Adaptive Hybrid Compression for Wireless Sensor Networks

Abstract

Wireless Sensor Networks (WSNs) are often deployed to sample the desired environmental attributes and deliver the acquired samples to a central station, termed as the sink, for processing as needed by the application. Many applications stipulate high granularity and data accuracy that results in high data volumes. However, sensor nodes are battery powered, and sending the requested large amounts of data rapidly depletes their energy. Fortunately, environmental attributes (e.g., temperature, pressure) often exhibit spatial and temporal correlations. Moreover, a large class of applications such as scientific analysis and simulations tolerate high latency for sensor data collection. Hence, we exploit the spatiotemporal correlation of sensor readings while benefiting from possible data delivery latency tolerance to minimize the amount of data to be transported to the sink. Accordingly, we develop a fully distributed adaptive hybrid compression scheme that exploits both spatial and temporal data redundancies and fuses both temporal and spatial compression for maximal data compression with accuracy guarantees. We present two main contributions: (i) an adaptive modeling technique that allows frugal and maximized temporal compression on resource-constraint sensor nodes by exploiting the data collection latency, and (ii) a novel model-based hierarchical clustering technique that allows for maximized spatial compression resulting into a hybrid compression scheme. Compared to the existing spatiotemporal compression approaches, our approach is fully decentralized and the proposed clustering scheme is based on sensor data models rather than instantaneous sensor data values, which allows merging nearby nodes with similar models into large clusters over a longer period of time rather than specific time instances. The analysis for computation and message overheads, the analysis for theoretical compressibility, and simulations using real-world data demonstrate that our proposed scheme can provide significant communication/energy savings without sacrificing the accuracy of collected data.

Increasing network lifetime using data compression in wireless sensor networks with energy harvesting

“…[17][18][19] In Tavli et al, 17 an optimal data compression scheme was formulated as an optimization problem that maximizes the minimum lifetime of the sensor nodes in order to increase the network lifetime. It is assumed that each node can compress and decompress raw data with multiple compression levels.…”

“…Five different compression strategies based on the multi-level compression have been compared to investigate the impacts of these strategies on the network lifetime in the stealth mode of WSNs. The data compression for delay-tolerant applications in WSNs has also been considered in Ali et al 19 The authors have focused on both spatial and temporal correlations between the data collected by different sensor nodes over a long period of time. Accordingly, they proposed an adaptive hybrid compression (AHC) scheme that fuses both spatial and temporal compression in order to increase the compression ratio with a guaranteed data recovery accuracy.…”

“…The data compression methods presented in this category focus on how sensor nodes cooperatively compress the data packets transmitted on the network by sharing the network information such as the position of each node or the spatial correlation between sensing data at different sensor nodes. [17][18][19] In Tavli et al, 17 an optimal data compression scheme was formulated as an optimization problem that maximizes the minimum lifetime of the sensor nodes in order to increase the network lifetime. It is assumed that each node can compress and decompress raw data with multiple compression levels.…”

“…As one effective way to reduce the amount of data to be transmitted, data compression has been actively researched. [12][13][14][15][16][17][18][19] Using an appropriate compression method for sensing data, sensor nodes can transmit and receive smaller-size data packets. As a result, the energy consumption for data transmission at sensor nodes can be significantly reduced.…”

BuildSense

“…Data estimation: As data transmission consumes the most energy for an embedded sensor device, data estimation techniques are employed to reduce the amount of data transmission per sensor, thus energy consumption [11,18,29,30]. Hybrid methods combine on-node temporal compression followed by spatial compression during data collection [1]. Nodes with correlated readings can form spatial clusters to reporting only one measurement [37].…”