Optimization of Fuzzy Logic-Based Genetic Algorithm Techniques in Wireless Sensor Networks Protocols
Keywords:
Wireless Sensor Network, critical node, relay nodes, Genetic Algorithm, Fuzzy LogicAbstract
The operational timeline of a Wireless Sensor Network (WSN) spans from the initiation of sensing activities until a predetermined proportion of nodes deplete their power reserves, with the "critical node" being the specific device undergoing power depletion. Optimizing WSN longevity, particularly of these critical nodes, is pivotal for overall network sustainability. The network involves relay nodes communicating with a central hub through intermediary nodes, extending longevity, enhancing accessibility, and efficiently managing traffic distribution in alignment with sensor network design principles.
To extend network operational lifespan, we propose a strategy utilizing a Genetic Algorithm embedded with Fuzzy Logic (FLbGA) to orchestrate relay nodes' data collection schedules. Relay nodes, acting as hand-off hubs, aggregate information within their groups or neighboring transfer hubs. This data is transmitted to the base station directly or through an interconnected sequence of intermediate relay nodes. The strategic use of FLbGA optimizes data collection, boosting network durability and performance.
In each designated cluster, relay nodes receive data from corresponding sensor nodes, where transmitted information may exhibit constancy or variability. Assuming post-deployment spatial node configurations, parameters like population size, cross-over frequency, mutation frequency, etc., are integrated into FLbGA's design for an optimal solution. This parameter augmentation enhances the system's efficiency and adaptability.
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