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Titlebook: Algorithms for Sensor Systems; 10th International S Jie Gao,Alon Efrat,Yanyong Zhang Conference proceedings 2015 Springer-Verlag Berlin Hei

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https://doi.org/10.1007/978-3-662-65674-7ote, we consider an important subfamily of non-deterministic dynamic networks: the time-homogeneous dynamic networks. We prove that it is hard to compute and approximate the value of the dynamic diameter for time-homogeneous dynamic networks.
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Strategies for Parallel Unaware Cleanersng with high probability, i.e. with probability .. For general graphs we present an .-competitive algorithm for the first visit problem, while for the visit problem we show an .-competitive algorithm both succeeding with high probability.
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Computing the Dynamic Diameter of Non-Deterministic Dynamic Networks is Hardote, we consider an important subfamily of non-deterministic dynamic networks: the time-homogeneous dynamic networks. We prove that it is hard to compute and approximate the value of the dynamic diameter for time-homogeneous dynamic networks.
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0302-9743 m on Algorithms and Experiments for Sensor Systems, Wireless Networks and Distributed Robotics, ALGOSENSORS 2014, held in Wroclaw, Poland, on September 12, 2014..The 10 papers presented in this volume were carefully reviewed and selected from 20 submissions. They are organized in topical sections na
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Conference proceedings 2015Systems, Wireless Networks and Distributed Robotics, ALGOSENSORS 2014, held in Wroclaw, Poland, on September 12, 2014..The 10 papers presented in this volume were carefully reviewed and selected from 20 submissions. They are organized in topical sections named: robot planning; algorithms and data st
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Dominic Lindner,Paul Niebler,Markus Wenzelt .. In the one-dimensional case, we prove that there are optimal solutions with nontrivial structural properties. These properties can be exploited to obtain an exact algorithm that runs in quasi-polynomial time. This generalizes a result by Tan et al. to the asymmetric case.
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https://doi.org/10.1007/978-3-322-82332-8hat are represented as unit interval graphs, we give a 2-approximation algorithm for the problem. For .-regular unit interval graphs, we give an optimal algorithm: it is guaranteed to have a latency that is within one time slot of the optimal latency. We also give tight bounds for the latency of aggregation convergecast for grids and tori.
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