Vehicular Cloud Computing

Despite the surge in Vehicular Ad Hoc NETwork (VANET) research, future high-end vehicles are expected to under-utilize the on-board computation, communication, and storage resources. Olariu et al. envisioned the next paradigm shift from conventional VANET to Vehicular Cloud Computing (VCC) by merging VANET with cloud computing. But to date, in the literature, there is no solid architecture for cloud computing from VANET standpoint. In this paper, we put forth the taxonomy of VANET based cloud computing. It is, to the best of our knowledge, the first effort to define VANET Cloud architecture. Additionally we divide VANET clouds into three architectural frameworks named Vehicular Clouds (VC), Vehicles using Clouds (VuC), and Hybrid Vehicular Clouds (HVC). We also outline the unique security and privacy issues and research challenges in VANET clouds.

Vehicular Cloud Computing (VCC) facilitates the customers to share resources ranging from storage to computing power to renting it to other users over the internet. VCC technology is combination of VANET and cloud computing. However, this integration of vehicular network and cloud computing introduces new challenges for the research community. For security reasons, users would like to verify the integrity of their data once storing it in the VCC. In this paper, we will introduce a scheme for the integrity checking with the support of the Road Side Unit (RSU) for dynamic vehicular cloud. The hash functions and signatures are employed for integrity checking of messages which are uploaded from the vehicle to the cloud. We have analysed effectiveness of proposed scheme against typical attacks like tampering attack, node impersonation and application attack etc. Extensive simulations are conducted to verify the performance of the proposed scheme and the results are compared with existing research based on ID-based cryptographic scheme. The proposed scheme performs better in terms of computation cost and communication cost.

Vehicular network technology has made substantial advancements in recent years in the field of Intelligent Transportation Systems. Vehicular Cloud Computing (VCC) has emerged as a novel paradigm with a substantial increase in data exchange within Vehicular ad-hoc networks (VANETs). VCC integrates cloud computing, vehicular networking, and Internet of Things (IoT) technologies. It enables Infrastructure-to-Vehicle (I2V), Vehicle-to-Vehicle (V2V), and Vehicle-to-Device (V2D) communication. VCC optimizes vehicle, cloud infrastructure, and IoT resources while addressing significant communication security and vehicle-user privacy challenges. To address these issues, we developed an RFID-based authentication protocol for VCC based on a Henon map using a hash function. In addition, we also incorporated a Physical Unclonable Function (PUF) to resist physical tampering attacks. We validate the protocol’s security formally and informally. The formal security analysis is conducted through a widely used RoR model. We use the Scyther simulation tool to verify the proposed protocol’s security against various attacks. Moreover, we compare the performance of our protocol with similar existing protocols across important performance parameters such as communication and computation overheads and security attributes. The proposed protocol yields substantial improvements, demonstrating a 40.74% reduction in computation overhead and a 13.03% decrease in communication overhead as compared to related protocols, delivering both enhanced performance and resource efficiency. The analysis demonstrates its capacity to support secure communication in the VCC environment and satisfy desirable security attributes.

A survey on vehicular cloud computing

We acquire or provide most of the services with the help of the Internet in the fast-growing world. We should deploy various kinds of systems globally so that other users can practice the same effortlessly, and use storage framework to provide conveniences world-wide effectively. Nowadays, we practice vehicular communication technology to exchange diversified data for varied intentions, which helps vehicle operators in diversified manners such as traffic awareness, weather conditions, road assistance, automatic toll payment system, etc. In this chapter, we converse about data transmission through vehicular ad-hoc networks, cloud computing in the vehicular technology. After that, we survey of different schemes related to secure routing and geo-location details of vehicles. We illustrate concerning security demands, possible attacks, and challenges in the vehicular cloud computing (VCC) architecture. Conclusively, we suggest a new identification scheme to get access of the VCC system from the user end, which can be secured against varied attacks. Moreover, we do analysis of the suggested system to determine security worthiness and measure total required time to execute the phases.

Vehicular adhoc network (VANET) has a significant potential in reducing traffic congestion to provide a stress-free and safer platform for road drivers to travel on the road. However, the current VANET is vulnerable to several challenges which need to be overcome. Congestion control is considered as one of the main challenges in VANET due to the high dynamic topology characteristic. Reliable congestion control (CC) are necessary to provide effectient dissemination of time-critical safety messages in VANET applications; safety and non-safety applications. In this paper, we present the overview on VANET, its application and challenges. We also discuss on the congestion control and provide a brief survey on the congestion control algorithms such as vehicular cloud computing, multiplicative rate decreasing algorithm, multi-objective Tabu search, D-FPAV algorithm and beaconing strategies which have been proposed in order to provide better solutions towards achieving a successful Smart Tranporation System.

