IEEE Transactions on Multi-Scale Computing Systems

From the January-March 2015 issue

An Energy-Efficient Wireless Video Sensor Node for Moving Object Surveillance

By Jong Hwan Ko, Burhan Ahmad Mudassar, and Saibal Mukhopadhyay

Featured article thumbnail imageIn a wireless video sensor system, encoder and transmitter parameters should be jointly adjusted for the optimal tradeoff between video quality and energy consumption under variable channel conditions. The optimization of the system can be further enhanced by exploiting relative importance of moving objects in remote surveillance applications. This paper presents an energy-efficient wireless video sensor node for remote surveillance using content-aware pre-processing and an energy- and content-aware feedback control scheme. A low-overhead pre-processing engine detects the region of interest (ROI) in a frame defined as the macroblocks with moving objects, and an encoder selectively processes ROI macroblocks, resulting in reduction of both computation and transmission energy with minimum quality degradation of the ROI. We also propose an energy- and content-aware control scheme that jointly tunes the system parameters to maintain target transmission energy with minimum degradation of the ROI quality under varying wireless channel conditions. Compared to conventional encoding approaches, the proposed design consumes up to 56 percent less energy at the same quality.

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  • We're pleased to announce that Partha Pratim Pande, professor at Washington State University, has accepted the position of inaugural Editor-in-Chief.


Call for Papers

Special Issue on Design and Applications of Neuromorphic Computing System

Closed for submissions: January 15, 2016. View PDF.

As artificial intelligence technology becomes pervasive in society and ubiquitous in our lives, the desire for embedded-everywhere and human-centric computational intelligence systems calls for an intelligent computation paradigm. However, the applications of machine learning and neural networks involve large, noisy, incomplete, natural data sets that do not lend themselves to convenient solutions from current systems. Neuromorphic systems that are inspired by the working mechanism of human brains possess a massively parallel architecture with closely coupled memory and computing. This special issue aims at the computing methodology and systems across multiple technology scales to accelerate the development the neuromorphic hardware systems and the adoption for machine learning applications.

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