RIDA: A Robust Information-Driven Data Compression Architecture for Irregular Wireless Sensor Networks

Abstract

Abstract. In this paper, we propose and evaluate RIDA, a novel informationdriven architecture for distributed data compression in a sensor network, allowing it to conserve energy and bandwidth and potentially enabling high-rate data sampling. The key idea is to determine the data correlation among a group of sensors based on the value of the data itself to significantly improve compression. Hence, this approach moves beyond traditional data compression schemes which rely only on spatial and temporal data correlation. A logical mapping, which assigns indices to nodes based on the data content, enables simple implementation, on nodes, of data transformation without any other information. The logical mapping approach also adapts particularly well to irregular sensor network topologies. We evaluate our architecture with both Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) on publicly available real-world data sets. Our experiments on both simulation and real data show that 30% of energy and 80-95% of the bandwidth can be saved for typical multi-hop data networks. Moreover, the original data can be retrieved after decompression with a low error of about 3%. Furthermore, we also propose a mechanism to detect and classify missing or faulty nodes, showing accuracy and recall of 95% when half of the nodes in the network are missing or faulty.

A wavelet transform for efficient consolidation of sensor relations with quality guarantees

“…We want to highlight the benefit of our integration approach of building upon an unmodified routing tree and using an irregular transform. Two alternatives have been proposed: Using zero padding to account for the irregular routing tree [16] and subdividing the network into clusters, each of which applies a wavelet transform [8]. However, these approaches provide no error guarantees and are not designed to cope with multiple attributes.…”

“…As shown in Section 7, this yields large detail coefficients, which is in the way of a compact representation. [8] copes with the irregular topology by assigning the nodes to clusters and transforming the data of each cluster. The problem is that only data within a cluster is decorrelated, redundancies between clusters are not removed.…”

Compression in wireless sensor networks

“…In the following we briefly review the algorithms for in-network linear transform-based compression in WSNs. Interestingly, transform-based methods have also been used in the training phase of DSC-like algorithms for the purposes of gathering correlation knowledge [Dang et al 2007].…”

“…A number of works [Lee et al 2007;Dang et al 2007] have adopted the DCT for data compression in WSNs. The JPEG-based method in [Lee et al 2007] exploits the DCT for energy e cient communication of images in WSNs.…”

Adaptive WSN Scheduling for Lifetime Extension in Environmental Monitoring Applications

“…Thus measurements must be transferred to the sink as soon as they are available [6][7][8]. WSN measurements of data, such as temperature, humidity, air pressure, wind speed, nitrogen dioxide, and light, often exhibit internode data correlation and strong temporal correlations between different sampling times at the same node [9][10][11][12]. Knowledge of these correlations can be exploited to reduce the number of measurements needed to meet the application-specific sensing accuracy requirements.…”