Combined localization and clustering approach for reduced energy presumption in heterogeneous IoT

Abstract

The field of H-IoT is emerging with enormous potential to empower various technologies. Smart cities and advanced manufacturing are a few of the fields where H-IoT is currently used. The issue with H-IoT is its heavy energy consumption while transmitting data, which makes scaling difficult. To overcome such issues, a hybrid approach of Crayfish Optimization (CFO) with FCM and Restricted Boltzmann Machine (RBM) with Soft Sign Activation (SSA) has been proposed. Initially, Node initialization lays the foundation by configuring individual sensor nodes for network participation. After initialization, Fuzzy C Means clustering optimizes data aggregation by categorizing nodes into clusters based on similarity. Gathering Neighbor Node Traffic Data (NNTD) provides insights into communication patterns. Based on the threshold of NNTD, node localization is performed that enhances network accuracy by pinpointing sensor node locations. Integration of CFO into clustering, along with localization further improves cluster head selection for optimal data routing. Classification through the RBM with SSA function enhances anomaly detection, combining data analysis for optimizing energy utilization in heterogeneous IoT environments. The ‘combined CFO-FCM and SSA-RBM’ has been implemented in MATLAB and achieved an accuracy of 94.50%. As a result, the overall performance of the system is improved.