. (2020). An improved energy-efficient clustering protocol to prolong the lifetime of the WSN-based IoT. IEEE Access, 8, 200500-200517.
• Başarslan, M. S., & Kayaalp, F. (2023). Sentiment analysis with ensemble and machine learning methods in multi-domain datasets. Turkish Journal of Engineering, 7(2), 141-148. https://doi.org/10.31127/tuje.1079698
• Ikotun, A. M., Ezugwu, A. E., Abualigah, L., Abuhaija, B., & Heming, J. (2023). K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data. Information Sciences, 622, 178-210.
• Sinaga, K. P., & Yang, M. S. (2020). Unsupervised K-means clustering algorithm. IEEE Access, 8, 80716-80727.
• Nakas, C., Kandris, D., & Visvardis, G. (2020). Energy efficient routing in wireless sensor networks: A comprehensive survey. Algorithms, 13(3), 72.
• Choi, K. W., Rosyady, P. A., Ginting, L., Aziz, A. A., Setiawan, D., & Kim, D. I. (2017). Theory and experiment for wireless-powered sensor networks: How to keep sensors alive. IEEE Transactions on Wireless Communications, 17(1), 430-444.
• Rezazadeh, J., Moradi, M., & Ismail, A. S. (2012). Mobile wireless sensor networks overview. International Journal of Computer Communications and Networks, 2(1), 17-22.
• Kaur, K., & Kang, S. (2023). A comprehensive study based on routing protocols and data transmission in M-WSN environment. In 2023 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) (pp. 971-977). IEEE.
• Shabbir, N., & Hassan, S. R. (2017). Routing protocols for wireless sensor networks (WSNs). Wireless Sensor Networks-Insights and Innovations, 36-40.
• Rady, A., El-Rabaie, E. L. S. M., Shokair, M., & Abdel-Salam, N. (2021). Comprehensive survey of routing protocols for mobile wireless sensor networks. International Journal of Communication Systems, 34(15).
• Nguyen, L. T., Defago, X., Beuran, R., & Shinoda, Y. (2008). An energy efficient routing scheme for mobile wireless sensor networks. IEEE International Symposium on Wireless Communication Systems, 568-572.
• Anitha, R. U., & Kamalakkannan, P. (2013). Enhanced cluster based routing protocol for mobile nodes in wireless sensor network. International Conference on Pattern Recognition, Informatics and Mobile Engineering, 187-193.
• Chen, Z., Zhou, W., Wu, S., & Cheng, L. (2021). An on-demand load balancing multi-path routing protocol for differentiated services in MWSN. Computer Communications, 296-306.
• Sara, G. S., Devi, S. P., & Sridharan, D. (2012). A genetic-algorithm-based optimized clustering for energy-efficient routing in MWSN. ETRI Journal, 34(6), 922-931.
• Jambli, M. N., Zen, K., Lenando, H., & Tully, A. (2011). Performance evaluation of AODV routing protocol for mobile wireless sensor network. In 7th International Conference on Information Technology (pp. 1-6).
• Swapna, D., & Nagaratna, M. (2023). A review on routing protocols for mobile wireless sensor networks (MWSN): Comparative study. International Journal of System Assurance Engineering and Management.
• Yu, S., Zhang, B., Li, C., & Mouftah, H. T. (2014). Routing protocols for wireless sensor networks with mobile sinks: A survey. IEEE Communications Magazine, 52(7), 150-157.
• 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.
• Zhang, D. G., Niu, H. L., Liu, S., & Ming, X. C. (2017). Novel positioning service computing method for WSN. Wireless Personal Communications, 92, 1747-1769.
• Dokmanic, I., Parhizkar, R., Ranieri, J., & Vetterli, M. (2015). Euclidean distance matrices: Essential theory, algorithms, and applications. IEEE Signal Processing Magazine, 32(6), 12-30.
• Mora-Garcia, R. T., Marti-Ciriquian, P., Perez-Sanchez, R., & Cespedes-Lopez, M. F. (2018). A comparative analysis of Manhattan, Euclidean, and network distances. Why are network distances more useful to urban professionals? International Multidisciplinary Scientific Geo Conference: SGEM, 18(2.2), 3-10.
• Rathod, A. B., Gulhane, S. M., & Padalwar, S. R. (2016). A comparative study on distance measuring approaches for permutation representations. 2016 IEEE International Conference on Advances in Electronics, Communication and Computer Technology (ICAECCT), 251-255.
• Eremeev, A. V., Kel’manov, A. V., Kovalyov, M. Y., & Pyatkin, A. V. (2019). Maximum diversity problem with squared Euclidean distance. In Mathematical Optimization Theory and Operations Research: 18th International Conference, MOTOR 2019, Ekaterinburg, Russia, July 8-12, 2019, Proceedings 18. Springer International Publishing, 541-551.
• Shokouhifar, M., & Jalali, A. (2015). A new evolutionary based application specific routing protocol for clustered wireless sensor networks. AEU-International Journal of Electronics and Communications, 69(1), 432-441.
• Kousar, A., Mittal, N., & Singh, P. (2020). An improved hierarchical clustering method for mobile wireless sensor networks using type-2 fuzzy logic. In Proceedings of ICETIT 2019: Emerging Trends in Information Technology, Springer International Publishing, 128-140.
• Mondal, S., Ghosh, S., Khatua, S., Biswas, U., & Das, R. K. (2022). Energy efficient algorithms for enhancing lifetime in wireless sensor networks. Microsystem Technologies, 28(12), 2593-2610.