Tom H. Luan

Pseudonym Changing at Social Spots: An Effective Strategy for Location Privacy in VANETs

As a prime target of the quality of privacy in vehicular ad hoc networks (VANETs), location privacy is imperative for VANETs to fully flourish. Although frequent pseudonym changing provides a promising solution for location privacy in VANETs, if the pseudonyms are changed in an improper time or location, such a solution may become invalid. To cope with the issue, in this paper, we present an effective pseudonym changing at social spots (PCS) strategy to achieve the provable location privacy. In particular, we first introduce the social spots where several vehicles may gather, e.g., a road intersection when the traffic light turns red or a free parking lot near a shopping mall. By taking the anonymity set size as the location privacy metric, we then develop two anonymity set analytic models to quantitatively investigate the location privacy that is achieved by the PCS strategy. In addition, we use game-theoretic techniques to prove the feasibility of the PCS strategy in practice. Extensive performance evaluations are conducted to demonstrate that better location privacy can be achieved when a vehicle changes its pseudonyms at some highly social spots and that the proposed PCS strategy can assist vehicles to intelligently change their pseudonyms at the right moment and place.

Enabling Fine-Grained Multi-Keyword Search Supporting Classified Sub-Dictionaries over Encrypted Cloud Data

Using cloud computing, individuals can store their data on remote servers and allow data access to public users through the cloud servers. As the outsourced data are likely to contain sensitive privacy information, they are typically encrypted before uploaded to the cloud. This, however, significantly limits the usability of outsourced data due to the difficulty of searching over the encrypted data. In this paper, we address this issue by developing the fine-grained multi-keyword search schemes over encrypted cloud data. Our original contributions are three-fold. First, we introduce the relevance scores and preference factors upon keywords which enable the precise keyword search and personalized user experience. Second, we develop a practical and very efficient multi-keyword search scheme. The proposed scheme can support complicated logic search the mixed “AND”, “OR” and “NO” operations of keywords. Third, we further employ the classified sub-dictionaries technique to achieve better efficiency on index building, trapdoor generating and query. Lastly, we analyze the security of the proposed schemes in terms of confidentiality of documents, privacy protection of index and trapdoor, and unlinkability of trapdoor. Through extensive experiments using the real-world dataset, we validate the performance of the proposed schemes. Both the security analysis and experimental results demonstrate that the proposed schemes can achieve the same security level comparing to the existing ones and better performance in terms of functionality, query complexity and efficiency.

MAC in Motion: Impact of Mobility on the MAC of Drive-Thru Internet

The pervasive adoption of IEEE 802.11 radios in the past decade has made possible for the easy Internet access from a vehicle, notably drive-thru Internet. Originally designed for the static indoor applications, the throughput performance of IEEE 802.11 in the outdoor vehicular environment is, however, still unclear especially when a large number of fast-moving users transmitting simultaneously. In this paper, we investigate the performance of IEEE 802.11 DCF in the highly mobile vehicular networks. We first propose a simple yet accurate analytical model to evaluate the throughput of DCF in the large scale drive-thru Internet scenario. Our model incorporates the high-node mobility with the modeling of DCF and unveils the impacts of mobility (characterized by node velocity and moving directions) on the resultant throughput. Based on the model, we show that the throughput of DCF will be reduced with increasing node velocity due to the mismatch between the MAC and the transient high-throughput connectivity of vehicles. We then propose several enhancement schemes to adaptively adjust the MAC in tune with the node mobility. Extensive simulations are carried out to validate the accuracy of the developed analytical model and the effectiveness of the proposed enhancement schemes.

Impact of Network Dynamics on User's Video Quality: Analytical Framework and QoS Provision

We develop an analytical framework to investigate the impacts of network dynamics on the user perceived video quality. Our investigation stands from the end users perspective by analyzing the receiver playout buffer. In specific, we model the playback buffer at the receiver by a G/G/1/¿ and G/G/1/N queue, respectively, with arbitrary patterns of packet arrival and playback. We then examine the transient queue length of the buffer using the diffusion approximation. We obtain the closed-form expressions of the video quality in terms of the start-up delay, fluency of video playback and packet loss, and represent them by the network statistics, i.e., the average network throughput and delay jitter. Based on the analytical framework, we propose adaptive playout buffer management schemes to optimally manage the threshold of video playback towards the maximal user utility, according to different quality-of-service requirements of end users. The proposed framework is validated by extensive simulations.

Engineering searchable encryption of mobile cloud networks: when QoE meets QoP

Mobile cloud computing can effectively address the resource limitations of mobile devices, and is therefore essential to enable extensive resource consuming mobile computing and communication applications. Of all the mobile cloud computing applications, data outsourcing, such as iCloud, is fundamental, which outsources a mobile users data to external cloud servers and accordingly provides a scalable and “always on” approach for public data access. With the security and privacy issues related to outsourced data becoming a rising concern, encryption on outsourced data is often necessary. Although encryption increases the quality of protection (QoP) of data outsourcing, it significantly reduces data usability and thus harms the mobile users quality of experience (QoE). How to strike a balance between QoP and QoE is therefore an important yet challenging task. In this article we focus on the fundamental problem of QoP and QoE provisioning in searchable encryption of data outsourcing. We develop a fine-grained data search scheme and discuss its implementation on encrypted mobile cloud data, which is an effective balance between QoE and QoP in mobile cloud data outsourcing.

