Abstract
Maximizing the lifetime of wireless sensor networks is NP‐hard, and existing exact algorithms run in exponential time. These algorithms implicitly use only one CPU core. In this work, we propose to use multiple CPU cores to speed up the computation. The key is to decompose the problem into independent subproblems and then solve them on different cores simultaneously. We propose three decomposition approaches. Two of them are based on the notion that a tree does not contain cycles, and the third is based on the notion that, in any tree, a node has at most one parent. Simulations on an 8‐core desktop computer show that our approach can speed up existing algorithms significantly.
Highlights
In wireless sensor networks, each sensor node has only a limited amount of energy
The basic idea is to decompose the problem into independent subproblems and solve them on different cores using existing exact algorithms
The first is based on the fact that a tree does not contain cycles, so we can break the network into subnetworks whenever we encounter an undirected cycle
Summary
Each sensor node has only a limited amount of energy. When a node sends or receives messages, it consumes the corresponding amount of energy. The basic idea is to decompose the problem into independent subproblems and solve them on different cores using existing exact algorithms. The first is based on the fact that a tree does not contain (undirected) cycles, so we can break the network into subnetworks whenever we encounter an undirected cycle This approach applies to all algorithms that consider the network as either an undirected graph or a directed graph. The proposed approaches are applicable to all exact algorithms based on one CPU core. (2) We propose three problem decomposition approaches These approaches can decompose the problem into subproblems, which can be solved on different cores using any exact algorithm. The results suggest that, in general, the proposed approaches can reduce the empirical time of existing exact algorithms, especially when the problem size is large.
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