Sine Cosine–Reptile Search–Based CH Selection and Optimized Routing in WSN-Assisted IoT to Mitigate Hotspot Problem

Abstract

Because of technological advances in the Internet of Things (IoT), IoT surveillance applications powered by wireless sensor networks (WSN) play an important role in spreading dangerous and nonhazardous data. When using such an application, the sensor devices must connect using multihop (Multi-H) communication, which overloads the relay nodes (RNs). This tends to cause hotspot problems and becomes a major problem for network lifetime as the RNs use their power more extensively. To address this issue, the proposed study introduces a novel hybrid-optimized clustering–based routing protocol (HOC-RP) to address the hotspot issues in WSN-enabled IoT. The network is distributed into numerous clusters using the fuzzy K-medoid clustering model (Fuzz-KMC). A new hybrid optimization recognized as the sine cosine–reptile search optimization (SC-RSO) algorithm is employed for optimal CH selection to resolve the hotspot issues in the WSN. In addition, an enhanced gannet optimization algorithm (EGOA) is used to define the optimal route for effective data transmission. The simulation of the HOC-RP model is carried out using the Matlab platform, and the performance is evaluated using different evaluation metrics. As a result, the proposed HOC-RP model has obtained a better network lifetime of 44,768 rounds for scenario 1 and 44,721 rounds for scenario 2 than the existing protocols.