Xumin Huang

Cooperative Resource Management in Cloud-Enabled Vehicular Networks

Cloud-enabled vehicular networks are a new paradigm to improve the quality of vehicular services, which have drawn considerable attention in industry and academia. In this paper, we consider the resource management and sharing problem for bandwidth and computing resources to support mobile applications in cloud-enabled vehicular networks. In such an environment, cloud service providers (SPs) can cooperate to form coalitions to share their idle resources with each other. We propose a coalition game model based on two-sided matching theory for cooperation among cloud SPs to share their idle resources. As a result, the resources can be better utilized, and the QoS for users can be improved. Numerical results indicate that our scheme can improve resource utilization and increase by 75% the QoS of the applications compared with that without cooperation. Moreover, the higher service cost of cooperation brings negative effect on coalition formation. The higher cooperation willingness of cloud SPs and the lower service cost support more service applications.

MixGroup: Accumulative Pseudonym Exchanging for Location Privacy Enhancement in Vehicular Social Networks

Vehicular social network (VSN) is envisioned to serve as an essential data sensing, exchanging and processing platform for the future Intelligent Transportation Systems. In this paper, we aim to address the location privacy issue in VSNs. In traditional pseudonym-based solutions, the privacy-preserving strength is mainly dependent on the number of vehicles meeting at the same occasion. We notice that an individual vehicle actually has many chances to meet several other vehicles. In most meeting occasions, there are only few vehicles appearing concurrently. Motivated by these observations, we propose a new privacy-preserving scheme, called MixGroup, which is capable of efficiently exploiting the sparse meeting opportunities for pseudonym changing. By integrating the group signature mechanism, MixGroup constructs extended pseudonym-changing regions, in which vehicles are allowed to successively exchange their pseudonyms. As a consequence, for the tracking adversary, the uncertainty of pseudonym mixture is accumulatively enlarged, and therefore location privacy preservation is considerably improved. We carry out simulations to verify the performance of MixGroup. Results indicate that MixGroup significantly outperforms the existing schemes. In addition, MixGroup is able to achieve favorable performance even in low traffic conditions.

Optimal Resource Sharing in 5G-Enabled Vehicular Networks: A Matrix Game Approach

Vehicular networks are expected to accommodate a large number of data-heavy mobile devices and multiapplication services, whereas it faces a significant challenge when we need to deal with the ever-increasing demand of mobile traffic. In this paper, we present a new paradigm of fifth-generation (5G)-enabled vehicular networks to improve network capacity and system computing capability. We extend the original cloud radio access network (C-RAN) to integrate local cloud services to provide a low-cost, scalable, self-organizing, and effective solution. The new C-RAN is named enhanced C-RAN (EC-RAN). Cloudlets in EC-RAN are geographically distributed for local services. Furthermore, device-to-device (D2D) and heterogeneous networks are essential technologies in 5G systems. They can greatly improve spectrum efficiency and support large-scale live video streaming in short-distance communications. We exploit matrix game theoretical approach to operate the cloudlet resource management and allocation. A Nash equilibrium solution can be obtained by a Karush-Kuhn-Tucker (KKT) nonlinear complementarity approach. Illustrative results indicate that the proposed resource-sharing scheme with the geodistributed cloudlets can improve resource utilization and reduce system power consumption. Moreover, with the integration of a software-defined network architecture, a vehicular network can easily reach a globally optimal solution.

Toward secure energy harvesting cooperative networks

The concept of energy harvesting cooperative networks is an emerging technology that has very high potential for a large variety of applications. However, energy transfer capability may lead to unprecedented security challenges. In this article, we study energy security issues and the solutions in energy harvesting networks. We first identify typical energy related attacks and then propose defense solutions against these attacks. We also carry out security analysis and performance analysis to evaluate our proposed solutions. Simulation results have shown that the proposed defense solutions are effective and efficient.

Enabling Localized Peer-to-Peer Electricity Trading Among Plug-in Hybrid Electric Vehicles Using Consortium Blockchains

We propose a localized peer-to-peer (P2P) electricity trading model for locally buying and selling electricity among plug-in hybrid electric vehicles (PHEVs) in smart grids. Unlike traditional schemes, which transport electricity over long distances and through complex electricity transportation meshes, our proposed model achieves demand response by providing incentives to discharging PHEVs to balance local electricity demand out of their own self-interests. However, since transaction security and privacy protection issues present serious challenges, we explore a promising consortium blockchain technology to improve transaction security without reliance on a trusted third party. A localized P2P Electricity Trading system with COnsortium blockchaiN (PETCON) method is proposed to illustrate detailed operations of localized P2P electricity trading. Moreover, the electricity pricing and the amount of traded electricity among PHEVs are solved by an iterative double auction mechanism to maximize social welfare in this electricity trading. Security analysis shows that our proposed PETCON improves transaction security and privacy protection. Numerical results based on a real map of Texas indicate that the double auction mechanism can achieve social welfare maximization while protecting privacy of the PHEVs.