OF DISSERTATION Secure and Authenticated Message Dissemination in Vehicular ad hoc Networks and an Incentive-based Architecture for Vehicular Cloud Vehicular ad hoc Networks (VANETs) allow vehicles to form a self-organized network. VANETs are likely to be widely deployed in the future, given the interest shown by industry in self-driving cars and satisfying their customers various interests. Problems related to Mobile ad hoc Networks (MANETs) such as routing, security, etc. have been extensively studied. Even though VANETs are special type of MANETs, solutions proposed for MANETs cannot be directly applied to VANETs because all problems related to MANETs have been studied for small networks. Moreover, in MANETs, nodes can move randomly. On the other hand, movement of nodes in VANETs are constrained to roads and the number of nodes in VANETs is large and covers typically large area. The following are the contributions of the thesis. Secure, authenticated, privacy preserving message dissemination in VANETs: When vehicles in VANET observe phenomena such as accidents, icy road condition, etc., they need to disseminate this information to vehicles in appropriate areas so the drivers of those vehicles can take appropriate action. When such messages are disseminated, the authenticity of the vehicles disseminating such messages should be verified while at the same time the anonymity of the vehicles should be preserved. Moreover, to punish the vehicles spreading malicious messages, authorities should be able to trace such messages to their senders when necessary. For this, we present an efficient protocol for the dissemination of authenticated messages. Incentive-based architecture for vehicular cloud: Due to the advantages such as flexibility and availability, interest in cloud computing has gained lot of attention in recent years. Allowing vehicles in VANETs to store the collected information in the cloud would facilitate other vehicles to retrieve this information when they need. In this thesis, we present a secure incentive-based architecture for vehicular cloud. Our architecture allows vehicles to collect and store information in the cloud; it also provides a mechanism for rewarding vehicles that contributing to the cloud. Privacy preserving message dissemination in VANETs: Sometimes, it is sufficient to ensure the anonymity of the vehicles disseminating messages in VANETs. We present a privacy preserving message dissemination protocol for VANETs.

With the increased number of vehicles, the traditional VANETs (Vehicular Ad-hoc Networks) experiencing new face transitions. One such concept is Vehicular Cloud Computing (VCC), where the data exchanges between vehicles or any entities, in traffic condition or in an emergency situation can be easily done. Establishing authentication and secure communication in VCC is a major problem because of the highly dynamic nature of vehicles. False messages may mislead the drivers which may cause serious issues. In this paper, we propose a lightweight protocol for authentication and secure communication in the VCC. More specifically, we design a lightweight protocol with hash operations, XORs, concatenations, etc., to ensure security. In-depth security analysis is done to check various attacks in the VCC system. In addition to that, we have done hardware implementation on a desktop computer, on a small single board computer-Raspberry Pi and verified the execution time needed. The performance analysis reveals that our protocol outperforms well in different scenarios like communication cost, storage overhead, computation time, and energy consumption.

With the acceleration of mobile applications, mobile cloud computing is envisioned to be the best fit solution to make a compromise between users' and service providers' benefits. An extension of mobile cloud computing, vehicular cloud computing, provides another viable solution, by consolidating the benefits of mobile cloud computing and vehicular communications. Among several challenges in this environment, privacy, service price and provision delay are the most important. In this paper, we propose a framework to address these challenges in a vehicular cloud based on a quality-of-experience (QoE) approach, discuss the drawbacks of existing architectures, and propose and validate a new architecture. This architecture is an extension of a system [1] we proposed in previous work. QoE is obtained via other mobile nodes in the vehicular cloud, and re-formulated according to a weighted combination of the three key factors: privacy, price and delay. Privacy is defined as a function of the information revealed to the service provider. We evaluate our proposal via simulations, and based on the numerical results, we show that QoE-based service provisioning in a vehicular cloud improves upon a naïve service provision approach, as well as other approaches that address only one of the factors.

Vehicular cloud is getting significant research attention due to the technological advancements in smart vehicles. In near future, vehicles are envisioned to become part of a grid network providing cloud services, such as computing, storage, network, and application as a service. Vehicular cloud computing is an emerging area, designed to support delay-sensitive applications. However, this integration of vehicular network and cloud computing introduces new challenges for the research community. New frameworks have been proposed to assimilate and efficiently manage this merger. In this survey paper, we present the recent advancements in vehicular cloud computing domain. The review is primarily focused on two areas. First, we discuss the frameworks designed to utilize the vehicles’ onboard resources to provide cloud services and highlight the design issues and research challenges. Secondly, we focus on a detailed study of mobility generators, network, and vehicular ad hoc network simulators, as well as the available vehicular data sets. We thus provide an overarching view of the complete domain of vehicular cloud computing and identify areas for future research directions.

