Utilizing Correlations to Compress Time-Series in Traffic Monitoring Sensor Networks

Abstract

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.Acknowledgments-Our work was supported by the Engineering and Physical Sciences Research Council (EPSRC) under the TIME-EACM award EP/C547640/1. Our traffic dataset was provided by SCOOT.

TIME: An Open Platform for Capturing, Processing and Delivering Transport-Related Data

“…We tested lossy compression techniques like the Fast Fourier Transform (FFT) and the Wavelet Transform (WT). We applied FFT and WT to compress a single time-series of traffic data, and further studied the use of spatio-temporal correlations to reduce multiple time-series [5].…”

“…If a strong correlation between the two signals exists, node d expresses the time-series of node a as a linear function of the time series of node b; it subsequently forwards only the timeseries data for node b and regression parameters for node a towards the gateway through the next hop e. A more detailed discussion on correlation-aware data dissemination techniques for traffic monitoring is provided in [5].…”

Query processing for mobile wireless sensor networks: State-of-the-art and research challenges

“…[2,14,15]? TIME-EACM project proposed a hybrid network architecture [17] which consists of three types of nodes: sensor nodes, gateway nodes and relay nodes. Sensor nodes are used to monitor the current environmental conditions and communicate with other sensor nodes.…”

“…Pushing pa al nodes to reduce power consumption. A nation algorithms [17] for reducing the cost of data pr nsor data s computation or caching. St which were "pushed" from ce s require that each no ering multidi store sensor information in a few se he results of continuous queries to be str of possible query processing p and it is the query optimizer's e to consideration in or west overall power co etwork topology cause the positions of network nodes have changed or their battery power is low.…”

“…TIME-EACM project proposed a class of correlation-aware dissemination algorithms. These algorithm de locally stores a series of data collected in the previous time period, in order to exploit correlations [17].…”