Compressed Sensing for Energy and Bandwidth Starved IoT Applications

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.