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In the domain of the Internet of Things (IoT), data reliability is important, particularly within critical sectors like healthcare, environmental surveillance, and smart grids. Nevertheless, data transfer from the physical domain to the digital layer is susceptible to trust-related challenges encompassing data integrity, genuineness, and credibility. Predominantly, prevailing models for trust assessment primarily concentrate on the conduct of nodes, thus disregarding the direct evaluation of data packets. This particular constraint results in an insufficient validation of data credibility, thereby failing to consider pivotal elements like timeliness and accuracy. Furthermore, utilizing cloud-based packet evaluation frameworks frequently leads to inaccuracies, unreliability, and energy inefficiencies owing to the transfer of untrusted data. The current study introduces a streamlined trust assessment framework called the lightweight trust evaluation model (LTEM), custom-built for IoT settings to combat these obstacles. LTEM meticulously examines node behavior and data packets via a multi-tiered approach encompassing nodes, cluster heads (CH), and base stations (BS). Moreover, the proposed model's architecture considers energy usage by averting the transmission of untrusted data. Simulation results showcase the supremacy of LTEM compared to existing models by achieving a detection rate of 99% for untrusted data packets, outperforming the detection rates ranging from 30% to 75% observed in other models. Moreover, LTEM enhances the operational efficiency of sensor nodes regarding energy consumption, achieving an average energy utilization of 1.33J out of 4J, resulting in savings of approximately 2.67J on average, thereby extending the lifespan of nodes.
Alhandi et al. (Sun,) studied this question.