No description available.

The emergence of Vehicular Ad-hoc Networks (VANETs) in the past decade has added a level of complexity to the modelling of Intelligent Transportation System (ITS) applications. In this paper, the Vehicular Network Integrated Simulator (VNetIntSim) is introduced as a new transportation network and VANET simulation tool by integrating transportation and VANET modelling. Specifically, it integrates the OPNET software, a communication network simulator, and the INTEGRATION software, a microscopic traffic simulation software. The INTEGRATION software simulates the movement of travellers and vehicles, while the OPNET software models the data exchange through the communication system. Information is exchanged between the two simulators as needed. The paper describes the implementation and the operation details of the VNetIntSim as well as the features it supports such as multiclass support and vehicle reuse. Subsequently, VNetIntSim is used to quantify the impact of mobility parameters (vehicular traffic stream speed and density) on the communication system performance considering Transmission Control Protocol (TCP) and User Datagram Protocol (UDP) applications. Specifically, the routing performance (packet drops and route discovery time), IP processing delay in case of a file transfer protocol (FTP) application, and jitter in case of a Voice over Internet Protocol (VoIP) application and evaluated.

Vehicular Ad-hoc Network (VANET) has emerged as a promising solution for enhancing road safety. Routing of messages in VANET is challenging due to packet delays arising from high mobility of vehicles, frequently changing topology, and high density of vehicles, leading to frequent route breakages and packet losses. Previous researchers have used either mobility in vehicular fog computing or cloud computing to solve the routing issue, but they suffer from large packet delays and frequent packet losses. We propose Dynamic Fog for Connected Vehicles (DFCV), a fog computing based scheme which dynamically creates, increments and destroys fog nodes depending on the communication needs. The novelty of DFCV lies in providing lower delays and guaranteed message delivery at high vehicular densities. Simulations were conducted using hybrid simulation consisting of ns-2, SUMO, and Cloudsim. Results show that DFCV ensures efficient resource utilization, lower packet delays and losses at high vehicle densities.

The safety and efficiency of vehicular communications rely on the correctness of the data exchanged between vehicles. Location spoofing is a proven and powerful attack against Vehicle-to-everything (V2X) communication systems that can cause traffic congestion and other safety hazards. Recent work also demonstrates practical spoofing attacks that can confuse intelligent transportation systems at road intersections. In this work, we propose two self-reliant schemes at the application layer and the physical layer to detect such misbehaviors. These schemes can be run independently by each vehicle and do not rely on the assumption that the majority of vehicles is honest. We first propose a scheme that uses application-layer plausibility checks as a feature vector for machine learning models. Our results show that this scheme improves the precision of the plausibility checks by over 20% by using them as feature vectors in KNN and SVM classifiers. We also show how to classify different types of known misbehaviors, once they are detected. We then propose three novel physical layer plausibility checks that leverage the received signal strength indicator (RSSI) of basic safety messages (BSMs). These plausibility checks have multi-step mechanisms to improve not only the detection rate, but also to decrease false positives. We comprehensively evaluate the performance of these plausibility checks using the VeReMi dataset (which we enhance along the way) for several types of attacks. We show that the best performing physical layer plausibility check among the three considered achieves an overall detection rate of 83.73% and a precision of 95.91%. The proposed application-layer and physical-layer plausibility checks provide a promising framework toward the deployment of on self-reliant misbehavior detection systems.

Clustering serves a vital role in the operation of Vehicular Ad hoc Networks (VANETs) by continually grouping highly mobile vehicles into logical hierarchical structures. These moving clusters support Intelligent Transport Systems (ITS) applications and message routing by establishing a more stable global topology. Clustering increases scalability of the VANET by eliminating broadcast storms caused by packet flooding and facilitate multi-channel operation. Clustering techniques are partitioned in research into two categories: active and passive. Active techniques rely on periodic beacon messages from all vehicles containing location, velocity, and direction information. However, in areas of high vehicle density, congestion may occur on the long-range channel used for beacon messages limiting the scale of the VANET. Passive techniques use embedded information in the packet headers of existing traffic to perform clustering. In this method, vehicles not transmitting traffic may cause cluster heads to contain stale and malformed clusters. This dissertation presents a hybrid active/passive clustering technique, where the passive technique is used as a congestion control strategy for areas where congestion is detected in the network. In this case, cluster members halt their periodic beacon messages and utilize embedded position information in the header to update the cluster head of their position. This work demonstrated through simulation that the hybrid technique reduced/eliminated the delays caused by congestion in the modified Distributed Coordination Function (DCF) process, thus increasing the scalability of VANETs in urban environments. Packet loss and delays caused by the hidden terminal problem was limited to distant, non-clustered vehicles. This dissertation report presents a literature review, methodology, results, analysis, and conclusion.

