Optimized Meta-Heuristic and Lightweight Encryption-Based Secure Routing for Sustainable Wireless Sensor Networks

In recent years, secure and energy-efficient communication has become crucial for sustaining reliability of Internet of Things (IoT)-based Wireless Sensor Networks (WSNs), particularly in large-scale resource-constrained environments. However, conventional routing protocols often require high computation and Deep Learning (DL) models, which limits their deployment on low-power sensor nodes. To overcome these challenges, this research proposes an improved routing Lightweight Secure Energy-Aware Meta-Heuristic Routing (L-SEAMHR) framework. Initially, data were collected from simulated WSN surroundings using MATLAB, to reflect real-world IoT states. Moreover, the proposed protocol optimizes routing using a lightweight variant of (MEHO) algorithm, which reduces computational overhead via population size control and beamwidth pruning while maintaining smart selection of the next hop. In addition, LSEAMHR combines a lightweight Rivest Cipher (RC5) encryption mechanism that swaps autoencoder-based DL, which allows secure data transmission with minimal memory and energy usage. Dynamic key generation utilizes node IDs and counter values to confirm data confidentiality without depending on static pre-shared keys or a Global Positioning System (GPS). The proposed L-SEAMHR protocol obtained better results in throughput (96.8%) while reducing packet drop (5.91%) and energy consumption (0.0127 mJ) when compared to existing Secure and Energy-efficient Hierarchical Routing (SEHR) Routing protocol.