Compressed Sensing Signal and Data Acquisition in Wireless Sensor Networks and Internet of Things

By Shancang Li, Lida Xu, Xinheng Wang. IEEE Transactions on Industrial Informatics, 2013

575 citations recorded by the source corpus.

The emerging compressed sensing (CS) theory can significantly reduce the number of sampling points that directly corresponds to the volume of data collected, which means that part of the redundant data is never acquired. It makes it possible to create standalone and net-centric applications with fewer resources required in Internet of Things (IoT). CS-based signal and information acquisition/compression paradigm combines the nonlinear reconstruction algorithm and random sampling on a sparse basis that provides a promising approach to compress signal and data in information systems. This paper investigates how CS can provide new insights into data sampling and acquisition in wireless sensor networks and IoT. First, we briefly introduce the CS theory with respect to the sampling and transmission coordination during the network lifetime through providing a compressed sampling process with low computation costs. Then, a CS-based framework is proposed for IoT, in which the end nodes measure, transmit, and store the sampled data in the framework. Then, an efficient cluster-sparse reconstruction algorithm is proposed for in-network compression aiming at more accurate data reconstruction and lower energy efficiency. Performance is evaluated with respect to network size using datasets acquired by a real-life deployment.

Subfields: Biomedical Engineering, Computational Mechanics, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/f0bf8dbaa57e1bf37cfc2f441f6699fd8712ee6f

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Citations in the corpus

Impact of payment technology innovations on the traditional financial industry: A focus on China (2018, background): “Recently, the Internet of Things (IoT) and cloud computing have been widely applied in supply chain, logistics, and transportation (Bi et al., 2014; Cai et al., 2014; Gorkhali and Xu, 2016; Fang et al., 2014; Jiang et al., 2014; Kim, 2017; Li et al., 2013; Liu et al., 2016, 2017; Whitmore et al., 20”
DEEProtect: Enabling Inference-based Access Control on Mobile Sensing Applications (2017, background): “1) Autoencoder based Data Minimization: Mobile sensor data is usually high-dimensional in nature, which typically exhibits both structure and redundancy, allowing minimization [50], [6].”
IoT-Based Intelligent Perception and Access of Manufacturing Resource Toward Cloud Manufacturing (2014, background): “The concept of IoT [24], [25] can be tracked back to the book “The Road Ahead” which is published in 1995.”
State-of-the-art, challenges, and open issues in the integration of Internet of things and cloud computing (2016, methodology): “On the other hand, the work in [74] proposed a CS (Compressed Sensing) framework with a different approach than SensCompr.”
IoT-Based Smart Rehabilitation System (2014, background): “First, the ontology brings in better structured domain knowledge on rehabilitation engineering, which helps us to perform better analysis with a clear inclusion relation, parallel relation, and less or no disambiguation [17].”
Fog of Everything: Energy-Efficient Networked Computing Architectures, Research Challenges, and a Case Study (2017, methodology): “pre-processing (like, data compressing, fusion and filtering [111]); and, (iii) routed to Cloud-based remote data centers over (typically, multi-hop) Internet WANs for further post-processing.”
An IoT-Oriented Data Storage Framework in Cloud Computing Platform (2014, methodology): “Researchers [4] provide a method for wireless sensors to reduce the redundant data collection.”
A Reconfigurable Smart Sensor Interface for Industrial WSN in IoT Environment (2014, background): “Sensor data acquisition surface device is the key part of study on industrial WSN application [17].”
An Integrated System for Regional Environmental Monitoring and Management Based on Internet of Things (2014, background): “The architecture of the IIS for regional environmental monitoring and management based on IoT contains four layers: perception layer, network layer, middleware layer, and application layer.”
Compressed Sensing (2014, unknown): “Estas abarcan diversas áreas como: imagenoloǵıa medica [49, 45], conversión analógico/digital [68], detección mediante RADAR [4, 42], corrección de errores [18], cámaras digitales [34, 21, 51], Sismoloǵıa [41], Radioastronomı́a [71], comunicación inalámbrica [63, 3, 65] y redes de sensores inalámbri”
The Internet of Things—A survey of topics and trends (2014, background): “…et al. 2009; Sheng et al. 2010) NFC (Broll et al. 2009; Garrido et al. 2010) Sensor networks (Hong et al. 2010; Tozlu 2011; Zhu et al. 2010; Li et al. 2013) Software Middleware (Aberer et al. 2006; Bandyopadhyay et al. 2011; Blackstock et al. 2010; De et al. 2011; Dong et al. 2010;…”
Accelerating Federated Learning via Momentum Gradient Descent (2019, methodology): “Compressed sensing, where the sparse data of the edge can be efﬁciently sampled and reconstructed with transmitting a much smaller data size, was applied to data acquisition of Internet of Things (IoT) network [16], [17].”
A Health-IoT Platform Based on the Integration of Intelligent Packaging, Unobtrusive Bio-Sensor, and Intelligent Medicine Box (2014, background): “The proposed platform seamlessly fuses IoT devices (e.g., wearable sensors, intelligent medicine packages, etc.) with in-home healthcare services (e.g., telemedicine) for an improved user experience and service efficiency.”
CCIoT-CMfg: Cloud Computing and Internet of Things-Based Cloud Manufacturing Service System (2014, background): “At the same time, service-oriented technologies (SOTs) [20], advanced computing technologies (ACTs), virtualization technology, embedded technology, cyber-physical systems (CPS), cloud computing (CC) [21], and Internet of things (IoT) [22] have been quickly developed and widely applied.”
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