Compressed Sensing Signal and Data Acquisition in Wireless Sensor Networks and Internet of Things (Extended) IEEE Industrial Electronics Technology News

Shancang Li

Research output: Contribution to conferenceConference Paperpeer-review

Abstract

Internet of Things (IoT) is expected to seamlessly integrate information from heterogeneous networks and networked physical objects, in which things can be uniquely identified. IoT makes it possible for products and service providers to develop brand-new services by integrating current technologies such as web services, radio frequency identification (RFID), wireless sensor networks (WSNs), intelligent sensors, and embedded devices. It is reported that IoT products and services will generate incremental revenue exceeding £180 billion in 2020 and will result in £1.2 trillion in global economic markets. IoT can provide a low cost combination of products and services, which makes the providers be able to easily extend the use of emerging technologies and will create a wide range of products and services for diverse markets, such as manufacturing, industrial, healthcare, agriculture, transportation, etc.

In IoT, one of the most important issues is to capture and collect data at the end nodes of IoT (such as RFID devices, intelligent sensors, networked devices, etc.). The infrastructure of IoT is expected to work at an extremely energy efficient status and the data generated by end-node can be transmitted over IoT in the compressed form, thereby greatly reduce the energy consumption and computation cost but exact information can be recovered. In recent years, the emerging compressed sensing (CS) attracts considerable attention in information processing, applied mathematics, computer sciences, and communication networks, which breaks the Shannon’s sampling theorem and is capable of recovering a signal/data without causing information lost. CS is an exciting and rapid growing field for acquiring the compressible and sparse signal/data in resource limited networks such as IoT, WSNs, etc. CS has been successfully used in wide-range applications such as signal acquisition, image and video processing, healthcare, radar, etc. It is reported that compressed sensing is able to improve performance with a very little cost. We believe this is just the beginning of CS applications in communication networks, and the future will see even more fruitful applications of CS in this field.

The CS will make IoT to collect data at an extremely low redundant level from various end nodes, which may play a critical role in IoT to save energy and transmission burden. The 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 IoT. By considering the features of IoT and networked objectives, the proposed 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.

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 cost. For the first time, our work studies information acquisition in IoT and WSNs with CS from the perspective of data-compressed sampling, robust transmission, and accurate reconstruction to reduce the energy consumption, computation cost, data redundancy and increase the network capacity.

Secondly, a CS-based information acquisition framework is proposed for the infrastructure of IoT, which involves the compressed sampling at IoT end nodes, information transmission over IoT, and accurate data reconstruction at information processing nodes in IoT. In this framework, the signal/data noise model, communication load, and recovery accuracy are considered for its industrial applications, which makes it possible to use this framework in various applications, such as healthcare, industrial monitoring, etc.

Thirdly, an efficient cluster-sparse reconstruction algorithm is proposed for in-network compression aiming at more accurate data reconstruction and higher energy efficiency. Performance is evaluated with respect to network size using datasets acquired by a real-life deployment. By taking the correlation of sensing data over IoT and WSNs into consideration, an adaptive sparse representation and corresponding signal reconstruction algorithm is proposed that offers a higher accuracy and lower computational complexity compared with pre-existing group/cluster-sparse reconstruction algorithms.

This work has shown that CS can be a powerful data acquisition tool for saving energy and communication resources in networks and information systems. It further strengthens the connection between information theory and CS. As part of our ongoing work, we are investigating the best achievable CS reconstruction schemes over information systems and networks.
Original languageEnglish
Publication statusPublished - 2014

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