32,291 research outputs found

    Distributed boundary tracking using alpha and Delaunay-Cech shapes

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    For a given point set SS in a plane, we develop a distributed algorithm to compute the α\alpha-shape of SS. α\alpha-shapes are well known geometric objects which generalize the idea of a convex hull, and provide a good definition for the shape of SS. We assume that the distances between pairs of points which are closer than a certain distance r>0r>0 are provided, and we show constructively that this information is sufficient to compute the alpha shapes for a range of parameters, where the range depends on rr. Such distributed algorithms are very useful in domains such as sensor networks, where each point represents a sensing node, the location of which is not necessarily known. We also introduce a new geometric object called the Delaunay-\v{C}ech shape, which is geometrically more appropriate than an α\alpha-shape for some cases, and show that it is topologically equivalent to α\alpha-shapes

    Lifetime Improvement in Wireless Sensor Networks via Collaborative Beamforming and Cooperative Transmission

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    Collaborative beamforming (CB) and cooperative transmission (CT) have recently emerged as communication techniques that can make effective use of collaborative/cooperative nodes to create a virtual multiple-input/multiple-output (MIMO) system. Extending the lifetime of networks composed of battery-operated nodes is a key issue in the design and operation of wireless sensor networks. This paper considers the effects on network lifetime of allowing closely located nodes to use CB/CT to reduce the load or even to avoid packet-forwarding requests to nodes that have critical battery life. First, the effectiveness of CB/CT in improving the signal strength at a faraway destination using energy in nearby nodes is studied. Then, the performance improvement obtained by this technique is analyzed for a special 2D disk case. Further, for general networks in which information-generation rates are fixed, a new routing problem is formulated as a linear programming problem, while for other general networks, the cost for routing is dynamically adjusted according to the amount of energy remaining and the effectiveness of CB/CT. From the analysis and the simulation results, it is seen that the proposed method can reduce the payloads of energy-depleting nodes by about 90% in the special case network considered and improve the lifetimes of general networks by about 10%, compared with existing techniques.Comment: Invited paper to appear in the IEE Proceedings: Microwaves, Antennas and Propagation, Special Issue on Antenna Systems and Propagation for Future Wireless Communication

    Distributed Dominating Sets on Grids

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    This paper presents a distributed algorithm for finding near optimal dominating sets on grids. The basis for this algorithm is an existing centralized algorithm that constructs dominating sets on grids. The size of the dominating set provided by this centralized algorithm is upper-bounded by (m+2)(n+2)5\lceil\frac{(m+2)(n+2)}{5}\rceil for m×nm\times n grids and its difference from the optimal domination number of the grid is upper-bounded by five. Both the centralized and distributed algorithms are generalized for the kk-distance dominating set problem, where all grid vertices are within distance kk of the vertices in the dominating set.Comment: 10 pages, 9 figures, accepted in ACC 201

    Parallelized Particle and Gaussian Sum Particle Filters for Large Scale Freeway Traffic Systems

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    Large scale traffic systems require techniques able to: 1) deal with high amounts of data and heterogenous data coming from different types of sensors, 2) provide robustness in the presence of sparse sensor data, 3) incorporate different models that can deal with various traffic regimes, 4) cope with multimodal conditional probability density functions for the states. Often centralized architectures face challenges due to high communication demands. This paper develops new estimation techniques able to cope with these problems of large traffic network systems. These are Parallelized Particle Filters (PPFs) and a Parallelized Gaussian Sum Particle Filter (PGSPF) that are suitable for on-line traffic management. We show how complex probability density functions of the high dimensional trafc state can be decomposed into functions with simpler forms and the whole estimation problem solved in an efcient way. The proposed approach is general, with limited interactions which reduces the computational time and provides high estimation accuracy. The efciency of the PPFs and PGSPFs is evaluated in terms of accuracy, complexity and communication demands and compared with the case where all processing is centralized
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