Abstract:
Opportunistic networks offer an innovative solution that enables fast transmission of messages based on the amount of interaction between the nodes. This work is focused ...Show MoreMetadata
Abstract:
Opportunistic networks offer an innovative solution that enables fast transmission of messages based on the amount of interaction between the nodes. This work is focused on classifying the nodes based on their willingness to forward messages to any destination. Memory constraints and social interaction features are considered to be attributes in finding their interest in forwarding messages. `Multiple Classifiers Approach' is adopted to ascertain the nodes that are available for participation in the message transmission of Vehicular Ad Hoc Networks (VANETs). The classifiers used in this approach are the `Naive Bayes Classifier' and the `Multi-Layer Perceptron Classifier'. Reachability among nodes is assessed using the number of participating nodes in Opportunistic Networks. The Naïve Bayes Classifier produced an accuracy of around 86% when determining the nodes available for participation in the VANET system while the Multi-Layer Perceptron Classifier produced an accuracy of around 87% for the same. This paper gives a detailed account of the classification of nodes and the simulation of the message transmission in a VANET system. The simulation was carried out using the NS2 simulator and SUMO.
Published in: 2019 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS)
Date of Conference: 16-19 December 2019
Date Added to IEEE Xplore: 16 June 2020
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