A survey of visual sensor network platforms

Tavlı, Bülent; Biçakcı, Kemal; Zilan, Ruken; Barceló-Ordinas, José M.

doi:10.1007/s11042-011-0840-z

Cited by 134 publications

(68 citation statements)

References 49 publications

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“…A. Mannasim -Network Simulator (NS-2) [Simulator I] The NS-2 is one of the most famous network simulators widely used in many researches. The NS-2 covers almost all of the IP protocols, but does not support all of the features needed to simulate a WSN [15], [16]. Specifically, the WSNs' characteristics that can be simulated by the NS-2 simulator are the following [15]- [17]:…”

Section: Simulator Selectionmentioning

confidence: 99%

“…The main features that Mannasim supports, and will be used as initial approach in this research work in order to develop different sensor node types, are the following [19], [20]:  Energy consumption for sensing and processing Another notorious simulator is OMNeT++ [15]- [17], [19] which simulates wired and wireless network. The components that uses are based on C++, although models use high-level language called NED1 (NEtwork Description language).…”

Section:  Energy Consumption Modelsmentioning

confidence: 99%

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An Analysis on the Impact of Video Traffic over Wireless Visual Sensor Network under Mannasim Sensor Network Architecture

Kanakaris¹,

Papakostas²,

Bandekas³

2016

IJET

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Abstract-The monitoring capability of a Wireless Sensor Network (WSN) constitutes an important feature making its application multidisciplinary. In a common WSN, the network handles only simple scalar data and thus it is sufficient for complex applications such as object detection, surveillance, image recognition, localization, and tracking. Therefore, in addition to normal sensing network infrastructures, the functionality of a WSN can be improved by adding vision capabilities. The existing hardware and software specifications of a typical WSN node are determined only for handling simple tasks. The main objective of this work is to extend the WSN to a Visual Sensor Network (VSN) by adding some visual sensor nodes in the traditional WSN infrastructure and study the video traffic impact to the overall performance with respect to different metrics. For these purposes, a sensor network with some Mica2 Motes with added visual sensors acting as cluster heads as well as routers was simulated herein. The generated data by the Mica Motes and the visual sensors nodes will be collected by separate sink nodes. The performance of the network will be studied with respect to different number of simultaneous video connections. The impact of video traffic over the scalar data flow as well as the overall performance will be analyzed with suitable metrics.

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Section: Simulator Selectionmentioning

confidence: 99%

Section:  Energy Consumption Modelsmentioning

confidence: 99%

An Analysis on the Impact of Video Traffic over Wireless Visual Sensor Network under Mannasim Sensor Network Architecture

Kanakaris¹,

Papakostas²,

Bandekas³

2016

IJET

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“…Traditionally, in WMSN platform designs [25], [26], [27], [28] these two limitations are viewed as a competing for a fixed energy budget (e.g., energy per video frame). Channel errors are compensated for by increasing the transmission power.…”

Section: • Predictive Encoding Of Video Increases the Impact Ofmentioning

confidence: 99%

A Tutorial on Encoding and Wireless Transmission of Compressively Sampled Videos

Pudlewski

Melodia

2013

IEEE Commun. Surv. Tutorials

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Abstract-Compressed sensing (CS) has emerged as a promising technique to jointly sense and compress sparse signals. One of the most promising applications of CS is compressive imaging. Leveraging the fact that images can be represented as approximately sparse signals in a transformed domain, images can be compressed and sampled simultaneously using low-complexity linear operations. Recently, these techniques have been extended beyond imaging to encode video. Much of the compression in traditional video encoding comes from using motion vectors to take advantage of the temporal correlation between adjacent frames. However, calculating motion vectors is a processingintensive operation that causes significant power consumption. Therefore, any technique appropriate for resource constrained video sensors must exploit temporal correlation through lowcomplexity operations.In this tutorial, we first briefly discuss challenges involved in the transmission of video over a wireless multimedia sensor network (WMSN). We then discuss the different techniques available for applying CS encoding first to images, and then to videos for error-resilient transmission in lossy channels. Existing solutions are examined, and compared in terms of applicability to wireless multimedia sensor networks (WMSNs). Finally, open issues are discussed and future research trends are outlined.

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“…At the high-end, web-cams can capture high-resolution video at full frame rate while consuming 200mW, whereas Pan-Tilt-Zoom cameras are re-targetable sensors that produce high quality video while consuming 1W. It is noticeable that the mentioned power amounts are the power consumed by the camera sensors without considering the power consumed by the host motes, see (Tavli et al, 2011) for a survey of visual network platforms. On the other hand, given the large amount of data generated by the multimedia nodes, both processing and transmitting image data are quite costly in terms of energy, much more so than for other types of sensor networks.…”

Section: Multimedia Sensing Subsystemmentioning

confidence: 99%

“…The assumption of fixed lenses is based on the current WMSN platforms (Tavli et al, 2011). Almost all of them (SensEye, MicrelEye, CITRIC, Panoptes, Meerkats) (Kulkarni et al, 2005;Kerhat et al, 2007;Chen et al, 2008;Feng et al, 2005;Margi et al, 2006) have fixed lenses and only high powered PTZ cameras have movement capabilities.…”

Section: Preliminarymentioning

confidence: 99%