ML-Fresh: Novel Routing Protocol in Opportunistic Networks Using Machine Learning

Puneet Garg; Ashutosh Dixit; Preeti Sethi

doi:10.32604/csse.2022.019557

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ARTICLE

ML-Fresh: Novel Routing Protocol in Opportunistic Networks Using Machine Learning

Puneet Garg^*, Ashutosh Dixit, Preeti Sethi
Department of Computer Engineering, J. C. Bose University of Science and Technology, YMCA, Faridabad, 121006, India
* Corresponding Author: Puneet Garg. Email:
(This article belongs to this Special Issue: Emerging Trends in Intelligent Communication and Wireless Technologies)

Computer Systems Science and Engineering 2022, 40(2), 703-717. https://doi.org/10.32604/csse.2022.019557

Received 17 April 2021; Accepted 23 May 2021; Issue published 09 September 2021

Abstract

Opportunistic Networks (OppNets) is gaining popularity day-by-day due to their various applications in the real-life world. The two major reasons for its popularity are its suitability to be established without any requirement of additional infrastructure and the ability to tolerate long delays during data communication. Opportunistic Network is also considered as a descendant of Mobile Ad hoc Networks (Manets) and Wireless Sensor Networks (WSNs), therefore, it inherits most of the traits from both mentioned networking techniques. Apart from its popularity, Opportunistic Networks are also starting to face challenges nowadays to comply with the emerging issues of the large size of data to be communicated and blind forwarding of data among participating nodes in the network. These issues lower the overall performance of the network. Keeping this thing in mind, ML-Fresh-a novel framework has been proposed in this paper which focuses to overcome the issue of blind forwarding of data by maintaining an optimum path between any pair of participating nodes available in the OppNet using machine learning techniques viz. pattern prediction, decision tree prediction, adamic-adar method for complex networks. Apart from this, ML-Fresh also uses the history of successful encounters between a pair of communicating nodes for route prediction in the background. Simulation results prove that the ML-Fresh outperforms the existing framework of Opportunistic Networks on the grounds of standard Quality-of-Service (QoS) parameters.

Keywords

Opportunistic networks; artificial intelligence; machine learning; link prediction; routing protocols; QoS parameters

Cite This Article

P. Garg, A. Dixit and P. Sethi, "Ml-fresh: novel routing protocol in opportunistic networks using machine learning," Computer Systems Science and Engineering, vol. 40, no.2, pp. 703–717, 2022.

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