Guangtao Xue

Recognizing Exponential Inter-Contact Time in VANETs

Inter-contact time between moving vehicles is one of the key metrics in vehicular ad hoc networks (VANETs) and central to forwarding algorithms and the end-to-end delay. Due to prohibitive costs, little work has conducted experimental study on inter-contact time in urban vehicular environments. In this paper, we carry out an extensive experiment involving thousands of operational taxies in Shanghai city. Studying the taxi trace data on the frequency and duration of transfer opportunities between taxies, we observe that the tail distribution of the inter-contact time, that is the time gap separating two contacts of the same pair of taxies, exhibits a light tail such as one of an exponential distribution, over a large range of timescale. This observation is in sharp contrast to recent empirical data studies based on human mobility, in which the distribution of the inter-contact time obeys a power law. By performing a least squares fit, we establish an exponential model that can accurately depict the tail behavior of the inter-contact time in VANETs. Our results thus provide fundamental guidelines on design of new vehicular mobility models in urban scenarios, new data forwarding protocols and their performance analysis.

Impact of Traffic Influxes: Revealing Exponential Intercontact Time in Urban VANETs

Intercontact time between moving vehicles is one of the key metrics in vehicular ad hoc networks (VANETs) and central to forwarding algorithms and the end-to-end delay. Due to prohibitive costs, little work has conducted experimental study on intercontact time in urban vehicular environments. In this paper, we carry out an extensive experiment involving thousands of operational taxies in Shanghai city. Studying the taxi trace data on the frequency and duration of transfer opportunities between taxies, we observe that the tail distribution of the intercontact time, that is, the time gap separating two contacts of the same pair of taxies, exhibits an exponential decay, over a large range of timescale. This observation is in sharp contrast to recent empirical data studies based on human mobility, in which the distribution of the intercontact time obeys a power law. By analyzing a simplified mobility model that captures the effect of hot areas in the city, we rigorously prove that common traffic influxes, where large volume of traffic converges, play a major role in generating the exponential tail of the intercontact time. Our results thus provide fundamental guidelines on design of new vehicular mobility models in urban scenarios, new data forwarding protocols and their performance analysis.

Toward Secure Multikeyword Top-k Retrieval over Encrypted Cloud Data

Cloud computing has emerging as a promising pattern for data outsourcing and high-quality data services. However, concerns of sensitive information on cloud potentially causes privacy problems. Data encryption protects data security to some extent, but at the cost of compromised efficiency. Searchable symmetric encryption (SSE) allows retrieval of encrypted data over cloud. In this paper, we focus on addressing data privacy issues using SSE. For the first time, we formulate the privacy issue from the aspect of similarity relevance and scheme robustness. We observe that server-side ranking based on order-preserving encryption (OPE) inevitably leaks data privacy. To eliminate the leakage, we propose a two-round searchable encryption (TRSE) scheme that supports top-k multikeyword retrieval. In TRSE, we employ a vector space model and homomorphic encryption. The vector space model helps to provide sufficient search accuracy, and the homomorphic encryption enables users to involve in the ranking while the majority of computing work is done on the server side by operations only on ciphertext. As a result, information leakage can be eliminated and data security is ensured. Thorough security and performance analysis show that the proposed scheme guarantees high security and practical efficiency.

A Reliability-oriented Transmission Service in Wireless Sensor Networks

Reliable communications are essential for most applications in wireless sensor networks (WSNs). In traditional approaches, the per-hop and end-to-end (E2E) recovery schemes are widely used. These schemes, however, suffer from low E2E success rate and poor energy efficiency in large-scale real environments. Through empirical studies, in this paper we identify three major problems that hinder the efficient and reliable communications. To address these problems, we propose a novel in-middle recovery scheme and realize it by designing and implementing a proliferation routing. Proliferation routing integrates three core technologies, namely, capability-based path finder, a randomized dispersity, and reproduction. Proliferation routing offers great flexibilities for transmissions. It cannot only be applied with any Medium Access Control (MAC) protocols and routing metrics, but also obtains a desired service quality (i.e., transmission success rate, energy cost, etc.) by controlling the system parameters. To demonstrate the effectiveness of proliferation routing, we thoroughly analyze its performance. We also conduct performance evaluations through implementation experiments as well as simulations. In a specific experimental setup, proliferation routing can increase the E2E transmission success rate up to 80 percent compared with the well-known hop-based routing and flooding.

Traffic-Known Urban Vehicular Route Prediction Based on Partial Mobility Patterns

Travel route analysis and prediction are essential for the success of many applications in Vehicular Ad-hoc Networks (VANETs). Yet it is quit challenging to make accuracy route prediction for general vehicles in urban settings due to several practical issues such as very complicated traffic networks, the highly dynamic real-time traffic conditions and their interaction with drivers’ route selections. In this paper, we undertake a systematic study on the vehicular route prediction in urban environments where the traffic conditions on complicated road networks keep changing from time to time. Inspired by the observation that a vehicle often has its own route selection flavor when traversing between its sources and destinations, we define a mobility pattern as a consecutive series of road segment selections that exhibit frequent appearance along all the itineraries of the vehicle. We further leverage Variable-order Markov Models (VMMs) to mine mobility patterns from the real taxi GPS trace data collected in Shanghai. In addition, considering the tremendous impact of dynamic traffic conditions to the accuracy of route prediction, we deploy multiple VMMs differentiating different traffic conditions in daytime. Our extensive trace-driven simulation results show that notable patterns can be mined from routes of common vehicles though they usually have no constraints when selecting routes. Given a specific taxi, around 40% next road segments are predictable using our model with a confidence weight of 60%. With multiple VMMs a high route prediction accuracy is achievable from the real traffic trace.

