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首页> 外文会议>2011 6th International ICST Conference on Communications and Networking in China >Compressive sensing based sparse event detection in wireless sensor networks
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Compressive sensing based sparse event detection in wireless sensor networks

机译：无线传感器网络中基于压缩感知的稀疏事件检测

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摘要

We investigate the compressive sensing theory(CS) for sparse events detection and reconstruction in energy-constrained large-scale wireless sensor networks(WSNs). In order to save more energy and prolong the lifetime of the network, we partition the nodes into C sets nearly uniformly in a purely distributed way by using game theory. In each specified time slot, we only wake up parts of the C sets nodes, and set the rest nodes to sleep for saving energy. Based on the proposed sleeping strategy and capitalizing on the spatial sparsity of the event in the local area, we apply compressive sensing theory to gather and reconstruct the sparse signals. The proposed algorithm for sparse events detection is able to efficiently reduce the number of sensors without introducing intensive computation and lose of detection resolution. Especially, the proposed algorithm is mainly based on the greedy algorithms, such as Orthogonal Matching Pursuit, Regularized Orthogonal Matching Pursuit and Subspace Pursuit algorithm. What is more important, based on the game theoretical sleeping strategy, compressive sensing algorithm shows a better detection resolution than the random sleeping strategy. Finally, extensive simulations confirm the performance and robustness of the proposed algorithm under noised environment.
机译：我们研究压缩感知理论（CS）用于能量受限的大型无线传感器网络（WSNs）中的稀疏事件检测和重构。为了节省更多的能量并延长网络的寿命，我们使用博弈论以纯分布的方式将节点几乎均匀地划分为C集。在每个指定的时隙中，我们仅唤醒部分C set节点，并将其余节点设置为睡眠以节省能量。基于所提出的睡眠策略并利用事件在本地的空间稀疏性，我们应用压缩感知理论来收集和重建稀疏信号。所提出的用于稀疏事件检测的算法能够有效地减少传感器的数量，而不会引起密集的计算和检测分辨率的损失。特别地，所提出的算法主要基于贪婪算法，例如正交匹配追踪，正则化正交匹配追踪和子空间追踪算法。更重要的是，基于博弈理论的睡眠策略，压缩感知算法显示出比随机睡眠策略更好的检测分辨率。最后，大量的仿真证实了该算法在噪声环境下的性能和鲁棒性。

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