Incentivizing Private Data Sharing in Vehicular Networks: A Game-Theoretic Approach

In the context of evolving smart cities and autonomous transportation systems, Vehicular Ad-hoc Networks (VANETs) and the Internet of Vehicles (IoV) are growing in significance. Vehicles are becoming more than just a means of transportation; they are collecting, processing, and transmitting massive amounts of data to make driving safer and more convenient. However, this advancement ushers in complex issues concerning the centralized structure of traditional vehicular networks and the privacy and security concerns around vehicular data. This paper offers a novel, game-theoretic network architecture to address these challenges. Our approach decentralizes data collection through distributed servers across the network, aggregating vehicular data into spatio-temporal maps via secure multi-party computation (SMPC). This strategy effectively reduces the chances of adversaries reconstructing a vehicle's complete path, increasing privacy. We also introduce an economic model grounded in game theory that incentivizes vehicle owners to participate in the network, balancing the owners' privacy concerns with the monetary benefits of data sharing. This model aims to maximize the data consumer's utility from the gathered sensor data by determining the most suitable payment to participating vehicles, the frequency in which these vehicles share their data, and the total number of servers in the network. We explore the interdependencies among these parameters and present our findings accordingly. To define meaningful utility and loss functions for our study, we utilize a real dataset of vehicular movement traces.Comment: To Appear in the Proceedings of The 2023 IEEE 98th Vehicular Technology Conference (VTC2023-Fall), 6 pages, 5 figure

DISCO: Achieving Low Latency and High Reliability in Scheduling of Graph-Structured Tasks over Mobile Vehicular Cloud

To effectively process data across a fleet of dynamic and distributed vehicles, it is crucial to implement resource provisioning techniques that provide reliable, cost-effective, and real-time computing services. This article explores resource provisioning for computation-intensive tasks over mobile vehicular clouds (MVCs). We use undirected weighted graphs (UWGs) to model both the execution of tasks and communication patterns among vehicles in a MVC. We then study low-latency and reliable scheduling of UWG asks through a novel methodology named double-plan-promoted isomorphic subgraph search and optimization (DISCO). In DISCO, two complementary plans are envisioned to ensure effective task completion: Plan A and Plan B.Plan A analyzes the past data to create an optimal mapping ($\alpha$) between tasks and the MVC in advance to the practical task scheduling. Plan B serves as a dependable backup, designed to find a feasible mapping ($\beta$) in case $\alpha$ fails during task scheduling due to unpredictable nature of the network.We delve into into DISCO's procedure and key factors that contribute to its success. Additionally, we provide a case study that includes comprehensive comparisons to demonstrate DISCO's exceptional performance in regards to time efficiency and overhead. We further discuss a series of open directions for future research

Integration of Blockchain and Auction Models: A Survey, Some Applications, and Challenges

In recent years, blockchain has gained widespread attention as an emerging technology for decentralization, transparency, and immutability in advancing online activities over public networks. As an essential market process, auctions have been well studied and applied in many business fields due to their efficiency and contributions to fair trade. Complementary features between blockchain and auction models trigger a great potential for research and innovation. On the one hand, the decentralized nature of blockchain can provide a trustworthy, secure, and cost-effective mechanism to manage the auction process; on the other hand, auction models can be utilized to design incentive and consensus protocols in blockchain architectures. These opportunities have attracted enormous research and innovation activities in both academia and industry; however, there is a lack of an in-depth review of existing solutions and achievements. In this paper, we conduct a comprehensive state-of-the-art survey of these two research topics. We review the existing solutions for integrating blockchain and auction models, with some application-oriented taxonomies generated. Additionally, we highlight some open research challenges and future directions towards integrated blockchain-auction models

Intelligent Pricing Model for Task Offloading in Unmanned Aerial Vehicle Mounted Mobile Edge Computing for Vehicular Network

In the fifth-generation (5G) cellular network, the Mobile Network Operator (MNO), and the Mobile Edge Computing (MEC) platform will play an important role in providing services to an increasing number of vehicles. Due to vehicle mobility and the rise of computation-intensive and delay-sensitive vehicular applications, it is challenging to achieve the rigorous latency and reliability requirements of vehicular communication. The MNO, with the MEC server mounted on an unmanned aerial vehicle (UAV), should make a profit by providing its computing services and capabilities to moving vehicles. This paper proposes the use of dynamic pricing for computation offloading in UAV-MEC for vehicles. The novelty of this paper is in how the price influences offloading demand and decides how to reduce network costs (delay and energy) while maximizing UAV operator revenue, but not the offloading benefits with the mobility of vehicles and UAV. The optimization problem is formulated as a Markov Decision Process (MDP). The MDP can be solved by the Deep Reinforcement Learning (DRL) algorithm, especially the Deep Deterministic Policy Gradient (DDPG). Extensive simulation results demonstrate that the proposed pricing model outperforms greedy by 26%and random by 51% in terms of delay. In terms of system utility, the proposed pricing model outperforms greedy only by 17%. In terms of server congestion, the proposed pricing model outperforms random by 19% and is almost the same as greedy

Future cities and autonomous vehicles: analysis of the barriers to full adoption

The inevitable upcoming technology of autonomous vehicles (AVs) will affect our cities and several aspects of our lives. The widespread adoption of AVs repose at crossing distinct barriers that prevent their full adoption. This paper presents a critical review of recent debates about AVs and analyse the key barriers to their full adoption. This study has employed a mixed research methodology on a selected database of recently published research works. Thus, the outcomes of this review integrate the barriers into two main categories; (1) User/Government perspectives that include (i) Users' acceptance and behaviour, (ii) Safety, and (iii) Legislation. (2) Information and Communication Technologies (ICT) which include (i) Computer software and hardware, (ii) Communication systems V2X, and (iii) accurate positioning and mapping. Furthermore, a framework of barriers and their relations to AVs system architecture has been suggested to support future research and technology development

Convergence of Blockchain and Edge Computing for Secure and Scalable IIoT Critical Infrastructures in Industry 4.0

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordCritical infrastructure systems are vital to underpin the functioning of a society and economy. Due to ever-increasing number of Internet-connected Internet-of-Things (IoTs) / Industrial IoT (IIoT), and high volume of data generated and collected, security and scalability are becoming burning concerns for critical infrastructures in industry 4.0. The blockchain technology is essentially a distributed and secure ledger that records all the transactions into a hierarchically expanding chain of blocks. Edge computing brings the cloud capabilities closer to the computation tasks. The convergence of blockchain and edge computing paradigms can overcome the existing security and scalability issues. In this paper, we first introduce the IoT/IIoT critical infrastructure in industry 4.0, and then we briefly present the blockchain and edge computing paradigms. After that, we show how the convergence of these two paradigms can enable secure and scalable critical infrastructures. Then, we provide a survey on state-of-the-art for security and privacy, and scalability of IoT/IIoT critical infrastructures. A list of potential research challenges and open issues in this area is also provided, which can be used as useful resources to guide future research.Engineering and Physical Sciences Research Council (EPSRC

High-level Architecture and Compelling Technologies for an Advanced Web-based Vehicle Routing and Scheduling System for Urban Freight Transportation

The search for a more efficient routing and scheduling, the improvement of service’s level and the increasing complexity of real-world distributive contexts are contingent variables that generate the need for a system’s architecture that may be holistic, innovative, scalable and reliable. Hence, new technologies and a lucid awareness of involved actors and infrastructures, provide the basis to create a more efficient routing and scheduling architecture for enterprises