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We consider k-coverage of a line by a two-dimensional, non homogeneous Poisson-Boolean model. This has applications in sensor networks. We extend the analysis of [1] to the case for k > 1. The extension requires us to define a vector Markov process that tracks the k segments that have the longest residual coverage at a point. This process is used to determine the probability of a segment of the...
Recent research in PC based routers has proposed a distributed architecture. Such an architecture poses several challenges in the areas of scalability, robustness, efficiency of routing, latency and other issues.We examine the issue of decrease in throughput due to large routing tables in this architecture and propose partitioning as a solution. Our contribution is twofold: defining the concept of...
We describe a reinforcement learning based scheme to estimate the stationary distribution of subsets of states of large Markov chains. dasiaSplit samplingpsila ensures that the algorithm needs to just encode the state transitions and will not need to know any other property of the Markov chain. (An earlier scheme required knowledge of the column sums of the transition probability matrix.) This algorithm...
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