DCOR: Enhancing Network Lifetime and Performance in IoT-Based Wireless Sensor Networks through Distributed Clustering and Optimized Routing
Keywords:
Wireless Sensor Networks, Internet of Things, Distributed Clustering, Routing Optimization, Teacher Learner Firefly OptimizerAbstract
The burgeoning field of Internet of Things (IoT) necessitates efficient management of network resources, particularly in Wireless Sensor Networks (WSNs), to extend network lifetime and enhance communication performance. Existing clustering and routing mechanisms in WSNs often grapple with limitations like suboptimal path selection, high energy consumption, and inconsistent communication speeds, which significantly impede network reliability and longevity. This study introduces a novel approach to surmount these challenges, focusing on enhancing network lifetime and performance in IoT-based WSNs through a Distributed Clustering Mechanism (DCM) and an efficient routing algorithm, the Teacher Learner Firefly Optimizer (TLFFO). Our proposed model incorporates spatial node metrics (node location, residual energy, and energy model) to form optimized clusters. These clusters, leveraging temporal node metrics such as previous communication performance, enable the establishment of multipath routes. The TLFFO, a custom-developed algorithm, innovatively integrates the principles of teacher-learner-based optimization with the bio-inspired firefly algorithm, ensuring optimal route selection with a focus on energy efficiency and communication speed. Empirical evaluations reveal that our model outperforms existing clustering and routing methods, demonstrating a 6.5% increase in communication speed, an 8.5% enhancement in energy efficiency, a 3.2% rise in throughput, along with a significant reduction in jitter (4.9%) and an improvement in packet delivery performance (4.9%). These advancements underscore the potential of our approach in extending the network lifetime while maintaining high-quality communication standards in IoT-based WSNs. The implications of this work are profound, promising a transformative impact on the efficiency and sustainability of WSNs in IoT environments. By addressing the critical challenges of energy consumption and communication efficacy, our approach sets a new benchmark for future research and practical applications in the domain of wireless sensor networking operations.
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