Broadcasting in wireless networks, unlike wired networks, inherently reaches several nodes with a single transmission. For an omnidirectional wireless broadcast to a node, all nodes closer to the transmitting node are also reached. This property can be used to compute routing trees which minimize the sum of the transmitter powers. We present a mixed integer programming formulation and a simulated annealing algorithm for the problem. Extensive experimental results for the heuristic approach are presented. They show that the proposed algorithm is capable of improving the results of state-of-the-art algorithms for most of the problems considered. The solutions provided by the simulated annealing algorithm can be improved by applying a very fast post-optimization procedure. This leads to the best known mean results for the problems considered.
The minimum power broadcast problem in wireless networks: a simulated annealing approach / Montemanni, Roberto; Gambardella Luca, Maria; Das Arindam, Kumar. - 4:(2005), pp. 2057-2062. (Intervento presentato al convegno IEEE Wireless Communications and Networking Conference tenutosi a New Orleans, LA, usa nel May 2005) [10.1109/WCNC.2005.1424835].
The minimum power broadcast problem in wireless networks: a simulated annealing approach
Montemanni Roberto;
2005
Abstract
Broadcasting in wireless networks, unlike wired networks, inherently reaches several nodes with a single transmission. For an omnidirectional wireless broadcast to a node, all nodes closer to the transmitting node are also reached. This property can be used to compute routing trees which minimize the sum of the transmitter powers. We present a mixed integer programming formulation and a simulated annealing algorithm for the problem. Extensive experimental results for the heuristic approach are presented. They show that the proposed algorithm is capable of improving the results of state-of-the-art algorithms for most of the problems considered. The solutions provided by the simulated annealing algorithm can be improved by applying a very fast post-optimization procedure. This leads to the best known mean results for the problems considered.
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