Location privacy attacks and defenses in cloud-enabled internet of vehicles

As one of the promising branches of the Internet of Things, the cloud-enabled Internet of Vehicles (CE-IoV) is envisioned to serve as an essential data sensing, exchanging, and processing platform with powerful computing and storage capabilities for future intelligent transportation systems. The CE-IoV shows great promise for various emerging applications. In order to ensure uninterrupted and high-quality services, a vehicle should move with its own VM via live VM migration to obtain real-time location-based services. However, the live VM migration may lead to unprecedented location privacy challenges. In this article, we study location privacy issues and defenses in CE-IoV. We first present two kinds of unexplored VM mapping attacks, and thus design a VM identifier replacement scheme and a pseudonym-changing synchronization scheme to protect location privacy. We carry out simulations to evaluate the performance of the proposed schemes. Numerical results show that the proposed schemes are effective and efficient with high quality of privacy.

Software Defined Energy Harvesting Networking for 5G Green Communications

Energy and spectrum resources play significant roles in 5G communication systems. In industrial applications in the 5G era, green communications are a great challenge for sustainable development of networks. Energy harvesting technology is a promising approach to prolong network lifetime. In energy harvesting networks, nodes may replenish energy from a mobile charger to overcome variations of renewable energy. In this article, energy-rich nodes are stimulated to upload surplus energy to the mobile charger, leading to bidirectional energy flow. This creates a new paradigm wherein energy flows coexist with data flows, which gives rise to new problems in controlling the energy flows and data flows. Software defined networking enables centralized control to optimize flow scheduling. We propose an SD-EHN architecture for 5G green communications. In SD-EHN, the data plane, the energy plane, and the control plane are decoupled to support flexible energy scheduling and improve energy efficiency, thus facilitating sustainability in energy harvesting networks. A scenario with a mobile charger acting as a mobile data collector is presented to introduce an energy trading model in SD-EHN. We use stochastic inventory theory to determine the optimal energy storage levels of the nodes. A Nash bargaining game is proposed to solve the benefit allocation problem for energy trading. Numerical results indicate that SD-EHN optimizes energy utilization and saves energy.

A Hierarchical Pseudonyms Management Approach for Software-Defined Vehicular Networks

Cloud-enabled vehicular network is an emerging paradigm which utilizes cloud computing to enhance the performance of vehicular network. But some issues still need to be addressed and we focus on the pseudonym resources management, which is crucial for vehicles to guarantee location privacy. A new three-plane hierarchical architecture with software defined network technology is proposed to manage the pseudonym resources. We use two-sided matching theory to solve the pseudonym resources allocation problem among pseudonym pools in different roadside unit clouds. Numerical results show that our proposed approach optimizes the pseudonym resources utilization and also improves the privacy entropy of vehicles.

Optimal roadside unit placement with location privacy enhancement in vehicular social network

Vehicular social network is envisioned to serve as an essential data sensing, exchanging and processing platform for the future intelligent transportation systems. However, large-scale and concurrent appearing vehicles bring new challenge to existing location privacy protection schemes in vehicular social networks. These schemes only enable privacy protection but seldom considering quality of privacy (QoP). In this paper, we propose an optimal roadside infrastructure placement scheme with location privacy enhancement. This scheme takes load balance ratio as the parameter of QoP, and solves the load unbalance problem in vehicular social networks. Simulation results illustrate that the proposed scheme achieves higher privacy preservation and better performance in load balance.

Distributed Reputation Management for Secure and Efficient Vehicular Edge Computing and Networks

Vehicular edge computing (VEC) is introduced to extend computing capacity to vehicular network edge recently. With the advent of VEC, service providers directly host services in close proximity of mobile vehicles for great improvements. As a result, a new networking paradigm, vehicular edge networks is emerged along with the development of VEC. However, it is necessary to address security issues for facilitating VEC well. In this paper, we focus on reputation management to ensure security protection and improve network efficiency in the implementation of VEC. A distributed reputation management system (DREAMS) is proposed, wherein VEC servers are adopted to execute local reputation management tasks for vehicles. This system has remarkable features for improving overall performance: 1) distributed reputation maintenance; 2) trusted reputation manifestation; 3) accurate reputation update; and 4) available reputation usage. In particular, we utilize multi-weighted subjective logic for accurate reputation update in DREAMS. To enrich reputation usage in DREAMS, service providers optimize resource allocation in computation offloading by considering reputation of vehicles. Numerical results indicate that DREAMS has great advantages in optimizing misbehavior detection and improving the recognition rate of misbehaving vehicles. Meanwhile, we demonstrate the effectiveness of our reputation-based resource allocation algorithm.