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In mobile adhoc netwotk (MANET), a node’s quality of service (QoS) trust represents how much it is reliable in quality. QoS trust of a node is computed based on its multiple quality parameters and it is an interesting and challenging area in MANETs. In this work, QoS trust is evaluated by taking into consideration quality parameters like node residual energy, bandwidth and mobility. The proposed method “Recommendations Based QoS Trust Aggregation and Routing in Mobile Adhoc Networks-QTAR” is a frame work. Where the trust is established through four phases like QoS trust computation, aggregation, propagation and routing. The Dempster Shafer Theory (DST) is used for aggregation of trust recommendations. In the network, trust information is propagated through HELLO packets. Each node stores the QoS trust information of other nodes in the form of trust matrices. We applied matrix algebra operations on trust matrices for route establishment from source to destination. The time and space complexity of proposed method is discussed theoretically. The simulation is conducted for the varying of node velocity and network size, where the proposed method shown considerable improvement over existing protocols.

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Summary Social network (OSN) is an emerging platform through which people can connect with their friends, relatives, and other like‐minded people. On the other hand, users' personal information might be misused because of other users' biased and malicious behavior. Establishing a trusted environment in social networks is one of the current research problems. Some of the research papers proposed to trust computational methods, but still, there is a lack of methods to handle biased recommendations and loss of trust accuracy towards the target user. In this article, to address these open issues, “a novel trust recommendation model in online social networks using soft computing methods (TRMSC)” is proposed for the Twitter social networks. Here direct and indirect trust is computed for known and unknown users, respectively. The direct trust of a user is computed using clustering methods based on his social activities (posts, retweets received, mentions received, listed count, and follower count) with other users. In the computation of indirect trust, the impact of biased recommendations is suppressed using the Dempster Shafer theory(DST) method, and loss of trust is minimized using trust transitive matrices. The performance of the proposed method is analyzed theoretically and experimentally. Time and space complexities are measured using asymptotic notations. In experimental results, TRMSC is evaluated for different network sizes and for target users at different distances (2 to 4‐hops), where it could perform better than existing methods.