Load Balancing and Energy Optimization in Wireless Sensor – Research

This study presented an energy-efficient Load Balancing Tree-Based Data Aggregation Scheme (LB-TBDAS) for grid-based Wireless Sensor Networks. The proposed approach utilizes a Minimum Spanning Tree (MST)-based routing mechanism to construct an optimized tree-like communication structure for efficient data transmission. Unlike conventional aggregation techniques, the proposed scheme incorporates multiple load balancing constraints during tree formation, including minimum energy consumption, controlled network depth, and limitation on the maximum number of child nodes connected to each parent node. By integrating these constraints, the proposed LB-TBDAS distributes communication load more uniformly among sensor nodes and minimizes excessive energy depletion in specific network regions. The grid-based architecture further improves routing efficiency by organizing sensor nodes into structured cells and dynamically selecting Cell Heads based on residual energy levels. This adaptive selection mechanism contributes to balanced energy utilization and reduces the hotspot problem commonly observed near the Base Station. Simulation results demonstrate that the proposed LB-TBDAS achieves significantly better performance  in terms of average residual energy, energy balancing, and network lifetime compared to existing schemes such as GB-PEDAP and PEDAP. The analysis indicates that the proposed method extends the overall network lifetime by more than 100% while simultaneously reducing total energy consumption during multi-hop communication. Therefore, the proposed LB-TBDAS provides an effective and reliable solution for energy-efficient data aggregation in large-scale Wireless Sensor Networks. The scheme successfully enhances communication performance, improves network stability, and prolongs operational lifetime, making it suitable for modern monitoring and sensing applications.

[1]     S. R. J. Ramson and D. J. Moni, “Applications of Wireless Sensor Networks—A Survey,” in Proceedings of the International Conference on Innovations in Electrical, Electronics, Instrumentation and Media Technology, Coimbatore, India, Feb. 2017, pp. 325–329.   [2]    S. Sharma and A. Kaur, “Survey on Wireless Sensor Network, Its Applications and Issues,” Journal of Physics: Conference Series, vol. 1969, pp. 1–10, 2021.   [3]     X. Wang and H. Chen, “A Survey of Compressive Data Gathering in WSNs for IoTs,” Wireless Communications and Mobile Computing, vol. 2022, pp. 1–14, 2022.   [4]    A. Hilmani, A. Maizate, and L. Hassouni, “Automated Real-Time Intelligent Traffic Control System for Smart Cities using Wireless Sensor Networks,” Wireless Communications and Mobile Computing, vol. 2020, pp. 1–28, 2020.   [5]    S. K. S. Durai, B. Duraisamy, and J. T. Thirukrishna, “Certain Investigation on Healthcare Monitoring for Enhancing Data Transmission in WSN,” International Journal of Wireless Information Networks, pp. 1–8, 2021.   [6]     M. Majid, S. Habib, A. R. Javed, M. Rizwan, G. Srivastava, T. R. Gadekallu, and C.-W. Lin, “Applications of Wireless Sensor Networks and Internet of Things Frameworks in the Industry Revolution 4.0: A Systematic Literature Review,” Sensors, vol. 22, no. 2087, 2022.   [7]    D. Braginsky and D. Estrin, “Rumor Routing Algorithm for Sensor Network,” in Proceedings of the 1st ACM International Workshop on Wireless Sensor Networks and Applications, Atlanta, GA, USA, Sept. 2002, pp. 22–31.   [8]     S. Lindsey and C. S. Raghavendra, “PEGASIS: Power-Efficient Gathering in Sensor Information System,” in Proceedings of the IEEE Aerospace Conference, Big Sky, MT, USA, Mar. 2002, vol. 3, pp. 1125–1130.   [9]    S. M. Jung, Y. J. Han, and T. M. Chung, “The Concentric Clustering Scheme for Efficient Energy Consumption in the PEGASIS,” in Proceedings of the 9th International Conference on Advanced Communication Technology, Korea, Feb. 2007, vol. 1, pp. 260–265.   [10]    F. Ye, H. Luo, J. Cheng, S. Lu, and L. Zhang, “A Two-Tier Data Dissemination Model for Large-Scale Wireless Sensor Networks,” in Proceedings of the ACM International Conference on Mobile Computing and Networking, London, UK, Sept. 2002, pp. 148–159.