Utilizing Correlations to Compress Time-Series in Traffic Monitoring Sensor Networks
Utilizing Correlations to Compress Time-Series in Traffic Monitoring Sensor Networks
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DOI：
10.1109/wcnc.2007.462
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发表时间：
2007-03
期刊：
影响因子：
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通讯作者：
A. Guitton;Antonios Skordylis;A. Trigoni
中科院分区：
文献类型：
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作者：
A. Guitton;Antonios Skordylis;A. Trigoni
无线传感器网络为精细和连续的道路交通监控提供了重要的机会，使仔细的城市规划，自动化道路维护和事故检测成为可能。用户通常愿意容忍车流数据中的小误差，以便降低从传感器节点到用户连接的网关节点的数据传播的成本。在本文中，我们首先研究的相对性能的傅立叶和小波为基础的算法压缩交通数据在本地的传感器节点。使用真实的交通信息，从城市的剑桥（英国），然后，我们证明了在地理上分散的传感器节点收集的车流数据表现出很强的空间和时间相关性。然后，我们结合联合收割机有损傅立叶压缩与基于相关性的压缩，以实现进一步的通信节省在用户指定的错误阈值。对于每5分钟5-15辆汽车的容许误差，示出了利用时间相关性相对于单独的傅立叶压缩产生14-30%的节省，而使用空间相关性导致10-35%的节省。
Wireless sensor networks present significant opportunities for fine-grained and continuous monitoring of road traffic, enabling careful city planning, automated road maintenance and accident detection. Users are typically willing to tolerate a small error in car-flow data, in order to reduce the cost of data propagation from the sensor nodes to the gateway nodes, to which users are connected. In this paper, we first examine the relative performance of Fourier- and wavelet-based algorithms for compressing traffic data locally at the sensor nodes. Using real traffic information from the city of Cambridge (UK), we then demonstrate that car-flow data collected across geographically dispersed sensor nodes exhibit strong spatial and temporal correlations. We then combine lossy Fourier-compression with correlation-based compression to achieve further communication savings within a user-specified error threshold. For a tolerated error of 5-15 cars per 5 min, it is shown that exploitation of temporal correlations yields 14-30% savings relative to Fourier compression alone, whilst use of spatial correlations results in 10-35% savings.