Enhancing IoT-Enabled Sustainable Smart Cities With Secure and Energy-Aware Data Collection Using Meta-Heuristic Technique

Internet of Things (IoT)-enabled wireless sensor networks (WSNs) are integral to enhancing urban living, sustainability, and operational efficiency in smart cities. However, their wireless and distributed nature leaves them vulnerable to security threats. Existing security algorithms face challenges in IoT-based WSNs due to resource constraints, particularly regarding energy-efficient and trustworthy cluster head (CH) selection. Many current techniques assume complete trust among all nodes, including CHs, which does not align with real-world scenarios. Malicious nodes posing as CHs can disrupt routing decisions and communication, particularly critical in smart city contexts. To address this, our research incorporates trust-based security into CH selection processes for IoT-based WSNs in smart cities. By prioritizing trustworthy CHs and considering potential malicious nodes, we aim to enhance network reliability. Our method assesses each sensor node’s (SN) trust and residual energy to optimize CH selection, mitigating the impact of malicious activities on WSN performance. By utilizing the Mountain Gazelle Optimizer (MGO), we combine trust concepts with meta-heuristic methods to enhance security, energy efficiency, and network lifetime. In terms of network lifetime, the proposed work exceeds the existing methods: 1.24 times better than LEACH-TM, 0.40 times better than DGTTSSA, 0.33 times better than ETCHS, 1.12 times better than eeTMFOGA, 6.70 times better than E-LEACH, 0.55 times better than Improved-LEACH, 0.61 times better than MG-LEACH, and 0.79 times better than RCH-LEACH in the scenario where the base station (BS) is positioned at the center.