An Adaptive Multi-Encryption Protocol for IoT Networks using Transit Search Optimization

Abstract

The rapid growth of heterogeneous Internet of Things (IoT) networks has increased the need for security mechanisms that protect sensitive data without imposing high computational and energy costs on resource-constrained devices. This study proposes an adaptive multi-encryption protocol based on Transit Search Optimization (TSO) for selecting suitable security profiles according to packet sensitivity, residual energy, memory availability, network delay, link quality, and node trust. The framework combines lightweight symmetric encryption, elliptic-curve-based sessionkey protection, integrity verification, dynamic key rotation, timestamps, nonces, and contextual IP binding. A multiobjective fitness function is used to balance security strength with encryption time, energy consumption, end-to-end delay, memory use, throughput, and packet-delivery ratio. Simulation results show that the proposed method achieves a 34% lower final objective value than PSO, reduces encryption time by 28.7% compared with fixed AES-ECC, and maintains a packetdelivery ratio of 94.2% at 20% malicious-node density. It also consumes 23.7% less energy than AES-ECC at 100 nodes. These results demonstrate that TSO-based adaptive encryption can provide stronger and more reliable IoT security while preserving the operational efficiency required for large-scale, resource-limited networks.