HS-EDEEC: An Energy-Efficient Transmission Model for Route Probability of Heterogeneous WSN-Based on IoT Networks

Abstract

In internet of things (IoT)-based wireless sensor networks (WSNs), sensor nodes are distributed across a designated area to collect and transmit data. However, communication failures in WSNs, caused by the failure of one or more cluster heads, can disrupt data transmission and processing. The study proposes the hybrid optimized harmony search enhanced distributed energy-efficient clustering algorithm that optimizes route selection, detects faults, and calculates probability values to identify reliable communication paths, thereby enhancing fault tolerance (FT) and energy consumption (EC) efficiency. The model’s performance has been evaluated using a Matrix Laboratory (MATLAB) simulator in the context of an IoT-based WSN network, demonstrating its effectiveness in improving computational delays and EC by 12% (P < .02) and minimizing average computation time (CT) with an improved 16% FT rate compared to existing models such as Efficient Fault-Tolerant routing using Path graph flow modeling with Marchenko-Pastur Distribution (EFT-PMD), energy-efficient secure-based IoT, distributed cognitive maps, and energy efficient data communication.In internet of things (IoT)-based wireless sensor networks (WSNs), sensor nodes are distributed across a designated area to collect and transmit data. However, communication failures in WSNs, caused by the failure of one or more cluster heads, can disrupt data transmission and processing. The study proposes the hybrid optimized harmony search enhanced distributed energy-efficient clustering algorithm that optimizes route selection, detects faults, and calculates probability values to identify reliable communication paths, thereby enhancing fault tolerance (FT) and energy consumption (EC) efficiency. The model’s performance has been evaluated using a Matrix Laboratory (MATLAB) simulator in the context of an IoT-based WSN network, demonstrating its effectiveness in improving computational delays and EC by 12% (P < .02) and minimizing average computation time (CT) with an improved 16% FT rate compared to existing models such as Efficient Fault-Tolerant routing using Path graph flow modeling with Marchenko-Pastur Distribution (EFT-PMD), energy-efficient secure-based IoT, distributed cognitive maps, and energy efficient data communication.