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Multi objective trust optimization for efficient communication in wireless M learning applications

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Abstract

Mobile learning (M learning) supports ubiquitous learning thus is a strong contender in distance learning. One of the challenge faced in development of m learning is an efficient and secure communication network. Mobile ad hoc network (MANET) refers to a set of wireless mobile nodes possessing no centralized architecture as well as a dynamic topology. One of the fast evolving application in MANET is mobile-learning (m learning). Security in MANET’s is a challenge due to its open medium. The routing assumes that all the nodes in the network are trustworthy which leaves the network vulnerable to attacks. Trust management schemes are popularly used for secure routing, verification, interruption location, and access control. In this work, clustering and trust is used for mitigating maliciousness and formation of clusters is optimized using heuristic method. Swarm intelligence (SI) is an efficient option for optimizing routing in a complex network situation, where traditional routing strategies come up short. Particle swarm optimization (PSO) employs the concept of social interactions to get optimal solutions. Simulation results proved that the proposed PSO with weighted trust model achieves better performance for packet delivery ratio, end to end delays as well as cluster formation in malicious environment.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Karpagam University, Coimbatore, 641021, India

    M. Ashwin

  2. Department of Electronics and Communication Engineering, Karpagam University, Coimbatore, 641021, India

    S. Kamalraj

  3. Department of CNE, King Khalid University, Abha, Saudi Arabia

    M. Azath

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  1. M. Ashwin
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  2. S. Kamalraj
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  3. M. Azath
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Correspondence to M. Ashwin.

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Ashwin, M., Kamalraj, S. & Azath, M. Multi objective trust optimization for efficient communication in wireless M learning applications. Cluster Comput 22 (Suppl 5), 10687–10695 (2019). https://doi.org/10.1007/s10586-017-1158-z

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  • DOI: https://doi.org/10.1007/s10586-017-1158-z

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