Modeling and Adapting JPEG to the Energy Requirements of VSN

Mammeri, Abdelhamid; Khoumsi, Ahmed; Ziou, Djemel; Hadjou, Brahim

doi:10.1109/icccn.2008.ecp.151

Cited by 26 publications

(28 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Energy Consumption of DCT In order to demonstrate that DCT is the main energy consuming stage, we assess the percentage of energy consumption in JPEG encoding/decoding due to DCT. The energy consumption model of JPEG that we adopted is the one found in [8,9]. The modeling takes into consideration the operations that are performed in every step for an entire image of size N and block size k. The energy model is represented by the following equations.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Energy efficient JPEG using stochastic processing

Saab

Elhajj

Chehab

et al. 2012

2012 International Conference on Energy Aware Computing

View full text Add to dashboard Cite

One of the serious challenges facing smartphones and other portable devices is battery life. We aim at improving the energy efficiency of multimedia applications for improved battery life while maintaining an acceptable level of user experience. In this paper, we design a stochastic processing technique for JPEG to limit its energy consumption particularly on mobile devices. This is a first step towards improving the energy efficiency for MPEG, since video streaming and conferencing applications are some of the most energyconsuming applications. We demonstrate the feasibility of using stochastic processing for multimedia encoders and analyze the resulting energy savings.

show abstract

Section: Methodsmentioning

confidence: 99%

“…The authors in [8,9] provide a set of equations that model the energy consumption in every stage in JPEG. Their work however did not tackle every stage on its own.…”

Section: Related Workmentioning

confidence: 99%

Energy efficient JPEG using stochastic processing

Saab

Elhajj

Chehab

et al. 2012

2012 International Conference on Energy Aware Computing

View full text Add to dashboard Cite

show abstract

“…In [8] for example, Mammeri et al consider an isosceles right-angled triangle shape with legs of size k. As a result, only k(kþ1)/2 DCT coefficients are assumed to be important instead of k 2 . However, Makkaoui et al in [9] showed that the square shape provides a better energy-distortion trade-off than the triangle shape, and consequently, the square shape will be considered in this work.…”

Section: Related Workmentioning

confidence: 98%

Design and performance analysis of a zonal DCT-based image encoder for Wireless Camera Sensor Networks

Kaddachi¹,

Soudani²,

Lecuire³

et al. 2012

Microelectronics Journal

View full text Add to dashboard Cite

“…The selected values on these pa rameters are used in relevant image compression literature [3], [17]. Using the constituent energy consumption formulae devel oped in III-B (Eq.…”

Section: ) Energy Consumption For Image Compressionmentioning

confidence: 99%

Adaptive transmission range assignment algorithm for in-routing image compression on wireless sensor networks

Tran-Quang

Huu

Miyoshi

2010

International Conference on Communications and Electronics 2010

View full text Add to dashboard Cite

Analyzing each constituent task involved in wireless sensor network image compression and communication processes revealed a considerable difference in energy consumption rates among these tasks, shortening the network lifetime, which is defined as the time interval to the death of the first sensor node. To overcome this problem, we proposed an adaptive transmission range assignment algorithm for in-routing image compression (ARIC) for wireless sensor networks. ARIC uses collaborative image compression to distribute the computational cost among the sensor nodes involved in routing paths between source nodes and base stations. The energy distribution involved in ARIC is formulated here as a mathematical optimization problem that can be mapped to a 0-1 Multiple Choice Knapsack Problem (0-1 MCKP); we then present a dynamic programming method to solve for the optimization solution. This method allows the source sensor nodes to dynamically assign compression and com munication tasks to appropriate sensor nodes according to their residual energy. To verify ARIC's efficacy, we then conducted computational experiments on the optimization problem; these show that ARIC effectively balances the total energy consumption among sensor nodes thereby increasing the overall network lifetime.

show abstract

Modeling and Adapting JPEG to the Energy Requirements of VSN

Cited by 26 publications

References 4 publications

Energy efficient JPEG using stochastic processing

Energy efficient JPEG using stochastic processing

Design and performance analysis of a zonal DCT-based image encoder for Wireless Camera Sensor Networks

Adaptive transmission range assignment algorithm for in-routing image compression on wireless sensor networks

Contact Info

Product

Resources

About