Trust and exclusion in Vehicular Ad Hoc Networks: An economic incentive model based approach

Abstract

-Doudane. Trust and exclusion in vehicular ad hoc networks: an economic incentive model based approach. ComComAP'2013, Apr 2013, Hong Kong, China. pp.13 -18, 2013, <10.1109/ComComAp.2013 Abstract-In this body of work, we are interested in road safety applications such as advanced driver assistance systems, based on Vehicular Ad Hoc Networks (VANETs). One of the particular characteristics of this kind of networks is the continuous sharing of safety information by its nodes. Since this kind of information is time sensitive, a node cannot spend much time to verify its validity with an authority. However, the presence of malicious and selfish nodes in VANETs corrupts exchanged data, and lowers the overall data reception ratio in the network. To tackle this, we propose a new incentive model with exclusion for malicious nodes called VIME. VIME is inspired from the signaling theory from economics. It is based on managing a credit count that each node receives at the initialization of the application. Straightforwardly, VIME is based on two pillars. On the one hand, a node pays an appropriate cost for each sent message, which is seen by the receivers as a guarantee from the source about the truthfulness of the information. On the other hand, nodes get rewarded for cooperating in the network. The proposed economic model allows computing the amounts to be paid and those to be awarded in order to fight selfish and malicious nodes. We validate our approach via simulations. We show that VIME is able to detect and evict gradually all malicious nodes in the network, and decreases the ratio of corrupted and false sent data until reaching zero. Moreover, it has a positive impact on the participation of selfish nodes, as our approach increases the average ratio of sent data as to equal the ideal case's percentage, when no selfish node is present.

Vcash: A Novel Reputation Framework for Identifying Denial of Traffic Service in Internet of Connected Vehicles

“…However, the black hole attack [26] cannot be avoided in such kind of frameworks. [27] proposed an incentive-based method to conduct credit management in the vehicular networks, which rewards positive activity and punish negative activity. Their method also suffers from the vehicleto-vehicle trust management in spite of their incentive-based credit management scheme.…”

“…When a vehicle broadcasts a false message in the Vehicular networks, the vehicles in the same network may take the wrong actions, leading to a bad traffic accident. Herein, to construct a robust and secure trust management framework [15][16][17][18][19][20][21][22][23][24][25][26][27] for the Internet of connected vehicles turns out to be a critical problem for the actual deployment of UGV.…”

“…The existing resolutions assume that the quantity of RSUs (Road Side Unit) is limited [22][23][24][25][26][27][28][29][30], and V2V network communication is very important to ensure the network connectivity. As a result, the communication between the RSU and the vehicle could be badly delayed, and turns out to be the bottleneck if it involves in the process of verifying the trustworthiness of a traffic event message.…”

“…Bogus event ratio over time (seconds). We compare our method with VIME used in[27]. Since the performance of VIME relies on the accuracy their entity based reputation, we set the accuracy as 99%( Accuracy=.1) and 99.9%( Accuracy=.01) individually.…”

A Cooperative Watchdog System to Detect Misbehavior Nodes in Vehicular Delay-Tolerant Networks

“…VIME [24] is an economic incentive model created to combat selfish and misbehavior nodes. Initially, this model gave a certain number of credits to network nodes that will be used by them to forward messages to other network nodes.…”