A novel vehicular location prediction based on mobility patterns for routing in urban VANET

Location information is crucial for most applications and protocol designs in high-speed vehicular ad-hoc networks (VANETs). In traditional approaches, this is obtained by object tracking techniques that keep tracking the objects and publish the information to the users. In highly dynamic environments, however, these approaches are not efficient as the target objects in VANETs are typically vehicles that present high mobility. Their locations keep changing in a large range so that the tracking and information publication algorithms have to be frequently invoked to obtain the instant locations of the objects. To deal with this problem, we propose a novel approach based on the observation that in high-speed VANET environment, the target objects are strictly constrained by the road network. Their mobilities are well patterned and many patterns can clearly be identified. These patterns can smartly be leveraged so that a large amount of control overhead can be saved. Towards this end, in this article we adopt Variable-order Markov model to abstract Vehicular Mobility Pattern (VMP) from the real trace data in Shanghai. We leverage VMP for predicting the possible trajectories of moving vehicles which help to keep the timely effectiveness of the evolutional location information. To reveal the benefits of VMP, we propose a Prediction-based Soft Routing Protocol (PSR), taking VMP as an advantage. The experimental results show that PSR significantly outperforms existing solutions in terms of control packet overhead, packet delivery ratio, packet delivery delay. In certain scenarios, the control packet overhead can be saved by up to 90% compared with DSR, and 75% compared with WSR.

POST: Exploiting dynamic sociality for mobile advertising in vehicular networks

Mobile advertising in vehicular networks is of great interest with which timely information can be fast spread into the network. Given a limited budget for hiring seed vehicles, how to achieve the maximum advertising coverage within a given period of time is NP-hard. In this paper, we propose an innovative scheme, POST, for mobile advertising in vehicular networks. The POST design is based on two key observations we have found by analyzing three large-scale vehicular traces. First, vehicles demonstrate dynamic sociality in the network; second, such vehicular sociality has strong temporal correlations. With the knowledge, POST uses Markov chains to infer future vehicular sociality and adopts two greedy heuristics to select the most “centric” vehicles as seeds for mobile advertising. Extensive simulations based on three real data sets of taxi and bus traces have been carried out. The results show that POSTcan greatly improve the coverage and the intensity of advertising. For all the three involved data sets, it achieves an average gain of 64 percent comparing with the state-of-art schemes.

Robust network compressive sensing

Networks are constantly generating an enormous amount of rich diverse information. Such information creates exciting opportunities for network analytics. However, a major challenge to enable effective network analytics is the presence of missing data, measurement errors, and anomalies. Despite significant work in network analytics, fundamental issues remain: (i) the existing works do not explicitly account for anomalies or measurement noise, and incur serious performance degradation under significant noise or anomalies, and (ii) they assume network matrices have low-rank structure, which may not hold in reality. To address these issues, in this paper we develop LENS decomposition, a novel technique to accurately decompose a network matrix into a low-rank matrix, a sparse anomaly matrix, an error matrix, and a small noise matrix. LENS has the following nice properties: (i) it is general: it can effectively support matrices with or without anomalies, and having low-rank or not, (ii) its parameters are self tuned so that it can adapt to different types of data, (iii) it is accurate by incorporating domain knowledge, such as temporal locality, spatial locality, and initial estimate (e.g., obtained from models), (iv) it is versatile and can support many applications including missing value interpolation, prediction, and anomaly detection. We apply LENS to a wide range of network matrices from 3G, WiFi, mesh, sensor networks, and the Internet. Our results show that LENS significantly out-performs state-of-the-art compressive sensing schemes.

E 3 : energy-efficient engine for frame rate adaptation on smartphones

Touch-screen technique has gained the large popularity in human-screen interaction with modern smartphones. Due to the limited size of equipped screens, scrolling operations are indispensable in order to display the content of interest on screen. While power consumption caused by hardware and software installed within smartphones is well studied, the energy cost made by human-screen interaction such as scrolling remains unknown. In this paper, we analyze the impact of scrolling operations to the power consumption of smartphones, finding that the state-of-art strategy of smartphones in responding a scrolling operation is to always use the highest frame rate which arouses huge computation burden and can contribute nearly 50% to the total power consumption of smartphones. In recognizing this significance, we further propose a novel system, Energy-Efficient Engine(E3), which automatically tracks the scrolling speed and adaptively adjusts the frame rate according to individual user preference. The goal of E3 is to guarantee the user experience and minimize the energy consumption caused by scrolling at the same time. Extensive experiment results demonstrate the efficiency of E3 design. On average, E3 can save up to 58% of the energy consumed by CPU and 34% of the overall energy consumption.

A topology-aware hierarchical structured overlay network based on locality sensitive hashing scheme

The DHT scheme without any knowledge about underlying physical topology could cause a serious topology mismatching between the P2P overlay network and the physical underlying network. In this paper, a new mechanism, TSO, is proposed for constructing the two layer topology-aware structured overlay network based on locality sensitive hashing scheme. In TSO, the physical close nodes have been clustered into a local level P2P ring which is regarded as a virtual node in the high level Chord ring of the overall P2P overlay network. A large portion of routing hops previously executed in the global P2P ring are now replaced by hops in local level rings, thus routing overheads can be reduced. The intensive simulation experiments, we have shown the effectiveness of TSO.