International Journal of Wireless and Microwave Technologies (IJWMT)
IJWMT Vol. 15, No. 2, 8 Apr. 2025
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Enhancing Sensor Node Energy Efficiency in Wireless Sensor Networks through an Adaptable Power Allocation Framework
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Author(s)
Index Terms
Power Conservation, WSN, IoT, Sensory Network
Abstract
In the realm of Wireless Sensor Networks (WSN), approaches to managing power are generally divided into two main strategies: reducing power consumption and optimizing power distribution. Power reduction strategies focus on creating a path for data packets between the sink and destination nodes that minimizes the distance and, consequently, the number of hops required. In contrast, power optimization strategies seek to enhance data transfer efficiency without splitting the network into disconnected segments. Adjusting the data path to balance power often leads to longer routes, which can shorten the network's lifespan. Conversely, opting for the shortest possible path tends to result in a densely packed network structure. The newly proposed Adaptable Power Allocation Framework (APAF) aims to improve energy-efficient routing by simultaneously addressing both power balance optimization and the management of the data packet path. Unlike conventional routing methods, which primarily focus on the shortest path, APAF designs the data pathway by taking into account both the least amount of data transmission and the equilibrium of power distribution and balancing. Through a focus on power balance optimization and intelligent data path management, it demonstrates its effectiveness in improving energy-efficient routing. This study introduces the Adaptable Power Allocation Framework (APAF), which improves energy-efficient routing in WSNs by balancing power consumption and optimizing the data path. APAF is compared with traditional methods (LEACH, Swarm Optimization), showing a 20-30% improvement in data loss reduction and extending network lifespan.
Cite This Paper
M. S. Muthukkumar, C. M. Arun Kumar, "Enhancing Sensor Node Energy Efficiency in Wireless Sensor Networks through an Adaptable Power Allocation Framework", International Journal of Wireless and Microwave Technologies(IJWMT), Vol.15, No.2, pp. 1-9, 2025. DOI:10.5815/ijwmt.2025.02.01
Reference
[1]Suresh Kumar, M., & Sathish Kumar, G. A. (2023). Efficient Hybrid Energy Optimization Method in Location Aware Unmanned WSN. Intelligent Automation & Soft Computing, 35(1).
[2]Wu, X., Yang, Y., Han, S., Zhao, Z., Fang, P., & Gao, Q. (2021). Multi-objective optimization method for nuclear reactor radiation shielding design based on PSO algorithm. Annals of Nuclear Energy, 160, 108404.
[3]Dhanalakshmi, N., Kumar, M. S., Indrani, B., & Srinivasan, R. (2023, February). Bayesian Trust Prediction and EASI (Energy Accumulation with Swarm Intelligence) Routing Protocol for Securing WSN. In 2023 7th International Conference on Computing Methodologies and Communication (ICCMC) (pp. 1509-1516). IEEE.
[4]da Silva, I. R., de AL Rabêlo, R., Rodrigues, J. J., Solic, P., & Carvalho, A. (2020). A preference-based demand response mechanism for energy management in a microgrid. Journal of Cleaner Production, 255, 120034.
[5]Prakash, P. S. (2021). Machine Learning-Based Optimized Hierarchical Routing Protocols for WSN Lifetime: A Review. Information Technology In Industry, 9(2), 289-307.
[6]Niu, W. J., Feng, Z. K., Feng, B. F., Xu, Y. S., & Min, Y. W. (2021). Parallel computing and swarm intelligence based artificial intelligence model for multi-step-ahead hydrological time series prediction. Sustainable Cities and Society, 66, 102686.
[7]Chavan, S. D., Thorat, A. S., Gunjal, M. S., Vibhute, A. S., & Desai, K. R. (2023). Biologically Inspired Energy Efficient Routing Protocol in Disaster Situation. International Journal of Electronics and Telecommunications, 163-168.
[8]Shadmani, A., Nikoo, M. R., Al-Raoush, R. I., Alamdari, N., & Gandomi, A. H. (2022). The Optimal Configuration of Wave Energy Conversions Respective to the Nearshore Wave Energy Potential. Energies, 15(20), 7734.
[9]Bouakkaz, A., Mena, A. J. G., Haddad, S., & Ferrari, M. L. (2021). Efficient energy scheduling considering cost reduction and energy saving in hybrid energy system with energy storage. Journal of Energy Storage, 33, 101887.
[10]Raghavendra, Y. M., & Mahadevaswamy, U. B. (2023). Hybrid rendezvous clustering model for efficient data collection in multi sink based wireless sensor networks. Wireless Personal Communications, 129(2), 837-851.
[11]Golbaz, D., Asadi, R., Amini, E., Mehdipour, H., Nasiri, M., Nezhad, M. M., ... & Neshat, M. (2021). Ocean wave energy converters optimization: A comprehensive review on research directions. arXiv preprint arXiv:2105.07180.
[12]Ammarfaizal, N., Putrada, A. G., & Abdurohman, M. (2021, October). A cluster head selection method comparison of dchsm, deec, and leach on wireless sensor network using voronoi diagram. In 2021 International Conference Advancement in Data Science, E-learning and Information Systems (ICADEIS) (pp. 1-6). IEEE.
[13]Ammarfaizal, N., Putrada, A. G., & Abdurohman, M. (2021, October). A cluster head selection method comparison of dchsm, deec, and leach on wireless sensor network using voronoi diagram. In 2021 International Conference Advancement in Data Science, E-learning and Information Systems (ICADEIS) (pp. 1-6). IEEE.
[14]Ayub, S., Ayob, S. M., Tan, C. W., Taimoor, M., Ayub, L., Bukar, A. L., & Daud, M. Z. (2022). Analysis of energy management schemes for renewable-energy-based smart homes against the backdrop of COVID-19. Sustainable Energy Technologies and Assessments, 52, 102136.
[15]Ayub, S., Ayob, S. M., Tan, C. W., Taimoor, M., Ayub, L., Bukar, A. L., & Daud, M. Z. (2022). Analysis of energy management schemes for renewable-energy-based smart homes against the backdrop of COVID-19. Sustainable Energy Technologies and Assessments, 52, 102136.
[16]Swaminathan, K., Vijay Ravindran, Ram Prakash Ponraj, S. Venkatasubramanian, K. S. Chandrasekaran, and Satheesh Ragunathan. "A Novel Composite Intrusion Detection System (CIDS) for Wireless Sensor Network." In 2023 International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT), pp. 112-117. IEEE, 2023.
[17]Ghiasi, R., Ghasemi, M. R., & Chan, T. H. (2021). Optimum feature selection for SHM of benchmark structures using efficient AI mechanism. Smart Struct. Syst, 27, 623-640.
[18]Thekiya, M. S., & Nikose, M. D. (2022). Energy efficient clustering routing protocol using novel admission allotment scheme (AAS) based intra-cluster communication for Wireless Sensor Network. International Journal of Information Technology, 14(6), 2815-2824.
[19]Niu, W. J., Feng, Z. K., Jiang, Z. Q., Wang, S., Liu, S., Guo, W., & Song, Z. G. (2021). Enhanced harmony search algorithm for sustainable ecological operation of cascade hydropower reservoirs in river ecosystem. Environmental Research Letters, 16(5), 055013.
[20]Venkatasubramanian, S., R. Vijay, and S. HariPrasath. "Artificial Butterfly Optimization based Cluster Head Selection with Energy Efficient Data Aggregation model for Heterogeneous WSN Environment." In 2023 7th International Conference on Computing Methodologies and Communication (ICCMC), pp. 940-948. IEEE, 2023.