Services and simulation frameworks for vehicular cloud computing: a contemporary survey

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 cloud computing (VCC) has been utilized to enhance traffic management and road safety. By connecting with base stations (BSes), VCC can provide the information of real-time dynamics for smart vehicles (SVs). However, the area outside the coverage of BSes will be the blind areas, where SVs cannot obtain the real-time safety guarantee, especially on the highway. In this article, we utilize unmanned aerial vehicles (UAVs) to assist the communication between SVs and BSes to solve the above problem. In particular, we study the interdependency task scheduling for the highway driving environment detection, where SVs, BSes, and UAVs collect the environmental data, schedule tasks, and feedback results cooperatively. There are two main problems in this scenario: 1) the scheduling within the coverage of BSes and 2) the rescheduling between the coverage of BSes. We model both the processes as constrained numerical optimization problems aiming to minimize the request-response time. To this end, we propose a systematical scheduling scheme named Teso, which consists of two stages: 1) designed approximation algorithm for scheduling and 2) offloading algorithm for rescheduling. Extensive experiments show that Teso can significantly reduce the response time overall and improve the system stability.

A survey on 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) 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.

Recently, Vehicular Ad Hoc Networks (VANET) have attracted the attention of research communities, leading car manufacturers, and governments due to their potential applications and specific characteristics. Their research outcome was started with awareness between vehicles for collision avoidance and Internet access and then expanded to vehicular multimedia communications. Moreover, vehicles’ high computation, communication, and storage resources set a ground for vehicular networks to deploy these applications in the near future. Nevertheless, on-board resources in vehicles are mostly underutilized. Vehicular Cloud Computing (VCC) is developed to utilize the VANET resources efficiently and provide subscribers safe infotainment services. In this chapter, the authors perform a survey of state-of-the-art vehicular cloud computing as well as the existing techniques that utilize cloud computing for performance improvements in VANET. The authors then classify the VCC based on the applications, service types, and vehicular cloud organization. They present the detail for each VCC application and formation. Lastly, the authors discuss the open issues and research directions related to VANET cloud computing.

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.

Facilitated by the emerging technologies of cloud computing, vehicular cloud computing (VCC) is being expected to be a new paradigm to create a new situation for vehicular data services. However, with the increasing popularity of vehicular cloud services, accessing vehicular terminals and produced mobile data are experiencing tremendous growth, therefore, how to provide enough capacity for all access requirements and handle all the mobile data traffic to address the challenges posed by richer vehicular applications are critical. In future 5G mobile networks, promoted by the advantages of supporting various connectivity and significantly faster transmission performance of 5G communications, VCC will be enabled to provide more realistic vehicular data cloud services. In this article, an innovative VCC platform with the support of 5G communications for providing effective vehicular data cloud services is first proposed. Then, in this context, a vehicular social-based taxi-sharing cloud service is mainly investigated. Finally, some open issues and challenges with possible solutions for future work are discussed.

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.

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.

Vehicular cloud computing (VCC) is composed of multiple distributed vehicular clouds (VCs), which are formed on-the-fly by dynamically integrating underutilized vehicular resources including computing power, storage, and so on. Existing proposals for identity-as-a-service (IDaaS) are not suitable for use in VCC due to limited computing resources and storage capacity of onboard vehicle devices. In this paper, we first propose an improved ciphertext-policy attribute-based encryption (CP-ABE) scheme. Utilizing the improved CP-ABE scheme and the permissioned blockchain technology, we propose a lightweight and privacy-preserving IDaaS architecture for VCC named IDaaSoVCC. It realizes lightweight and privacy-preserving access control of vehicles’ personally identifiable information (PII) in a large distributed vehicular cloud system. Security analysis demonstrates the security features of IDaaSoVCC, most notably forward secrecy, confidentiality and identity information privacy. Meanwhile, we verify that IDaaSoVCC is feasible and practical in a large distributed VC system through extensive simulations.

The rapid technological evolution of cloud computing and technical innovation in the intelligent transportation system have allowed vehicular cloud computing (VCC) to gain massive popularity across industry and academia. Recent studies have shown that modern vehicles have the potential for hosting virtual machines (VM) to augment the services of cloud service providers (CSP). Nowadays, vehicles are powered by alternative fuels (i.e., green vehicles (GVs)) that generate less carbon footprint than conventional vehicles (CVs). The cost of executing a User Request (UR) by a VM hosted on a GV is less than a CV. However, GVs are subjected to constraints, such as irregularity in energy availability, limited processing capability and lower bandwidth capacity. The submitted URs by the cloud users have different quality of service (QoS) requirements. QoS defines the priority, promised completion time and promised cost between the CSP and the cloud users. In this paper, we propose energy-aware service management (EASM) algorithm that considers different types of energy sources of vehicles and priority of the URs, to map and migrate them to the vehicles on the move, vehicles in the parking lot and roadways. We compare the EASM algorithm with the existing vehicular VM migration-uniform (VVMM-U), and mobility and destination workload aware migration (MDWLAM) algorithms through an extensive simulation process. Simulation results indicate that the proposed algorithm outperforms the existing algorithms in terms of four performance metrics, such as total cost, cost of completed URs, profit of completed URs, and percentage of completed high priority URs.KeywordsEnergy-aware service managementVehicular cloud computingRoadside unitGreen vehicleVirtual machineUser request

VCC is a computing paradigm which consists of vehicles cooperating with each other to realize a lot of practical applications, such as delivering packages. Security cooperation is a fundamental research topic in Vehicular Cloud Computing (VCC). Because of the existence of malicious vehicles, the security cooperation has become a challenging issue in VCC. In this paper, a trust-based model for security cooperating, named DBTEC, is proposed to promote vehicles’ security cooperation in VCC. DBTEC combines the indirect trust estimation in Public board and the direct trust estimation in Private board to compute the trust value of vehicles when choosing cooperative partners; a trustworthy cooperation path generating scheme is proposed to ensure the safety of cooperation and increase the cooperation completion rates in VCC. Extensive experiments show that our scheme improves the overall cooperation completion rates by 6~7%.

In intelligent transportation systems, Vehicular Cloud Computing (VCC) is a new technology that can help ensure road security and transport efficiency. The study and evaluation of performances of a VCC is a topic of crucial interest in these environments. This paper presents a model of the computation resource allocation problem in VCC by considering heterogeneity and priority of service requests. We consider service requests from two classes, Primary service requests and Secondary service requests. We involve a Semi-Markov Decision Process (SMDP) to achieve the optimal policy that maximizes the performances of the VCC system taking into account the variability of resources, the income and the system cost. We utilize an iterative approach to achieve the optimal scheme that characterizes the action to be taken under each state. We validate our study by numerical results that show the effectiveness of the proposed SMDP-based scheme.

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.