Disseminating shared information to many vehicles could incur significant access fees if it relies only on unicast cellular communications. We consider the problem of efficient content dissemination over a vehicular network, in which vehicles are equipped with two kinds of radios: a high-cost low-bandwidth, long-range cellular radio, and a free high-bandwidth short-range radio. We formulate and solve an optimization problem to maximize content dissemination from the infrastructure to vehicles within a predetermined deadline while minimizing the cost associated with communicating over the cellular connection. We examine numerically the tradeoffs between cost, delay and system utility in the optimum regime. We find that, in the optimum regime, (a) system utility is more sensitive to the cost budget when the allowed delay for the dissemination is not large, (b) the system requires relatively smaller cost budget as more vehicles participate and more delay is allowed, (c) when the cost is very important, it is better not to spread the content if it needs small delay. We also develop a polynomial-time algorithm to obtain the optimal discrete solution needed in practice. Finally, we verify our analysis using real GPS traces of 632 taxis in Beijing, China.

With the advent of the new IEEE 802.11p DSRC/WAVE radios, Vehicle-to-Vehicle (V2V) communications is poised for a dramatic leap. A canonical application for these future vehicular networks is the detection and notification of anomalous road events (e.g., potholes, bumps, icy road patches, etc.). We present the Road Information Sharing Architecture (RISA), the first distributed approach to road condition detection and dissemination for vehicular networks. RISA provides for the in-network aggregation and dissemination of event information detected by multiple vehicles in a timely manner for improved information reliability and bandwidth efficiency. RISA uses a novel Time-Decay Sequential Hypothesis Testing (TD-SHT) approach in which event information from multiple sources is combined with time-varying beliefs. We describe our implementation of RISA which has been deployed and tested on a fleet of vehicles on-site at the GM Warren Technical Center in Michigan. We further provide a comprehensive evaluation of the aggregation mechanism using emulation of the RISA code on real vehicular mobility traces.

We use a constrained optimization framework to derive fundamental scaling laws for both unstructured sensor networks (which use blind sequential search for querying) and structured sensor networks (which use efficient hash-based querying). We find that the scalability of a sensor network's performance depends upon whether or not the increase in energy and storage resources with more nodes is outweighed by the concomitant application-specific increase in event and query loads. Let m be the number of events sensed by a network over a finite period of deployment, q the number of queries for each event, and N the size of the network. Our key finding is that q1/2•m must be O(N1/4)for unstructured net-works, and q2/3•m must be O(N1/2)for structured networks, to ensure scalable network performance. These conditions determine (i) whether or not the energy requirement per node grows without bound with the network size for a fixed-duration deployment, (ii) whether or not there exists a maximum network size that can be operated for a specified duration on a fixed energy budget, and (iii) whether the network lifetime increases or decreases with the size of the network for a fixed energy budget. We discuss the practical implications of these results for the design of hierarchical two-tier wireless sensor networks.

The scalability of a wireless sensor network has been of interest and importance. We use a constrained optimization framework to derive fundamental scaling laws for both unstructured sensor networks (which use blind sequential search for querying) and structured sensor networks (which use efficient hash-based querying). We find that the scalability of a sensor network's performance depends upon whether or not the increase in energy and storage resources with more nodes is outweighed by the concomitant application-specific increase in event and query loads. We have figured out the theoretical scaling laws for the networks of 2 dimensional deployment in our previous work [2]. We report on our work-in-progress aimed at extending the scaling laws to networks of various dimensional deployment. As a recent achievement, we find that m⋅q1/2 must be O(N d?1/2d) for unstructured networks, and m⋅q d/d+1 must be O(N d?1/d) for structured networks, to ensure scalable network performance, where m is the number of events sensed by a network over a finite period of deployment, q is the number of queries for each event, d is the dimension of deployment, and N is the size of the network. These conditions determine (i) whether or not the energy requirement per node grows without bound with the network size for a fixed-duration deployment, (ii) whether or not there exists a maximum network size that can be operated for a specified duration on a fixed energy budget, and (iii) whether the network lifetime increases or decreases with the size of the network for a fixed energy budget. An interesting finding of this extension is that 3D uniform deployments are inherently more scalable than 2D uniform deployments, which in turn are more scalable than 1D uniform deployments.

We consider the problem of optimizing the number of replicas for event information in wireless sensor networks, when queries are disseminated using expanding rings. We obtain closed-form approximations for the expected energy costs of search, as well as replication. Using these expressions we derive the replication strategies that minimize the expected total energy cost consisting of search and replication costs, both with and without storage constraints. In both cases, we find that events should be replicated with a frequency that is proportional to the square root of their query rates. We validate our analysis and optimization through a set of realistic simulations that incorporate non-idealities including deployment boundary effects and lossy wireless links.

This paper presents a model-based matching technique for recognition of articulated objects (with two parts) and the poses of these parts in synthetic aperture radar (SAR) images. Using articulation invariants as features, the recognition system first hypothesizes the pose of the larger part and then the pose of the smaller part. Geometric reasoning is carried out to correct identification errors. The thresholds for the quality of match are determined dynamically by minimizing the probability of a random match. Results are presented using SAR images of three articulated objects. The system performance is evaluated with respect to identification performance, accuracy of estimates for the poses of the object parts and noise.