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Design and Analysis of Compressive Data Persistence in Large-Scale Wireless Sensor Networks

机译：大规模无线传感器网络中压缩数据持久性的设计与分析

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摘要

This paper addresses the data persistence problem in wireless sensor networks (WSNs) where static sinks are not present and the sensed data have to be temporarily but resiliently stored in the network. Based on the observation that sensor readings are correlated, we propose compressive data persistence (CDP) scheme that makes use of the compressive sensing (CS) theory. Each sensor node independently computes and stores a random projection of the sensed data, such that a mobile sink can recover the data with high probability after visiting a small and random portion of the network. As a prerequisite of distributed CS encoding, sensor readings from all nodes are disseminated within the network through random walk. Therefore, the CS measurement matrix depends heavily on how the random walk is performed. In this paper, we present an in-depth analysis on the interplay between random walk parameters and sensing data characteristics, and derive the conditions in successful CS data recovery. In addition, we discover that there is a trade-off between the number of random walk instances and steps in order to achieve the required data persistence performance. Experiments using real sensor data verify that the proposed CDP scheme achieves much lower decoding ratio than the state-of-the-art Fountain code based schemes or the decentralized erasure codes based schemes, and demonstrate that there exist energy-optimized random walk parameters for CDP.
机译：本文解决了无线传感器网络（WSN）中的数据持久性问题，该网络中不存在静态接收器，并且感测到的数据必须临时但有弹性地存储在网络中。基于传感器读数相关的观察，我们提出了利用压缩感测（CS）理论的压缩数据持久性（CDP）方案。每个传感器节点独立地计算和存储感测数据的随机投影，以便移动接收器在访问网络的一小部分和随机部分后可以高概率恢复数据。作为分布式CS编码的先决条件，来自所有节点的传感器读数将通过随机游走在网络内传播。因此，CS测量矩阵在很大程度上取决于如何执行随机游走。在本文中，我们对随机游走参数与传感数据特征之间的相互作用进行了深入分析，并得出了成功进行CS数据恢复的条件。此外，我们发现在随机游动实例的数量和步骤之间要进行权衡，以实现所需的数据持久性性能。使用实际传感器数据进行的实验证明，与基于最新的方源代码方案或基于分散擦除码的方案相比，所提出的CDP方案实现的解码率低得多，并证明存在针对CDP的能量优化的随机游走参数。

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