Dimensioning network deployment and resource management in green mesh networks

In this article, network deployment and resource management issues are revisited in the context of green radio communication networks with sustainable energy supply. It is argued that under the green network paradigm powered by renewable energy, the fundamental design criterion and main performance metric have shifted from energy efficiency to energy sustainability. As an effort to this end, in this article, new network solutions are proposed with an objective of improving network sustainability; the proposed solutions ensure that dynamically harvested energy can sustain the traffic demands in the network. Specifically, the placement issue of green access points (i.e., APs powered by sustainable energy sources) is investigated to meet the energy and QoS demands of mobile users; and an adaptive resource management scheme is proposed to address the unreliability of renewable energy in QoS provisioning. It is shown that by mitigating the energy depletion probability of green APs, sustainable network performance can be significantly improved.

Optimal Workload Allocation in Fog-Cloud Computing Toward Balanced Delay and Power Consumption

Mobile users typically have high demand on localized and location-based information services. To always retrieve the localized data from the remote cloud, however, tends to be inefficient, which motivates fog computing. The fog computing, also known as edge computing, extends cloud computing by deploying localized computing facilities at the premise of users, which prestores cloud data and distributes to mobile users with fast-rate local connections. As such, fog computing introduces an intermediate fog layer between mobile users and cloud, and complements cloud computing toward low-latency high-rate services to mobile users. In this fundamental framework, it is important to study the interplay and cooperation between the edge (fog) and the core (cloud). In this paper, the tradeoff between power consumption and transmission delay in the fog-cloud computing system is investigated. We formulate a workload allocation problem which suggests the optimal workload allocations between fog and cloud toward the minimal power consumption with the constrained service delay. The problem is then tackled using an approximate approach by decomposing the primal problem into three subproblems of corresponding subsystems, which can be, respectively, solved. Finally, based on simulations and numerical results, we show that by sacrificing modest computation resources to save communication bandwidth and reduce transmission latency, fog computing can significantly improve the performance of cloud computing.

Provisioning QoS controlled media access in vehicular to infrastructure communications

The emerging IEEE 802.11p standard adopts the enhanced distributed channel access (EDCA) mechanism as its Media Access Control (MAC) scheme to support quality-of-service (QoS) in the rapidly changing vehicular environment. While the IEEE 802.11 protocol family represents the dominant solutions for wireless local area networks, its QoS performance in terms of throughput and delay, in the highly mobile vehicular networks, is still unclear. To explore an in-depth understanding on this issue, in this paper, we develop a comprehensive analytical model that takes into account both the QoS features of EDCA and the vehicle mobility (velocity and moving directions). Based on the model, we analyze the throughput performance and mean transmission delay of differentiated service traffic, and seek solutions to optimally adjust the parameters of EDCA towards the controllable QoS provision to vehicles. Analytical and simulation results are given to demonstrate the accuracy of the proposed model for varying EDCA parameters and vehicle velocity and density.

Engineering a Distributed Infrastructure for Large-Scale Cost-Effective Content Dissemination over Urban Vehicular Networks

This paper proposes a practical and cost-effective approach to construct a fully distributed roadside communication infrastructure to facilitate the localized content dissemination to vehicles in the urban area. The proposed infrastructure is composed of distributed lightweight low-cost devices called roadside buffers (RSBs), where each RSB has the limited buffer storage and is able to transmit wirelessly the cached contents to fast-moving vehicles. To enable the distributed RSBs working toward the global optimal performance (e.g., minimal average file download delays), we propose a fully distributed algorithm to determine optimally the content replication strategy at RSBs. Specifically, we first develop a generic analytical model to evaluate the download delay of files, given the probability density of file distribution at RSBs. Then, we formulate the RSB content replication process as an optimization problem and devise a fully distributed content replication scheme accordingly to enable vehicles to recommend intelligently the desirable content files to RSBs. The proposed infrastructure is designed to optimize the global network utility, which accounts for the integrated download experience of users and the download demands of files. Using extensive simulations, we validate the effectiveness of the proposed infrastructure and show that the proposed distributed protocol can approach to the optimal performance and can significantly outperform the traditional heuristics.

Enabling Efficient Multi-Keyword Ranked Search Over Encrypted Mobile Cloud Data Through Blind Storage

In mobile cloud computing, a fundamental application is to outsource the mobile data to external cloud servers for scalable data storage. The outsourced data, however, need to be encrypted due to the privacy and confidentiality concerns of their owner. This results in the distinguished difficulties on the accurate search over the encrypted mobile cloud data. To tackle this issue, in this paper, we develop the searchable encryption for multi-keyword ranked search over the storage data. Specifically, by considering the large number of outsourced documents (data) in the cloud, we utilize the relevance score and k-nearest neighbor techniques to develop an efficient multi-keyword search scheme that can return the ranked search results based on the accuracy. Within this framework, we leverage an efficient index to further improve the search efficiency, and adopt the blind storage system to conceal access pattern of the search user. Security analysis demonstrates that our scheme can achieve confidentiality of documents and index, trapdoor privacy, trapdoor unlinkability, and concealing access pattern of the search user. Finally, using extensive simulations, we show that our proposal can achieve much improved efficiency in terms of search functionality and search time compared with the existing proposals.