This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract-In this work, an Intrusion Detection System (IDS) for vehicular ad hoc networks (VANETs) is proposed and evaluated. The IDS is evaluated by simulation in presence of rogue nodes that can launch different attacks. The proposed IDS is capable of detecting a false information attack using statistical techniques effectively and can also detect other types of attacks. First, the theory and implementation of the VANET model that is used to train the IDS is discussed. Then an extensive simulation and analysis of our model under different traffic conditions is conducted to identify the effects of these parameters in VANETs. In addition, the extensive data gathered in the simulations is presented using graphical and statistical techniques. Moreover, rogue nodes are introduced in the network and an algorithm is presented to detect these rogue nodes. Finally, we evaluate our system and observe that the proposed application layer IDS based on cooperative information exchange mechanism is better for dynamic and fast moving networks such as VANETs as compared to other techniques available. Permanent repository link

This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link

Abstract-Vehicular ad hoc networks (VANETs) are the future of vehicular technology and Traffic Information Systems. In VANETs vehicles communicate by different types of beacon messages to inform each other of their position and speed to give them a sense of traffic around them. Vehicles can also send emergency messages in case of accidents or other hazards. The very fast moving nodes have to act quickly based on these emergency messages. However, a rogue node which sends false emergency messages can wreak havoc in the network that may even result in fatalities. This paper develops and simulates a technique to detect a rogue node that is sending false emergency messages in VANETs by cooperative exchange of data without the need of any infrastructure or revocation list. Also, the proposed mechanism will make VANETs fault tolerant and resilient against injection of false data.

Abstract-Intelligent location-aware data aggregation mechanism for real-time observation, estimation and efficient dissemination of any kind of traffic information in vehicular adhoc networks (VANETs) is presented in this paper. The mechanism introduces location awareness algorithm, enabling spatiotemporal database indexing and providing location context of the messages without the use of advanced positioning systems like satellite navigation and digital maps. Intelligent passive clustering and adaptive broadcasting are used to minimize the number of messages exchanged, packet collisions and network load. The incoming messages are fused by Kalman filter allowing the description of the traffic related information as a system characterized by as many variables as needed, depending on the application design. The scheme allows the comparison of aggregates and single observations which enables their merging and better overall accuracy. Old information in aggregates is removed by real-time database refreshing thus leaving only newer relevant information for driver to make real-time decisions in traffic. The mechanism is generic and can be used for any kind of VANET information. It is evaluated by extensive simulations to show the efficiency and accuracy.

Abstract-Vehicular traffic congestion is a well-known economic and social problem generating significant costs and safety challenges, and increasing pollution in the cities. Current intelligent transport systems and vehicular networking technologies rely heavily on the supporting network infrastructure which is still not widely available. This paper contributes towards the development of distributed and cooperative vehicular traffic congestion detection by proposing a new vehicle-to-vehicle (V2V) congestion detection algorithm based on the IEEE 802.11p standard. The new algorithm allows vehicles to be self-aware of the traffic in the street, performing congestion detection based on speed monitoring and cooperation with the surrounding vehicles. Cooperation is achieved using adaptive single-hop broadcasting which depends on the level of congestion. The paper presents the congestion detection algorithm and the cooperative communication in detail, and presents performance evaluation using large-scale simulation in Veins framework based on OMNeT++ network simulator and SUMO vehicular mobility simulator. Results show that precise congestion detection and quantification can be achieved using a significantly decreased number of exchanged packets.

Abstract-This paper presents a Decentralized data Dissemination and Harvesting (DDH) mechanism for urban pollution monitoring using mobile sensor nodes with limited resources. The proposed DDH mechanism enables participating nodes to self-decide whether to process the received data or not, thus, reducing the on-board processing load. Based on the harvested data, the nodes calculate their level of interest in monitoring the particular street segments. In this way a reduction in the number of actively participating nodes is accomplished. In addition, the mobile nodes process raw sensor readings using the Delayed State Information Filter (DSIF) to maintain the past pollution states and perform a decentralized data fusion. The proposed DDH mechanism is assessed using simulations with varying number of the participating nodes. The results show that the proposed mechanism outperforms existing solutions in terms of the utilisation of nodes resources, without affecting the amount of volume of gathered data for the monitored street segments.

This paper presents a Decentralized Data Fusion (DDF) framework for micro-scale monitoring applications in urban environments using a mobile sensor network. Here nodes collect data along their routes and share them with other nodes in the network in an opportunistic manner. The DDF framework enables the nodes to fuse data that arrives delayed from other nodes and estimate the missing values in the time gaps. This allows the nodes to create an autonomous perception about the dynamics of the observed phenomenon. The performance of the proposed DDF framework is demonstrated in the context of an urban air pollution monitoring scenario. Simulation results show that the proposed framework is able to expand the estimated pollution data set at the expense of a slight decrease in its accuracy. The simulation results also evaluate the impact of population of nodes on the performance of the DDF framework.

This paper presents the beaconless multi-hop Decentralised Dissemination of Warnings (DDW) mechanism for micro-scale monitoring applications in urban environments. It is designed for a Vehicular Sensor Network (VSN) based on mobile nodes with limited resources. Mobile nodes sense and generate high pollution levels early warnings in near real-time. The warning messages are disseminated toward Static Monitoring Units used by the local authorities to monitor onsets of harmful pollution level episodes. The DDW is a distance-based mechanism where a receiving node calculates the waiting time before rebroadcsting the message based on the distance from the sending node and the distance to the Static Monitoring Units. The DDW mechanism performance is evaluated in an urban environment with different number of mobile nodes in the network. Results show that the proposed mechanism collects more non-duplicated warnings at the Static Monitoring Units than other evaluated dissemination protocols. It is also shown the DDW mechanism reduces the amount of duplicated warning messages sent in the network, which is especially important when mobile nodes are resource constrained.