International Journal of Applied Sciences & Development
E-ISSN: 2945-0454
Volume 4, 2025
Near-Optimal Lifetime Maximization in Energy-Efficient Wireless Sensor Random Networks via Scalability and Density Synthesis Based Energy Load Balancing
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Abstract: Both dense and sparse Wireless Sensor Networks (WSNs) are broadly applied due to the rapid development of control, computation and communication technologies. Impact of either scalability or density on the lifetime of WSNs has however seldom been analyzed and synthesized, which depends on sensor node deployment optimization. Scalability is one of the leading factors in enhancing connectivity, mobility, reliability and coverage range of WSNs, which in turn directly affects the WSNs lifetime defined in terms of either the first energy-exhausted sensor node or last energy-exhausted sensor node of WSNs. In order to minimize power consumption and maximize the WSN lifetime, the energy-efficient WSNs protocol is proposed to examine the impact of both scalability and density on WSNs lifetime. In practical implementation of WSNs, transceiver circuit energy and path transmission energy are both major parts of energy dissipation, thus it has never been proved whether the single-hop routing or multi-hop routing would always be superior to another. Mostly multi-hop routing is more applicable to WSNs with relatively small radio transceiver power within large diameter coverage range. Instead single-hop routing is more applicable to WSNs with relatively large radio transceiver power within small diameter coverage range. For the matter of simplicity, the single-hop routing is selected for WSNs characteristic analysis and synthesis based on multiple scalability based and density based topological deployment. Via comprehensive numerical case analysis, the practical near-optimal solution to maximize the WSNs lifetime has been reached. This approach can be easily expanded to multi-hop routing cases in WSNs.
Keywords:
Wireless Sensor Networks, Scalability, Density, Lifetime, Adaptive Clustering, Energy Load Balancing, Hierarchical Clustering, Dense Deployment, Sparse Deployment
Pages: 221-235
DOI: 10.37394/232029.2025.4.24