The vehicular announcement network is one of the most promising utilities in the communications of smart vehicles and in the smart transportation systems. In general, there are two major issues in building an effective vehicular announcement network. First, it is difficult to forward reliable announcements without revealing users’ identities. Second, users usually lack the motivation to forward announcements. In this paper, we endeavor to resolve these two issues through proposing an effective announcement network called CreditCoin , a novel privacy-preserving incentive announcement network based on Blockchain via an efficient anonymous vehicular announcement aggregation protocol. On the one hand, CreditCoin allows nondeterministic different signers (i.e., users) to generate the signatures and to send announcements anonymously in the nonfully trusted environment. On the other hand, with Blockchain, CreditCoin motivates users with incentives to share traffic information. In addition, transactions and account information in CreditCoin are tamper-resistant. CreditCoin also achieves conditional privacy since Trace manager in CreditCoin traces malicious users’ identities in anonymous announcements with related transactions. CreditCoin thus is able to motivate users to forward announcements anonymously and reliably. Extensive experimental results show that CreditCoin is efficient and practical in simulations of smart transportation.

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CreditCoin: A Privacy-Preserving Blockchain-Based Incentive Announcement Network for Communications of Smart Vehicles

Recent growth in the processing and memory resources of mobile devices has fueled research within the field of mobile virtualization. Mobile virtualization enables multiple persona on a single mobile device by hosting heterogeneous operating systems (OSs) concurrently. However, adding a virtualization layer to resource-constrained mobile devices with real-time requirements can lead to intolerable performance overheads. Hardware virtualization extensions that support efficient virtualization have been incorporated in recent mobile processors. Prior to hardware virtualization extensions, virtualization techniques that are enabled by performance prohibitive and resource consuming software were adopted for mobile devices. Moreover, mobile virtualization solutions lack standard procedures for device component sharing and interfacing between multiple OSSs. The objective of this article is to survey software- and hardware-based mobile virtualization techniques in light of the recent advancements fueled by the hardware support for mobile virtualization. Challenges and issues faced in virtualization of CPU, memory, I/O, interrupt, and network interfaces are highlighted. Moreover, various performance parameters are presented in a detailed comparative analysis to quantify the efficiency of mobile virtualization techniques and solutions.

A Survey of Mobile Device Virtualization: Taxonomy and State of the Art

Many cloud-based applications employ a data centers as a central server to process data that is generated by edge devices, such as smartphones, tablets and wearables. This model places ever increasing demands on communication and computational infrastructure with inevitable adverse effect on Quality-of-Service and Experience. The concept of Edge Computing is predicated on moving some of this computational load towards the edge of the network to harness computational capabilities that are currently untapped in edge nodes, such as base stations, routers and switches. This position paper considers the challenges and opportunities that arise out of this new direction in the computing landscape.

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Challenges and Opportunities in Edge Computing

An overview of virtual machine placement schemes in cloud computing

Point clouds obtained from 3D scans are typically sparse, irregular, and noisy, and required to be consolidated. In this paper, we present the first deep learning based edge-aware technique to facilitate the consolidation of point clouds. We design our network to process points grouped in local patches, and train it to learn and help consolidate points, deliberately for edges. To achieve this, we formulate a regression component to simultaneously recover 3D point coordinates and point-to-edge distances from upsampled features, and an edge-aware joint loss function to directly minimize distances from output points to 3D meshes and to edges. Compared with previous neural network based works, our consolidation is edge-aware. During the synthesis, our network can attend to the detected sharp edges and enable more accurate 3D reconstructions. Also, we trained our network on virtual scanned point clouds, demonstrated the performance of our method on both synthetic and real point clouds, presented various surface reconstruction results, and showed how our method outperforms the state-of-the-arts.

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EC-Net: An Edge-Aware Point Set Consolidation Network

In recent years researchers have made noticeable progresses in mesh denoising, that is, recovering high-quality 3D models from meshes corrupted with noise (raw or synthetic). Nevertheless, these state of the art approaches still fall short for robustly handling various noisy 3D models. The main technical challenge of robust mesh denoising is to remove noise while maximally preserving geometric features. In particular, this issue becomes more difficult for models with considerable amount of noise. In this paper we present a novel scheme for robust feature-preserving mesh denoising. Given a noisy mesh input, our method first estimates an initial mesh, then performs feature detection, identification and connection, and finally, iteratively updates vertex positions based on the constructed feature edges. Through many experiments, we show that our approach can robustly and effectively denoise various input mesh models with synthetic noise or raw scanned noise. The qualitative and quantitative comparisons between our method and the selected state of the art methods also show that our approach can noticeably outperform them in terms of both quality and robustness.

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A Robust Scheme for Feature-Preserving Mesh Denoising

Point set filtering, which aims at reconstructing noise-free point sets from their corresponding noisy inputs, is a fundamental problem in 3D geometry processing. The main challenge of point set filtering is to preserve geometric features of the underlying geometry while at the same time removing the noise. State-of-the-art point set filtering methods still struggle with this issue: some are not designed to recover sharp features, and others cannot well preserve geometric features, especially fine-scale features. In this paper, we propose a novel approach for robust feature-preserving point set filtering, inspired by the Gaussian Mixture Model (GMM). Taking a noisy point set and its filtered normals as input, our method can robustly reconstruct a high-quality point set which is both noise-free and feature-preserving. Various experiments show that our approach can soundly outperform the selected state-of-the-art methods, in terms of both filtering quality and reconstruction accuracy.

GPF: GMM-Inspired Feature-Preserving Point Set Filtering

Vehicular ad-hoc networks (VANETs) have drawn much attention of researchers. The vehicles in VANETs frequently join and leave the networks, and therefore restructure the network dynamically and automatically. Forwarded messages in vehicular ad-hoc networks are primarily multimedia data, including structured data, plain text, sound, and video, which require access control with efficient privacy preservation. Ciphertext-policy attribute-based encryption (CP-ABE) is adopted to meet the requirements. However, solutions based on traditional CP-ABE suffer from challenges of the limited computational resources on-board units equipped in the vehicles, especially for the complex policies of encryption and decryption. In this paper, we propose a CP-ABE delegation scheme, which allows road side units (RSUs) to perform most of the computation, for the purpose of improving the decryption efficiency of the vehicles. By using decision tree to jointly optimize multiple factors, such as the distance from RSU, the communication and computational cost, the CP-ABE delegation scheme is adaptively activated based on the estimation of various vehicles decryption overhead. Experimental results thoroughly demonstrate that our scheme is effective and efficient for multimedia data forwarding in vehicular ad-hoc networks with privacy preservation.

Wenzhi Chen

Papers

A Robust Scheme for Feature-Preserving Mesh Denoising

GPF: GMM-Inspired Feature-Preserving Point Set Filtering

Adaptive Multimedia Data Forwarding for Privacy Preservation in Vehicular Ad-Hoc Networks

Robust mesh denoising via vertex pre-filtering and L1-median normal filtering☆

SEMD: Secure and efficient message dissemination with policy enforcement in VANET☆