Abstract

The internet of things (IoT) refers to a network of interconnected devices, often utilizing wireless communication, which includes sensor nodes (SNs) organized within wireless sensor networks (WSNs) to collect and transmit data. The IoT-WSN approach groups SNs into clusters, with each cluster having a designated cluster head (CH). However, node security and energy efficiency remain significant challenges due to resource constraints, which adversely affect the overall performance and reliability of the network. A multi-objective trust-aware brown bear optimization algorithm (MO-TABBOA) was proposed for cluster head (CH) and route path selection in IoT-WSNs. Node degree, inter-node distance, residual energy, distance between the CH and the base station (BS), and trust are considered as fitness parameters for secure cluster head (SCH) selection. A routing process is then established in MO-TABBOA based on distance and energy factors. Consequently, MO-TABBOA identifies and avoids malicious nodes through a fitness evaluation mechanism that ensures reliable data delivery. For comparative analysis, the existing energy-aware clustering and multi-hop routing protocol with a mobile sink (EACMRP-MS) is utilized to evaluate the effectiveness of the proposed MO-TABBOA against conventional models. The proposed MO-TABBOA achieves superior energy efficiency, consuming only 29.70 mJ of energy to maintain 100 active nodes. This energy consumption is lower than that of the existing EACMRP-MS, thereby demonstrating MO-TABBOA’s superiority over existing approaches.

Keywords

Internet of things (IoT), Wireless sensor networks (WSNs), Cluster head (CH) selection, Trust-aware optimization, Energy efficiency, Routing protocol.

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