Fusion function placement for Active Networks paradigm in wireless sensor networks

Abstract

Active Networks paradigm integrated with distributed data fusion has the potential to significantly reduce energy dissipation in wireless sensor networks, where energy conservation is the most challenging issue. This work aims to minimize energy cost when distributed data fusion is deployed for the Active Networks computing paradigm. First we propose an optimal solution for mapping task graph of distributed data fusion application into network. Optimal solution uses an exhaustive search algorithm for finding the placements with minimized power consumption. However, optimal solution has high computational complexity—O(mn k ), where n denotes the number of network nodes, m is the number of fusion functions, and k is the maximum number of children a fusion function has in task graph and its children are also fusion functions. Then, an approximate solution with low complexity (O(mlog n + log2 m)) is proposed called P2lace, which includes two phases, task graph partition and task graph placement. Finally, an extensive evaluation compares approximate solution with optimal solution. The results show that approximate solution is scalable with different task graph characteristics and network size and only causes slightly more transmission cost than optimal solution. And the algorithm without optimizing is shown to be applicable to the network, where the sink node does not have global information of entire network.