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1 LIGM - Laboratoire d-Informatique Gaspard-Monge

Abstract : In collaborative wireless networks with a low infrastructure, the presence of misbehaving nodes can have a negative impact on network performance. In particular, we are interested in dealing with this nasty presence in road safety applications, based on vehicular ad hoc networks VANETs. In this work, we consider as harmful the presence of malicious nodes, which spread false and forged data; and selfish nodes, which cooperate only for their own benefit. To deal with this, we propose a Distributed Trust Model DTM2, adapted from the job market signaling model. DTM2 is based on allocating credits to nodes and securely managing these credits. To motivate selfish nodes to cooperate more, our solution establishes the cost of reception to access data, forcing them to earn credits. Moreover, to detect and exclude malicious nodes, DTM2 requires the cost of sending, using signaling values inspired form economics and based on the node-s behavior, so that the more a node is malicious, the higher its sending cost, thus limiting their participation in the network. Similarly, rewards are given to nodes whose sent messages are considered as truthful, and that paid a sending cost considered as correct. The latter is a guarantee for the receivers about the truthfulness of the message since, in case of message refusal, the source node is not rewarded despite its payment. We validated DTM2 via a theoretical study using Markov chains; and with a set of simulations, in both urban and highway scenarios. Both theoretical and simulation results show that DTM2 excludes from the network 100% of malicious nodes, without causing any false positive detection. Moreover, our solution guarantees a good ratio of reception even in the presence of selfish nodes.

Keywords : job market signaling model Vehicular ad hoc networks incentive model malicious nodes selfish nodes job market signaling model.

Autor: Nadia Haddadou - Abderrezak Rachedi - Yacine Ghamri-Doudane -

Fuente: https://hal.archives-ouvertes.fr/


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