With increasing popularity of media-enabled handhelds and their integration with the in-vehicle entertainment systems, the need for high-data-rate services for mobile users on the go is evident. This ever-increasing demand of data is constantly surpassing what cellular networks can economically support. Large-scale wireless local area networks (WLANs) can provide such a service, but they are expensive to deploy and maintain. Open WLAN access points, on the other hand, need no new deployments, but can offer only opportunistic services, lacking any performance guarantees. In contrast, a carefully planned sparse deployment of roadside WiFi provides an economically scalable infrastructure with quality-of-service assurance to mobile users. In this paper, we present a new metric, called Contact Opportunity, to closely model the quality of data service that a mobile user might experience when driving through the system. We then present efficient deployment algorithms for minimizing the cost for ensuring a required level of contact opportunity. We further extend this concept and the deployment techniques to a more intuitive metric-the average throughput-by taking various dynamic elements into account. Simulations over a real road network and experimental results show that our approach achieves significantly better cost versus throughput tradeoff in both the worst case and average case compared to some commonly used deployment algorithms.

Using broadcast in vehicular applications such as autonomous cruise control and collaborative driving can disturb unrelated drivers and fail to convey the message due to unspecified receiver, resulting in increased risk of accidents. For supporting the unicast communication primitive, it is important to know the electronic identities (EIDs), e.g., the IP addresses and the relative positions of the nearby vehicles. We show that the estimated GPS coordinates alone are not accurate enough to uniquely identify the intended vehicle. On the other hand, there is an increasing array of devices, such as on-board camera, RADAR, and DSRC radio that are becoming available in newer vehicles. These heterogeneously deployed devices provide information sources that have varying levels of accuracy and potentially different coverage regions, making it challenging to accurately identify the vehicle. As a first step, we design ForeSight, a system that dynamically integrates the information observed in the visual domain (e.g., from camera) and the electronic domain (e.g., WiFi radio) to match the vehicles observed in these two domains with high accuracy. The experiment and simulation results show that ForeSight is able to significantly improve the vehicle identification accuracy compared to using GPS or other algorithms. In our case study, ForeSight reduces disturbing messages by 14 × as compared to the number of a GPS-based communication method.

Data offloading is an increasingly popular mechanism for meeting the rising demands of cellular users. In order to enable the small players, such as businesses and individual owners to make their services available to the bigger wireless service providers (WSPs) to help offload data, a simple, practical and easy-to-use payment machinery needs to be devised. Existing auction mechanisms usually assume that bidders can precisely estimate their true valuations, and they ignore the significant overhead to sellers incurred for obtaining a precise estimation. Such assumption is unrealistic in femtocell networks. To allow imprecise valuations, we introduce the novel concept of perceived valuation, which is a value that can be acquired by the seller at little or no cost. We further propose two novel metrics: partial truthfulness, and imprecision loss, to measure the quality of a truthful auction that accepts perceived valuations. Based on this, we propose EasyBid, a new auction model that provides guarantees for truthfulness even when considering a system with imprecise valuations. Finally, we design a dynamic programming based algorithm which aims to maximize the WSP's utility while satisfying any given constraints on partial truthfulness and imprecision loss. Through simulations, we show that the utility achieved by EasyBid with imprecise valuations can be close to the optimal solution that assumes precise valuations.

Femtocell-based architectures have the potential to position the cellular service providers to compete head-on with the WiFi market. However, significant interference can happen due to unplanned deployments. Current use of hard partitioning approaches for resource allocation, and lack of guidelines for configuring the femtocells, makes it difficult to obtain significant performance gains over traditional cellular networks. In this paper, we study the dynamic OFDMA subchannel assignment problem while jointly considering power assignment and association control to provide maxmin fairness. Toward this objective, we first consider a noninterfering model (NINT model), which disallows interfering femtocells in the solution. A more general interfering model (INT model) is then considered under which the problem is transformed into the partition coloring problem. We then show the NP-hardness of the problems and design centralized approximation algorithms with provable bounds and distributed solutions. Through extensive simulations in realistic settings we show that, compared to previous work, our solutions under the NINT model can achieve two times the minimum throughput, and under the INT model the minimum throughput can be up to three times the baseline algorithms.

Access to relative location of nearby vehicles on the local roads or on the freeways is useful for providing critical alerts to the drivers, thereby enhancing their driving experience as well as reducing the chances of accidents. The problem of determining the relative location of two vehicles can be broken into two smaller subproblems: (i) Relative lane localization, where a vehicle determines if the other vehicle is in left lane, same lane or right lane with respect to it, and (ii) Relative front-rear localization where it needs to be determined which of the two vehicles is ahead of the other on the road. In this paper, we propose a novel antenna diversity based solution, MARVEL, that solves the two problems of determining the relative location of two vehicles. MARVEL has two components: (i) a smartphone; and (ii) four wireless radios. Unlike exisiting technologies, MARVEL can also determine relative location of vehicles that are not in the immediate neighborhood, thereby providing the driver with more time to react. Further, due to minimal hardware requirements, the deployment cost of MARVEL is low and it can be easily installed on newer as well as existing vehicles. Using results from our real driving tests, we show that MARVEL is able to determine the relative lane location of two vehicles with 96.8% accuracy. Through trace-driven simulations, we also show that by aggregating information across different vehicles, MARVEL is able to increase the localization accuracy to 98%.

Access to relative location of nearby vehicles on the local roads or on the freeways is useful for providing critical alerts to the drivers, thereby enhancing their driving experience as well as reducing the chances of accidents. The problem of determining the relative location of two vehicles can be broken into two smaller subproblems: (i) Relative lane localization, where a vehicle determines if the other vehicle is in left lane, same lane or right lane with respect to it, and (ii) Relative front-rear localization where it needs to be determined which of the two vehicles is ahead of the other on the road. In this demo, we show a novel antenna diversity based solution, MARVEL, that solves the problem of relative lane localization.

With increasing popularity of media enabled hand-helds, the need for high data-rate services for mobile users is evident. Large-scale Wireless LANs (WLANs) can provide such a service, but they are expensive to deploy and maintain. Open WLAN access-points (APs), on the other hand, need no new deployments, but can offer only opportunistic services with no guarantees on short term throughput. In contrast, a carefully planned sparse deployment of roadside WiFi provides an economically scalable infrastructure with quality of service assurance to mobile users. In this paper, we propose to study the deployment techniques with respect to roadside WiFi. In particular, we present a new metric, called Contact Opportunity, as a characterization of a roadside WiFi network. Informally, the contact opportunity for a given deployment measures the fraction of distance or time that a mobile user is in contact with some AP when moving through a certain path. Such a metric is closely related to the quality of data service that a mobile user might experience while driving through the system. We then present an efficient deployment method that maximizes the worst case contact opportunity under a budget constraint. We further show how to extend this concept and the deployment techniques to a more intuitive metric -- the average throughput -- by taking various dynamic elements into account. Simulations over a real road network and experimental results show that our approach achieves more than 200% higher minimum contact opportunity, 30%-100% higher average contact opportunity and a significantly improved distribution of average throughput compared with two commonly used baseline algorithms.