Fuzzy Data Aggregation Approach to Enhance Energy-Efficient Routing Protocol for HWSNs
The sensor nodes' computing capability, communication capabilities, and power supply are severely constrained in WSNs, making sensor battery replacement or recharging difficult or even impossible. Therefore, energy is an important challenge to consider while creating WSNs. In hazardous circumstances, accurate data aggregation and routing are crucial, and the energy consumption of sensors must be closely controlled. Due to environmental conditions and short-distance sensors, however, there is a high possibility of duplicating data. Large datasets include a range of data, some of which are helpful while others are entirely unnecessary. This redundancy reduces performance in terms of redundant transmission and computation expense. Data aggregation, on the other hand, may reduce duplicate data in a network, hence reducing the volume of data sent and increasing the network's lifespan. In this context, two novel energy-conscious approaches called Fuzzy Data Aggregation with Spider monkey optimization (FDA-SMORP) for data aggregation in the cluster head and routing to the sink are presented. These strategies attempt to offset the energy consumption among all nodes in a wireless network such that these nodes exhaust all of their energy and die almost simultaneously. To demonstrate the efficacy of the suggested approaches in terms of minimizing delay caused by route planning, balancing energy usage, and extending network lifespan, the proposed methods are compared to some of the most well-known WSN systems.
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