John Dillenburg

Bio: John Dillenburg is an academic researcher from University of Illinois at Chicago. The author has contributed to research in topics: Vehicular ad hoc network & Mobile ad hoc network. The author has an hindex of 8, co-authored 20 publications receiving 339 citations.

Cluster-Based framework in vehicular ad-hoc networks

Abstract: The application of Mobile Ad Hoc Network (MANET) technologies in the service of Intelligent Transportation Systems (ITS) has brought new challenges in maintaining communication clusters of network members for long time durations. Stable clustering methods reduce the overhead of communication relay in MANETs and provide for a more efficient hierarchical network topology. During creation of VANET clusters, each vehicle chooses a head vehicle to follow. The average number of cluster head changes per vehicle measures cluster stability in these simulations during the simulation. In this paper we analyze the effect of weighting two well-known clustering methods with the vehicle-specific position and velocity clustering logic to improve cluster stability over the simulation time.

Reliable route guidance: A case study from Chicago

Abstract: Reliable route guidance can be obtained by solving the reliable a priori shortest path problem, which finds paths that maximize the probability of arriving on time. The goal of this paper is to demonstrate the benefits and applicability of such route guidance using a case study. An adaptive discretization scheme is first proposed to improve the efficiency in computing convolution, a time-consuming step used in the reliable routing algorithm to obtain path travel time distributions. Methods to construct link travel time distributions from real data in the case study are then discussed. Particularly, the travel time distributions on arterial streets are estimated from linear regression models calibrated from expressway data. Numerical experiments demonstrate that optimal paths are substantially affected by the reliability requirement in rush hours, and that reliable route guidance could generate up to 5–15% of travel time savings. The study also verifies that existing algorithms can solve large-scale problems within a reasonable amount of time.

Traffic model for clustering algorithms in vehicular ad-hoc networks

Abstract: Inter-vehicle communication by means of wireless Ad Hoc networking has the potential to improve traffic safety and comfort tremendously. Therefore, the application of Vehicular Ad Hoc Networks (VANETs) in the service of Intelligent Transportation Systems (ITS) has been highly focused in recent years. Derived from the successful outcome of a cluster-based framework in Mobile Ad Hoc Networks (MANETs), we apply this network topology to VANETs. Unfortunately, previous studies lack realistic modeling of vehicle mobility and evaluation of clustering performance so they may not correlate well with performance in a real deployment. Hence, in this paper, we propose a realistic micro- simulation model with the hope of contributing to clustering research in VANETs, and demonstrate how clustering algorithms work on it.

Real Time Change Detection and Alerts from Highway Traffic Data

Abstract: We developed a testbed containing: real time data from over 830 highway traffic sensors in the Chicago region, data about weather, and text data about events that might affect traffic. The goal was to detect in real time interesting changes in traffic conditions. Given the size and complexity of the data, we choose to build a large number of separate baseline models. We built a separate baseline for each hour in the day, for each day in the week, and for every 2 or 3 traffic sensors, resulting in over 42,000 separate baseline models. We also built a baseline engine to build the necessary baselines automatically. We modified an open source scoring engine to process in real time each new sensor reading, update the appropriate feature vectors, score the updated feature vectors using the baseline models, and send out real time alerts when deviations from the baselines were detected.

Map-matching for low-sampling-rate GPS trajectories

Abstract: Map-matching is the process of aligning a sequence of observed user positions with the road network on a digital map. It is a fundamental pre-processing step for many applications, such as moving object management, traffic flow analysis, and driving directions. In practice there exists huge amount of low-sampling-rate (e.g., one point every 2--5 minutes) GPS trajectories. Unfortunately, most current map-matching approaches only deal with high-sampling-rate (typically one point every 10--30s) GPS data, and become less effective for low-sampling-rate points as the uncertainty in data increases. In this paper, we propose a novel global map-matching algorithm called ST-Matching for low-sampling-rate GPS trajectories. ST-Matching considers (1) the spatial geometric and topological structures of the road network and (2) the temporal/speed constraints of the trajectories. Based on spatio-temporal analysis, a candidate graph is constructed from which the best matching path sequence is identified. We compare ST-Matching with the incremental algorithm and Average-Frechet-Distance (AFD) based global map-matching algorithm. The experiments are performed both on synthetic and real dataset. The results show that our ST-matching algorithm significantly outperform incremental algorithm in terms of matching accuracy for low-sampling trajectories. Meanwhile, when compared with AFD-based global algorithm, ST-Matching also improves accuracy as well as running time.

A Comparative Survey of VANET Clustering Techniques

Abstract: A vehicular ad hoc network (VANET) is a mobile ad hoc network in which network nodes are vehicles—most commonly road vehicles. VANETs present a unique range of challenges and opportunities for routing protocols due to the semi-organized nature of vehicular movements subject to the constraints of road geometry and rules, and the obstacles which limit physical connectivity in urban environments. In particular, the problems of routing protocol reliability and scalability across large urban VANETs are currently the subject of intense research. Clustering can be used to improve routing scalability and reliability in VANETs, as it results in the distributed formation of hierarchical network structures by grouping vehicles together based on correlated spatial distribution and relative velocity. In addition to the benefits to routing, these groups can serve as the foundation for accident or congestion detection, information dissemination and entertainment applications. This paper explores the design choices made in the development of clustering algorithms targeted at VANETs. It presents a taxonomy of the techniques applied to solve the problems of cluster head election, cluster affiliation, and cluster management, and identifies new directions and recent trends in the design of these algorithms. Additionally, methodologies for validating clustering performance are reviewed, and a key shortcoming—the lack of realistic vehicular channel modeling—is identified. The importance of a rigorous and standardized performance evaluation regime utilizing realistic vehicular channel models is demonstrated.

Finding optimal solutions to Rubik's cube using pattern databases

Abstract: We have found the first optimal solutions to random instances of Rubik's Cube. The median optimal solution length appears to be 18 moves. The algorithm used is iterative-deepening-A* (IDA*), with a lower-bound heuristic function based on large memory-based lookup tables, or "pattern databases" (Culberson and Schaeffer 1996). These tables store the exact number of moves required to solve various subgoals of the problem, in this case subsets of the individual movable cubies. We characterize the effectiveness of an admissible heuristic function by its expected value, and hypothesize that the overall performance of the program obeys a relation in which the product of the time and space used equals the size of the state space. Thus, the speed of the program increases linearly with the amount of memory available. As computer memories become larger and cheaper, we believe that this approach will become increasingly cost-effective.