Computing and Information Systems - Research Publications
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ItemProactive Traffic Merging Strategies for Sensor-Enabled Cars(IGI Global, 2009)Congestion is a major challenge in today’s road traffic. The primary cause is bottlenecks such as ramps leading onto highways, or lane blockage due to obstacles. In these situations, the road capacity reduces because several traffic streams merge to fewer streams. Another important factor is the non-coordinated driving behavior resulting from the lack of information or the intention to minimize the travel time of a single car. This chapter surveys traffic control strategies for optimizing traffic flow on highways, with a focus on more adaptive and flexible strategies facilitated by current advancements in sensor-enabled cars and vehicular ad hoc networks (VANETs). The authors investigate proactive merging strategies assuming that sensor-enabled cars can detect the distance to neighboring cars and communicate their velocity and acceleration among each other. Proactive merging strategies can significantly improve traffic flow by increasing it up to 100% and reduce the overall travel delay by 30%.
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ItemEfficient data collection and selective queries in sensor networks(SPRINGER-VERLAG BERLIN, 2008)
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ItemSensor web: Integration of sensor networks with web and cyber infrastructure(IGI Global, 2010-12-01)As sensor network deployments grow and mature there emerge a common set of operations and transformations. These can be grouped into a conceptual framework called Sensor Web. Sensor Web combines cyber infrastructure with a Service Oriented Architecture (SOA) and sensor networks to provide access to heterogeneous sensor resources in a deployment independent manner. In this chapter we present the Open Sensor Web Architecture (OSWA), a platform independent middleware for developing sensor applications. OSWA is built upon a uniform set of operations and standard data representations as defined in the Sensor Web Enablement Method (SWE) by the Open Geospatial Consortium (OGC). OSWA uses open source and grid technologies to meet the challenging needs of collecting and analyzing observational data and making it accessible for aggregation, archiving and decision making.
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ItemOpportunistic Sampling in Wireless Sensor Networks(ACM, 2009)In an active WSN where user queries are regularly processed, a significant proportion of nodes relay and overhear data generated by other nodes in the network. In this paper, we propose to exploit this mode of data communication towards a gradual buildup of global knowledge. We show that by harnessing the multihop and multipath communication advantages, only a few user queries in a WSN can lead to an accumulation of accurate global knowledge at node level. This global knowledge can greatly improve numerous WSN applications when used in data validation, event detection, and query optimization.
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ItemLocal network Voronoi diagrams(ACM, 2010)Continuous queries in road networks have gained significant research interests due to advances in GIS and mobile computing. Consider the following scenario: "A driver uses a networked GPS navigator to monitor five nearest gas stations in a road network." The main challenge of processing such a moving query is how to efficiently monitor network distances of the k nearest and possible resultant objects. To enable result monitoring in real-time, researchers have devised techniques which utilize precomputed distances and results, e.g., the network Voronoi diagram (NVD). However, the main drawback of preprocessing is that it requires access to all data objects and network nodes, which means that it is not suitable for large datasets in many real life situations. The best existing method to monitor kNN results without precomputation relies on executions of snapshot queries at network nodes encountered by the query point. This method results in repetitive distance evaluation over the same or similar sets of nodes. In this paper, we propose a method called the local network Voronoi diagram (LNVD) to compute query answers for a small area around the query point. As a result, our method requires neither precomputation nor distance evaluation at every intersection. According to our extensive analysis and experimental results, our method significantly outperforms the best existing method in terms of data access and computation costs.
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ItemThe V*Diagram: A query-dependent approach to moving KNN queries(Association for Computing Machinery (ACM), 2008-01-01)The moving k nearest neighbor (M k NN) query finds the k nearest neighbors of a moving query point continuously. The high potential of reducing the query processing cost as well as the large spectrum of associated applications have attracted considerable attention to this query type from the database community. This paper presents an incremental safe-region-based technique for answering M k NN queries, called the V*-Diagram. In general, a safe region is a set of points where the query point can move without changing the query answer. Traditional safe-region approaches compute a safe region based on the data objects but independent of the query location. Our approach exploits the current knowledge of the query point and the search space in addition to the data objects. As a result, the V*-Diagram has much smaller IO and computation costs than existing methods. The experimental results show that the V*-Diagram outperforms the best existing technique by two orders of magnitude.
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ItemPrivacy preserving group nearest neighbor queries(ACM, 2010-05-19)
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ItemAnalysis and evaluation of V*-kNN: an efficient algorithm for moving kNN queries(SPRINGER, 2010-06)
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ItemRobust Traffic Merging Strategies for Sensor-Enabled Cars Using Time Geography(ACM, 2009)We present two novel merging algorithms that optimize traffic flow on highways, particularly at intersections of ramps and main roads. In our work, cars are equipped with sensors that can detect distance to neighboring cars, and communicate their velocity and acceleration readings with one another. Sensor-enabled cars can locally exchange sensed information about traffic and adapt their behavior much earlier than regular cars. However, the accuracy level of sensors is a major challenge for merging algorithms, because inaccuracies can potentially lead to unsafe merging behaviors. In this paper, we investigate how the accuracy of sensors impacts merging algorithms, and design robust merging algorithms that tolerate sensor errors. Experimental results show that our main proposed merging algorithm, which is based on concepts from time geography, is able to guarantee safe merging while tolerating four times more imprecise positioning information, and can double the road capacity and increase the traffic flow by 25%.