Paper abstract bibtex

We study an NP-hard problem motivated by energy-efficiently maintaining the connectivity of a symmetric wireless sensor communication network: Given an edge-weighted n-vertex graph, find a connected spanning subgraph of minimum cost, where the cost is determined by letting each vertex pay the most expensive edge incident to it in the subgraph. On the negative side, we show that o(logn)-approximating the difference d between the optimal solution cost and a natural lower bound is NP-hard and that, under the Exponential Time Hypothesis, there are no exact algorithms running in 2^o(n) time or in f(d)⋅n^O(1) time for any computable function f. On the positive side, we provide an algorithm that reconnects O(logn) connected components with minimum additional cost in polynomial time. These algorithms are motivated by application scenarios of monitoring areas or where an existing sensor network may fall apart into several connected components due to sensor faults. In experiments, the algorithm solves instances with four such connected components and about 8 000 vertices in five minutes, outperforming CPLEX with known ILP formulations for the problem.

@article{BBN+xx, author = {{Bentert}, Matthias and {van Bevern}, René and {Nichterlein}, André and {Niedermeier}, Rolf and Smirnov, Pavel V.}, title = {Parameterized algorithms for power-efficiently connecting wireless sensor networks: Theory and experiments}, journal = {INFORMS Journal on Computing}, abstract = {We study an NP-hard problem motivated by energy-efficiently maintaining the connectivity of a symmetric wireless sensor communication network: Given an edge-weighted n-vertex graph, find a connected spanning subgraph of minimum cost, where the cost is determined by letting each vertex pay the most expensive edge incident to it in the subgraph. On the negative side, we show that o(logn)-approximating the difference d between the optimal solution cost and a natural lower bound is NP-hard and that, under the Exponential Time Hypothesis, there are no exact algorithms running in 2^o(n) time or in f(d)⋅n^O(1) time for any computable function f. On the positive side, we provide an algorithm that reconnects O(logn) connected components with minimum additional cost in polynomial time. These algorithms are motivated by application scenarios of monitoring areas or where an existing sensor network may fall apart into several connected components due to sensor faults. In experiments, the algorithm solves instances with four such connected components and about 8 000 vertices in five minutes, outperforming CPLEX with known ILP formulations for the problem.}, sourcecode = {https://gitlab.com/rvb/mpsc}, year = {accepted for publication, 2020}, url = {https://arxiv.org/abs/1706.03177v3}, date = {2020-10-08} }

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