No description available.

The dynamic nature of vehicular ad hoc network (VANET) induced by frequent topology changes and node mobility, imposes critical challenges for vehicular communications. Aggravated by the high volume of information dissemination among vehicles over limited bandwidth, the topological dynamics of VANET causes congestion in the communication channel, which is the primary cause of problems such as message drop, delay, and degraded quality of service. To mitigate these problems, congestion detection, and control techniques are needed to be incorporated in a vehicular network. Congestion control approaches can be either open-loop or closed loop based on pre-congestion or post congestion strategies. We present a general architecture of vehicular communication in urban and highway environment as well as a state-of-the-art survey of recent congestion detection and control techniques. We also identify the drawbacks of existing approaches and classify them according to different hierarchical schemes. Through an extensive literature review, we recommend solution approaches and future directions for handling congestion in vehicular communications.

The Internet of Things (IoT) is a vision for an internetwork of intelligent, communicating objects, which is on the cusp of transforming human lives. Smart transportation is one of the critical application domains of IoT and has benefitted from using state-of-the-art technology to combat urban issues such as traffic congestion while promoting communication between the vehicles, increasing driver safety, traffic efficiency and ultimately paving the way for autonomous vehicles. Connected Vehicle (CV) technology, enabled by Dedicated Short Range Communication (DSRC), has attracted significant attention from industry, academia, and government, due to its potential for improving driver comfort and safety. These vehicular communications have stringent transmission requirements. To assure the effectiveness and reliability of DRSC, efficient algorithms are needed to ensure adequate quality of service in the event of network congestion. Previously proposed congestion control methods that require high levels of cooperation among Vehicular Ad-Hoc Network (VANET) nodes. This paper proposes a new approach, VANETomo, which uses statistical Network Tomography (NT) to infer transmission delays on links between vehicles with no cooperation from connected nodes. Our proposed method combines open and closed loops congestion control in a VANET environment. Simulation results show VANETomo outperforming other congestion control strategies.

The emergence of connected vehicles paradigm has made secure communication a key concern amongst the connected vehicles. Communication between the vehicles and Road Side Units (RSUs) is critical to disseminate message among the vehicles. We focus on secure message transmission in connected vehicles using multi-hop social networks environment to deliver the message with varying trustworthiness. We proposed a Geographic Social Trust Routing (GSTR) approach; messages are propagated using multiple hops and by considering the various available users in the vehicular network. GSTR is proposed in an application perspective with an assumption that the users are socially connected. The users are selected based on trustworthiness as defined by social connectivity. The route to send a message is calculated based on the highest trust level of each node by using the node's social network connections along the path in the network. GSTR determines the shortest route using the trusted nodes along the route for message dissemination. GSTR is made delay tolerant by introducing message storage in the cloud if a trustworthy node is unavailable to deliver the message. We compared the proposed approach with Geographic and Traffic Load based Routing (GTLR), Greedy Perimeter Stateless Routing (GPSR), Trust-based GPSR (T-GPSR). The performance results obtained show that GSTR ensures efficient resource utilization, lower packet losses at high vehicle densities.

In intelligent transportation systems, the cooperation between vehicles and the road side units is essential to bring these systems to fruition. Vehicular ad hoc networks (VANETs) are a promising technology to enable the communications among vehicles on one hand and between vehicles and road side units on the other hand. However, it is a challenging task to develop a reliable routing algorithm for VANETs due to the high mobility and the frequent changes of the network topology. Communication links are highly vulnerable to disconnection in VANETs; hence, the routing reliability of these ever-changing networks needs to be paid special attention. In this paper, we propose a new vehicular reliability model to facilitate the reliable routing in VANETs. The link reliability is defined as the probability that a direct communication link between two vehicles will stay continuously available over a specified time period. Furthermore, the link reliability value is accurately calculated using the location, direction and velocity information of vehicles along the road. We extend the well-known ad hoc on-demand distance vector (AODV) routing protocol to propose our reliable routing protocol AODV-R. Simulation results demonstrate that AODV-R outperforms significantly the AODV routing protocol in terms of better delivery ratio and less link failures while maintaining a reasonable routing control overhead.