Road safety, optimized traffic management, and passenger comfort have always been the primary goals of the vehicle networking research community. Advances in computer and communication technologies have made the dream of modern intelligent vehicles a reality through the use of smart sensors, cameras, networking devices, and storage capabilities. Autonomous operation of modern intelligent vehicles requires massive computations where tasks are outsourced. In recent years, various computing paradigms, e.g., mobile cloud computing (MCC), vehicular cloud computing (VCC), multi-access or mobile edge computing (MEC), vehicular edge computing (VEC), vehicular fog computing (VFC), and volunteer computing based VANET (VCBV), have been developed to move computational resources close to the user and handle the delay-sensitive applications of modern intelligent vehicles. Therefore, in this study, we provide a comprehensive overview of all computing paradigms related to vehicular networks. We also present the architectural details, similarities, differences, and key features of each computing paradigm. Finally, we conclude the study with open research challenges in vehicular networks along with future research directions.

Vehicular networks as the key enablers in Intelligent Transportation Systems (ITS) and the Internet of Things (IoT) are key components of smart sustainable cities. Vehicles as a significant component of smart cities have emerging in-vehicle applications that can assist in good governance for sustainable smart cities. Most of these applications are delay sensitive and demand high computational capabilities that are provided by emerging technologies. Utilizing the distributed computational resources of vehicles with the help of volunteer computing is an efficient method to fulfill the high computational requirements of vehicles itself and the other components of smart cities. Vehicle as a resource is an emerging concept that must be considered to address the future challenges of sustainable smart cities. In this paper, an infrastructure-assisted job scheduling and task coordination mechanism in volunteer computing-based VANET called RSU-based VCBV is proposed, which enhances the architecture of VANET to utilize the surplus resources of vehicles for task execution. We propose job scheduling and task coordination algorithms for different volunteer models. Further, we design and implement an adaptive task replication method to seek fault tolerance by avoiding task failures due to locations of vehicles. We propose a task replication algorithm called location-based task replication algorithm. Extensive simulations validate the performance of our proposed volunteer models while comparing average task execution time and weight ratios with existing work.

Computation offloading is a process that provides computing services to vehicles with computation sensitive jobs. Volunteer Computing-Based Vehicular Ad-hoc Networking (VCBV) is envisioned as a promising solution to perform task executions in vehicular networks using an emerging concept known as vehicle-as-a-resource (VaaR). In VCBV systems, offloading is the primary technique used for the execution of delay-sensitive applications which rely on surplus resource utilization. To leverage the surplus resources arising in periods of traffic congestion, we propose a hybrid VCBV task coordination model which performs the resource utilization for task execution in a multi-hop fashion. We propose an algorithm for the determination of boundary relay vehicles to minimize the requirement of placement for multiple road-side units (RSUs). We propose algorithms for primary and secondary task coordination using hybrid VCBV. Extensive simulations show that the hybrid technique for task coordination can increase the system utility, while the latency constraints are addressed.

Location privacy is a critical problem in the vehicular communication networks. Vehicles broadcast their road status information to other entities in the network through beacon messages. The beacon message content consists of the vehicle ID, speed, direction, position, and other information. An adversary could use vehicle identity and positioning information to determine vehicle driver behavior and identity at different visited location spots. A pseudonym can be used instead of the vehicle ID to help in the vehicle location privacy. These pseudonyms should be changed in appropriate way to produce uncertainty for any adversary attempting to identify a vehicle at different locations. In the existing research literature, pseudonyms are changed during silent mode between neighbors. However, the use of a short silent period and the visibility of pseudonyms of direct neighbors provides a mechanism for an adversary to determine the identity of a target vehicle at specific locations. Moreover, privacy is provided to the driver, only within the RSU range; outside it, there is no privacy protection. In this research, we address the problem of location privacy in a highway scenario, where vehicles are traveling at high speeds with diverse traffic density. We propose a Dynamic Grouping and Virtual Pseudonym-Changing (DGVP) scheme for vehicle location privacy. Dynamic groups are formed based on similar status vehicles and cooperatively change pseudonyms. In the case of low traffic density, we use a virtual pseudonym update process. We formally present the model and specify the scheme through High-Level Petri Nets (HLPN). The simulation results indicate that the proposed method improves the anonymity set size and entropy, provides lower traceability, reduces impact on vehicular network applications, and has lower computation cost compared to existing research work.

An encoder system is commonly used as a passive device within a controller system. It is sampled by the controller with the same sampling time as that of the control system. Control loop requires time to execute set of commands hence sampling time of control system cannot be very small and is generally fixed. This limitation made utilization of M/T method unavoidable to measure high and low speeds by switching between M and T method at certain point. Error analysis conducted in this paper showed high-valued errors at the switching instance. Inherent error associated with M-Method, i.e. yielding average speed, is also pointed out. To overcome these problems, an alternative encoder system is proposed, in which sampling time for the measurement system is made independent from control system. Therefore, there will not be any restriction on using high sampling frequency for measurement system. Proposed strategy made it possible to use T-Method, only, for entire range of speed measurement without compromising accuracy. Hence, sources of errors usually encountered by using M/T Method can be avoided. Error analysis of proposed strategy showed notable improvement in measurements.

Preserving location privacy is increasingly an essential concern in Vehicular Adhoc Networks (VANETs). Vehicles broadcast beacon messages in an open form that contains information including vehicle identity, speed, location, and other headings. An adversary may track the various locations visited by a vehicle using sensitive information transmitted in beacons such as vehicle identity and location. By matching the vehicle identity used in beacon messages at various locations, an adversary learns the location history of a vehicle. This compromises the privacy of the vehicle driver. In existing research work, pseudonyms are used in place of the actual vehicle identity in the beacons. Pseudonyms should be changed regularly to safeguard the location privacy of vehicles. However, applying simple change in pseudonyms does not always provide location privacy. Existing schemes based on mix zones operate efficiently in higher traffic environments but fail to provide privacy in lower vehicle traffic densities. In this paper, we take the problem of location privacy in diverse vehicle traffic densities. We propose a new Crowd-based Mix Context (CMC) privacy scheme that provides location privacy as well as identity protection in various vehicle traffic densities. The pseudonym changing process utilizes context information of road such as speed, direction and the number of neighbors in transmission range for the anonymisation of vehicles, adaptively updating pseudonyms based on the number of a vehicle neighbors in the vicinity. We conduct formal modeling and specification of the proposed scheme using High-Level Petri Nets (HPLN). Simulation results validate the effectiveness of CMC in terms of location anonymisation, the probability of vehicle traceability, computation time (cost) and effect on vehicular applications.