Spread Spectrum Visual Sensor Network Resource Management Using an End-to-End Cross-Layer Design

Bentley, Elizabeth Serena; Kondi, Lisimachos P.; Matyjas, John D.; Medley, Michael J.; Suter, Bruce W.

doi:10.1109/tmm.2010.2086441

Cited by 21 publications

(29 citation statements)

References 14 publications

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“…The presented framework can be run on any Linux-based platform supporting OpenCV, regardless of the type of processor (X86 or ARM). It also In case the latter is used, the framework also provide support for TinyOS-compliant transceivers 1 . Additional details on the tested hardware configurations are given in Section IV.…”

Section: Ez-vsn System Architecturementioning

confidence: 99%

“…On the other hand, being related to the more general Wireless Sensor Networks, VSNs have tight constraints in terms of computational, communication and energy resources. Thus, a huge part of the research on VSNs has been committed to finding efficient software solutions at every layer of the protocol stack, ranging from low-level communication protocols [1], [2], to multimedia compression schemes at the application layer [3], [4], [5].…”

Section: Introductionmentioning

confidence: 99%

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EZ-VSN: An Open-Source and Flexible Framework for Visual Sensor Networks

Bondi

Baroffio

Cesana

et al. 2016

IEEE Internet Things J.

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Abstract-We present a complete, open-source framework for rapid experimentation of Visual Sensor Networks (VSN) solutions. From the software point of view, we base our architecture on open-source and widely known C++ libraries to provide the basic image processing and networking primitives. The resulting system can be leveraged to create different types of VSNs, characterized by the presence of multiple cameras, relays and cooperator nodes, and can be run on any Linux-based hardware platform, such as the BeagleBone Black. To demonstrate the flexibility of the proposed framework, we describe two different application scenarios typical of VSNs, namely object recognition and parking monitoring. The framework is then used to evaluate the benefits of two complementary paradigms for networked visual analysis recently discussed in the literature. In the traditional Compress-then-Analyze (CTA) paradigm, compressed images are transmitted from camera nodes to a central controller, where they are analyzed. In the novel Analyze-then-Compress (ATC) paradigm, camera nodes extracts and compress local features from the acquired images. Such features are transmitted to the central controller and used to perform visual analysis. We show that the ATC paradigm outperforms CTA from the consumed energy point of view, at the same target analysis accuracy in both the application scenarios.

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Section: Ez-vsn System Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

EZ-VSN: An Open-Source and Flexible Framework for Visual Sensor Networks

Bondi

Baroffio

Cesana

et al. 2016

IEEE Internet Things J.

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“…It can be shown that, using Eq. (1), Viterbi's upper bound for P b , and the URDC model, the expected video distortion of node k can be expressed as a function of the source and channel coding rates of node k and the power levels of all nodes [2]. Thus, the expected video distortion can be written as E{D s+c,k }(R s,k , R c,k , S).…”

Section: The Nash Bargaining Solutionmentioning

confidence: 99%

“…An appropriate optimization criterion that considers the qualities of the videos of all nodes should be used. In previous work [1,2], we proposed cross-layer optimization schemes that minimize either the average video distortion of all nodes, or the maximum video distortion among the nodes.…”

Section: Introductionmentioning

confidence: 99%

Optimal power allocation and joint source-channel coding for wireless DS-CDMA visual sensor networks using the Nash Bargaining Solution

Pandremmenou

Kondi

Parsopoulos

2011

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We consider the problem of resource allocation for a Direct Sequence Code Division Multiple Access (DS-CDMA) wireless visual sensor network (VSN). We use the Nash Bargaining Solution (NBS) from game theory in order to determine the transmission power and source and channel coding rate for each node. The NBS assumes that the nodes negotiate (using the help of a centralized control unit) in order to jointly determine their transmission parameters. The transmission powers are allowed to take continuous values, whereas the source and channel coding rate combination can only assume discrete values. Thus, the resulting optimization problem is a mixedinteger optimization task and is solved using Particle Swarm Optimization (PSO). Experimental results are provided and conclusions are drawn.

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“…In our previous works [2], [3], we assumed a wireless VSN, where an application-driven cross-layer optimization 978-1-4673-5807-1/13/$31.00 ©2013 IEEE scheme was proposed for the dynamic adjustment of the sensor nodes' transmission parameters across all network layers. Such a scheme provides the opportunity for increased network resource usage and user profit maximization, at the same time.…”

Section: Introductionmentioning

confidence: 99%

Resource allocation in visual sensor networks using a reinforcement learning framework

Pandremmenou

Tziortziotis

Kondi

et al. 2013

2013 18th International Conference on Digital Signal Processing (DSP)

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In recent years, video delivery over wireless visual sensor networks (VSNs) has g ained increasin g attention. The lossy compression and channel errors that occur durin g wire less multimedia transmissions can de g rade the quality of the transmitted video sequences. This paper addresses the problem of cross-layer resource allocation amon g the nodes of a wireless direct-sequence code division multiple access (DS-CDMA) VSN.The optimal g roup of pictures (GoP) len g th durin g the encodin g process is also considered, based on the motion level of each video sequence. Three optimization criteria that optimize a different objective function of the video qualities of the nodes are used. The nodes' transmission parameters, i.e., the source codin g rates, channel codin g rates and power levels can only take discrete values. In order to tackle the resultin g optimization problem, a reinforcement learning (RL) strate g y that promises efficient exploration and exploitation of the parameters' space is employed. This makes the proposed methodolo g y usable in lar g e or continuous state spaces as well as in an online mode.Experimental results hi g hli g ht the efficiency of the proposed method.

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Spread Spectrum Visual Sensor Network Resource Management Using an End-to-End Cross-Layer Design

Cited by 21 publications

References 14 publications

EZ-VSN: An Open-Source and Flexible Framework for Visual Sensor Networks

EZ-VSN: An Open-Source and Flexible Framework for Visual Sensor Networks

Optimal power allocation and joint source-channel coding for wireless DS-CDMA visual sensor networks using the Nash Bargaining Solution

Resource allocation in visual sensor networks using a reinforcement learning framework

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