Kablosuz Multimedya Algılayıcı Ağlarda Enerji Verimliliği için Katmanlı Mimari Üzerinde Bir Araştırma

Kablosuz Multimedya Algılayıcı Ağ (KMAA)’lar için enerji tüketimi en önemli problemdir. KMAA’lar Kablosuz Algılayıcı Ağ (KAA)’lara göre daha büyük boyutta veri aktardıkları ve veriler üzerinde işlem yapma yeteneğine sahip oldukları için enerji gereksinimleri oldukça fazladır. Bu çalışmada, KMAA’lar için önerilen enerji duyarlı protokoller katmanlı mimari yapısı baz alınarak incelenmiştir. Fiziksel katmandan başlanarak uygulama katmanına kadar literatürde yer alan enerji verimliliği ile ilgili çalışmalara yer verilmiştir. Bu çalışmanın hedefi, KMAA’larda geliştirilecek olan enerji verimli uygulamalar için ihtiyaç olan gereksinimlerin daha açık anlaşılmasını sağlamaktır

Energy Consumption Of Visual Sensor Networks: Impact Of Spatio-Temporal Coverage

Wireless visual sensor networks (VSNs) are expected to play a major role in future IEEE 802.15.4 personal area networks (PAN) under recently-established collision-free medium access control (MAC) protocols, such as the IEEE 802.15.4e-2012 MAC. In such environments, the VSN energy consumption is affected by the number of camera sensors deployed (spatial coverage), as well as the number of captured video frames out of which each node processes and transmits data (temporal coverage). In this paper, we explore this aspect for uniformly-formed VSNs, i.e., networks comprising identical wireless visual sensor nodes connected to a collection node via a balanced cluster-tree topology, with each node producing independent identically-distributed bitstream sizes after processing the video frames captured within each network activation interval. We derive analytic results for the energy-optimal spatio-temporal coverage parameters of such VSNs under a-priori known bounds for the number of frames to process per sensor and the number of nodes to deploy within each tier of the VSN. Our results are parametric to the probability density function characterizing the bitstream size produced by each node and the energy consumption rates of the system of interest. Experimental results reveal that our analytic results are always within 7% of the energy consumption measurements for a wide range of settings. In addition, results obtained via a multimedia subsystem show that the optimal spatio-temporal settings derived by the proposed framework allow for substantial reduction of energy consumption in comparison to ad-hoc settings. As such, our analytic modeling is useful for early-stage studies of possible VSN deployments under collision-free MAC protocols prior to costly and time-consuming experiments in the field.Comment: to appear in IEEE Transactions on Circuits and Systems for Video Technology, 201

Energy consumption of visual sensor networks: impact of spatio-temporal coverage

Wireless visual sensor networks (VSNs) are expected to play a major role in future IEEE 802.15.4 personal area networks (PANs) under recently established collision-free medium access control (MAC) protocols, such as the IEEE 802.15.4e-2012 MAC. In such environments, the VSN energy consumption is affected by a number of camera sensors deployed (spatial coverage), as well as a number of captured video frames of which each node processes and transmits data (temporal coverage). In this paper we explore this aspect for uniformly formed VSNs, that is, networks comprising identical wireless visual sensor nodes connected to a collection node via a balanced cluster-tree topology, with each node producing independent identically distributed bitstream sizes after processing the video frames captured within each network activation interval. We derive analytic results for the energy-optimal spatiooral coverage parameters of such VSNs under a priori known bounds for the number of frames to process per sensor and the number of nodes to deploy within each tier of the VSN. Our results are parametric to the probability density function characterizing the bitstream size produced by each node and the energy consumption rates of the system of interest. Experimental results are derived from a deployment of TelosB motes and reveal that our analytic results are always within 7%of the energy consumption measurements for a wide range of settings. In addition, results obtained via motion JPEG encoding and feature extraction on a multimedia subsystem (BeagleBone Linux Computer) show that the optimal spatiooral settings derived by our framework allow for substantial reduction of energy consumption in comparison with ad hoc settings

Correlation Estimation from Compressed Images

This paper addresses the problem of correlation estimation in sets of compressed images. We consider a framework where images are represented under the form of linear measurements due to low complexity sensing or security requirements. We assume that the images are correlated through the displacement of visual objects due to motion or viewpoint change and the correlation is effectively represented by optical flow or motion field models. The correlation is estimated in the compressed domain by jointly processing the linear measurements. We first show that the correlated images can be efficiently related using a linear operator. Using this linear relationship we then describe the dependencies between images in the compressed domain. We further cast a regularized optimization problem where the correlation is estimated in order to satisfy both data consistency and motion smoothness objectives with a modified Graph Cut algorithm. We analyze in detail the correlation estimation performance and quantify the penalty due to image compression. Extensive experiments in stereo and video imaging applications show that our novel solution stays competitive with methods that implement complex image reconstruction steps prior to correlation estimation. We finally use the estimated correlation in a novel joint image reconstruction scheme that is based on an optimization problem with sparsity priors on the reconstructed images. Additional experiments show that our correlation estimation algorithm leads to an effective reconstruction of pairs of images in distributed image coding schemes that outperform independent reconstruction algorithms by 2 to 4 dB