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 that it uses a lot of energy when transmitting data, which makes it 
difficult to scale. 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 done that enhances network accuracy by 
pinpointing sensor node locations. Integration of CFO into clustering with localization further 
improves cluster head selection for optimal data routing. Classification through the RBM with 
SSA function enhances anomaly detection with 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.