학술논문

Compressed Sensing for Energy and Bandwidth Starved IoT Applications
Document Type
Conference
Source
2018 IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER), 2018 IEEE. :131-134 Aug, 2018
Subject
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Image coding
Compressed sensing
Internet of Things
Sensors
Sparse matrices
Image reconstruction
Bandwidth
Compressed Sensing
Image processing
Energy efficiency
Language
Abstract
Ensuring security through the use of video surveillance cameras at public places is becoming attractive these days, thanks to the efficient compression, transmission and storage schemes. To up-scale the surveillance mechanism to large sensor networks, it is imperative that the applications become compatible to wireless sensor networks using Internet of Things (IoT) infrastructure. IoT nodes are generally energy and bandwidth-limited owing to their small size and large scale deployment. Therefore, any image/video acquisition application using IoT infrastructure should function within these constraints. Compressed sensing (CS) is one such paradigm that uses simultaneous sensing and compression and provides a technique for efficient image/video acquisition. This paper investigates the use of compressed sensing for image acquisition in IoT based applications that suffer from energy, bandwidth and storage limitations.