Vehicular Cloud Computing (VCC) denotes a shared pool of computing resources in a set of wirelessly connected vehicles, which are available for on- demand and rapid access based on the pay-as-you-go fashion. VCC consolidates the benefits of Mobile Cloud Computing (MCC) and vehicular communications. Prior to the VCC concept being widely adopted, several research issues and challenges need to be carefully addressed in such environment. Privacy, service cost and provisioning delay are identified as the most crucial challenges to be addressed. State of the art includes several proposals to address these issues however a combined solution that aims to address these requirements holistically does not exist. This paper refines our recently presented frameworks in [1][2], and extends it by building a generalized multi-objective framework, namely Auction-driven QoE Provisioning (AQoEP). A Trusted Third Party (TTP) approach has been adopted to address such challenges. Simulations results are extensively studied to proof the concept of the proposed framework. Through simulations, we proof that AQoEP in a vehicular cloud better fulfils the vehicular service requirements than any other proposed approach.

To satisfy the explosive growth of computation-intensive vehicular applications, we investigated the computation offloading problem in a cognitive vehicular networks (CVN). Specifically, in our scheme, the vehicular cloud computing (VCC)- and remote cloud computing (RCC)-enabled computation offloading were jointly considered. So far, extensive research has been conducted on RCC-based computation offloading, while the studies on VCC-based computation offloading are relatively rare. In fact, due to the dynamic and uncertainty of on-board resource, the VCC-based computation offloading is more challenging then the RCC one, especially under the vehicular scenario with expensive inter-vehicle communication or poor communication environment. To solve this problem, we propose to leverage the VCC’s computation resource for computation offloading with a perception-exploitation way, which mainly comprise resource discovery and computation offloading two stages. In resource discovery stage, upon the action-observation history, a Long Short-Term Memory (LSTM) model is proposed to predict the on-board resource utilizing status at next time slot. Thereafter, based on the obtained computation resource distribution, a decentralized multi-agent Deep Reinforcement Learning (DRL) algorithm is proposed to solve the collaborative computation offloading with VCC and RCC. Last but not least, the proposed algorithms’ effectiveness is verified with a host of numerical simulation results from different perspectives.

Vehicular communication becomes an advanced area of research to deliver services to the users like standardization, traffic management and road safety, infotainment and entertainment. Vehicles carry communication modules, computing facility, storage, Internet accessing capability and equipped with application specific sensors in the on-board unit. Recently, vehicular cloud computing is a technology that is embedded with vehicular networks to solve many vehicular networking issues and challenges like storage, computing, information gain, internet access, network, security, etc. In this paper, we consider the storage as a service (STaaS) issue in vehicular networks. The vehicles in the parking lots are used as a data center to store the information of the user. However, providing this service to the users requires a scheduling policy to give the storage service in a minimum time. Therefore, we develop a new scheduling policy, called TSP-HVC for STaaS in the vehicular cloud environment. The simulation results show that the proposed scheduling policy produces less makespan and high average resource utilization than the well-known min–min and max–min cloud scheduling policies.

Fog computing for vehicular Ad-hoc networks: paradigms, scenarios, and issues

Vehicular Cloud Computing (VCC) is a new technological shift which exploits the computation and storage resources on vehicles for computational service provisioning. Spare onboard resources are pooled by a VCC operator, e.g. a roadside unit, to serve computational tasks using the vehicle-as-a-resource framework. This paper investigates timely service provisioning for deadline-constrained tasks in VCC systems by leveraging the task replication technique (i.e., allowing one task to be executed by vehicles). A learning-based algorithm, called DATEV (Deadline-Aware Task rEplication for Vehicular Cloud), is proposed to address the special issues in VCC systems including uncertainty of vehicle movements, volatile vehicle members, and large vehicle population. The proposed algorithm is developed based on a novel contextual-combinatorial multi-armed bandit learning framework. DATE-V is " contextual " because it utilizes side information (context) of vehicles and tasks to infer the completion probability of a task replication under random vehicle movements. DATE-V is "combinatorial" because it replicates the received task and sends task replications to multiple vehicles to guarantee the service timeliness. When learning with multi-armed bandit, DATE-V also addresses the practical concern of delayed feedbacks caused by the task transmission/computational delay in using VCC. We rigorously prove that our learning algorithm achieves a sublinear regret bound compared to an oracle algorithm that knows the exact completion probability of any task replications. Simulations are carried out based on real-world vehicle movement traces and the results show that DATE-V significantly outperforms benchmark solutions.