DASS: Distributed Adaptive Sparse Sensing

Abstract

Abstract-Wireless sensor networks are often designed to perform two tasks: sensing a physical field and transmitting the data to end-users. A crucial design aspect of a WSN is the minimization of the overall energy consumption. Previous researchers aim at optimizing the energy spent for the communication, while mostly ignoring the energy cost of sensing. Recently, it has been shown that considering the sensing energy cost can be beneficial for further improving the overall energy efficiency. More precisely, sparse sensing techniques were proposed to reduce the amount of collected samples and recover the missing data using data statistics. While the majority of these techniques use fixed or random sampling patterns, we propose adaptively learning the signal model from the measurements and using the model to schedule when and where to sample the physical field. The proposed method requires minimal on-board computation, no inter-node communications, and achieves appealing reconstruction performance. With experiments on real-world datasets, we demonstrate significant improvements over both traditional sensing schemes and the state-of-the-art sparse sensing schemes, particularly when the measured data is characterized by a strong intra-sensor (temporal) or inter-sensors (spatial) correlation.Index Terms-Wireless sensor networks, sparse sensing, adaptive sampling scheduling, compressive sensing, energy efficiency.

Temporal Data-Driven Sleep Scheduling and Spatial Data-Driven Anomaly Detection for Clustered Wireless Sensor Networks

“…Although the energy spent for the transmission is further optimized by reducing the amount of data transmitted from cluster members to the cluster head, the energy consumption of sensing is still ignored. Chen et al [4] realized a distributed adaptive sparse sensing based on compressive sensing, which reduced the amount of collected samples and recovered the missing data by using the spatial–temporal correlation. By exploiting spatial and temporal (spatiotemporal) correlations among sensor readings simultaneously, Gong et al [11] proposed a spatiotemporal compressive network coding.…”

“…This kind of data aggregation uses the spatial–temporal correlation to reduce the amount of transmitted data. Aimed at reducing the energy consumption caused by data sensing, sparse sensing based on the spatial–temporal correlation can reduce the amount of collected samples [4]. To reduce the energy consumption caused by node operation, the spatial–temporal correlation can be used to realize the sleep scheduling of nodes [5].…”

Limited-Memory Warping LCSS for Real-Time Low-Power Pattern Recognition in Wireless Nodes

“…This can be supported by software frameworks [9]. Another approach is to rely on signal processing techniques such as compressed sensing to reduce bandwidth by exploiting signal statistics [8]. Sparse representations decompose the sensor signal along an optimized basis and also allow to reduce bandwidth as well as improve classification performance.…”

Cost-Aware Dual Prediction Scheme for Reducing Transmissions at IoT Sensor Nodes

“…The sensor measurements in IoT tend to be spatiotemporally correlated, which can be exploited to reduce the number of transmitted measurements. This has motivated solutions such as adaptive sensing [2], [3], data compression [4], and data aggregation [5] to reduce the communication from sensor nodes. However, in delay-sensitive and safetycritical applications [6], it is important to fulfill real-time monitoring requirements, where compressing or aggregating data is not an option.…”