Abstract

BackgroundSecure routing is of utmost importance in protecting mobile devices from external threats. However, the dynamic characteristics of Mobile Ad hoc Networks (MANETs) pose significant challenges in achieving a secure routing path. ObjectivesThis paper aims to propose a Trust and Anonymous Model (TAM) for efficient and secure data routing in MANETs. The objective is to address the limitations of existing routing approaches and enhance the security of the network. MethodsThe TAM protocol is designed with a Two-Tier Security Mechanism (TTSM). In the first level of security, trustable nodes are selected based on their ability to process control messages. The recommended trust value of a node is determined by estimating the speed at which it processes control messages, with higher energy nodes being considered more trustable. In the second level of security, the original node identity is concealed, and data is transmitted through selected trusted nodes with duplicate identities generated using a factorial recursive function. This ensures secure transmission, as malicious observers are unable to identify participating nodes in the routing operation. ResultsThe proposed TAM model is evaluated and compared with state-of-the-art schemes such as A Multi-attribute-based Trusted Routing for Embedded devices (EMBTR) and a cognitive energy-efficient-based trusted model (CEMT). Experimental evaluation demonstrates that the TAM model achieves better performance in terms of energy consumption, delay, packet delivery rate, and false node detection rate, thereby improving network optimality. ConclusionThe Trust and Anonymous Model (TAM) with its Two-Tier Security Mechanism (TTSM) presents a promising approach for secure data routing in MANETs. By selecting trustable nodes and utilizing duplicate identities, the TAM model enhances network security while achieving improved performance compared to existing schemes. The findings of this study contribute to the advancement of secure routing techniques in MANETs and provide insights for future research in this domain.

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