An Availability Metric and Optimization Algorithms for Simultaneous Coverage of Targets and Areas by Wireless Visual Sensor Networks

Costa, Daniel G.; Rangel, Elivelton O.; Peixoto, João Paulo Just; Jesus, Thiago C.

doi:10.1109/indin41052.2019.8972176

Cited by 4 publications

(10 citation statements)

References 26 publications

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“…Table 1 summarizes the reviewed articles related to spatial coverage quality. We can notice some works as Li et al [38] and Costa et al [35] addressing both target and area coverage. Li et al [38] propose an area coverage quality formulation to deal with a target coverage application, while Costa et al [35] propose a evaluation of simultaneous target and area coverage applications, addressing quality through both target and area coverage rate.…”

Section: Evaluating the Spatial Coverage Objectivesmentioning

confidence: 98%

“…The paramount quality metric in target coverage applications is the achieved number of covered targets, which defines the coverage rate, i.e., the percentage of the covered targets among all the targets [31][32][33][34][35]. Target coverage is commonly stated in terms of k-coverage, requiring that each target must be covered by at least k visual nodes [36,37].…”

Section: Target Coveragementioning

confidence: 99%

“…Since the monitored area is a continuous space, it is difficult to state which region has been covered by which visual sensors. Discrete solutions using grid approaches have been proposed to approximately compute area coverage for WVSN [6,17,35,38,[48][49][50]52]. That way, the area of interest can be divided into smaller regions, each one defined as identical rectangles.…”

Section: Area Coveragementioning

confidence: 99%

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A Survey on Monitoring Quality Assessment for Wireless Visual Sensor Networks

et al. 2022

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Wireless visual sensor networks have been adopted in different contexts to provide visual information in a more flexible and distributed way, supporting the development of different innovative applications. Although visual data may be central for a considerable set of applications in areas such as Smart Cities, Industry 4.0, and Vehicular Networks, the actual visual data quality may be not easily determined since it may be associated with many factors that depend on the characteristics of the considered application scenario. This entails several aspects from the quality of captured images (sharpness, definition, resolution) to the characteristics of the networks such as employed hardware, power consumption, and networking efficiency. In order to better support quality analysis and performance comparisons among different wireless visual sensor networks, which could be valuable in many monitoring scenarios, this article surveys this area with special concern on assessment mechanisms and quality metrics. In this context, a novel classification approach is proposed to better categorize the diverse applicable metrics for quality assessment of visual monitoring procedures. Hence, this article yields a practical guide for analyzing different visual sensor network implementations, allowing fairer evaluations and comparisons among a variety of research works. Critical analysis are also performed regarding the relevance and usage of the proposed categories and identified quality metrics. Finally, promising open issues and research directions are discussed in order to guide new developments in this research field.

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Section: Evaluating the Spatial Coverage Objectivesmentioning

confidence: 98%

Section: Target Coveragementioning

confidence: 99%

Section: Area Coveragementioning

confidence: 99%

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A Survey on Monitoring Quality Assessment for Wireless Visual Sensor Networks

et al. 2022

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“…In this case, each sensor node will be reorientated in order to compute the highest AQM . Actually, this is a simple way to improve the value of QoM, while keeping lower computational costs as compared with other approaches, although greedy algorithms may provide suboptimal results since they only evaluate local information [30]. The proposed Greedy approach is detailed in Algorithm 1.…”

Section: A Greedymentioning

confidence: 99%

FoV-Based Quality Assessment and Optimization for Area Coverage in Wireless Visual Sensor Networks

et al. 2020

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Wireless visual sensor networks are commonly employed on several applications contexts such as smart cities, intelligent transportation systems and industrial management, aiming at the use of visual data from cameras to provide enhanced information and to expand the networks utilities. In these scenarios, some applications may require high-definition images when performing more specialized tasks, for example in face and text recognition, adding an important monitoring requirement when using camera-based sensors. In fact, it is important to ensure that the network is able to gather visual data with the associated required quality to each task, and such perceived quality may be processed as a function of the Field of View (FoV) of the visual sensors. In order to address this issue, new quality metrics are proposed for wireless visual sensor networks that are deployed to perform area coverage, exploiting for that different perceptions of the FoV. Those metrics are proposed along with redeployment optimization methods for visual sensor nodes aiming at the improvement of the perceived monitoring quality, which are based on greedy and evolutionary-based approaches. The proposed metrics and algorithms are expected to be more realistic than previous solutions, allowing flexible processing of variables as cameras' positions, orientations and viewing angles, providing then high flexibility on the definition of parameters and significantly contributing to the development of sensor networks based on visual sensors. INDEX TERMS Wireless sensor networks, Area coverage, Optimization, Visual quality, Quality of monitoring, Quality metric, Visual sensing, Field of view, Mathematical modelling.

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“…The WVSN considered in this article is composed of a set of visual sensor nodes

, which are deployed over a two-dimensional area

. Each camera may assume different values for their parameters, however, without loss of generality, we consider in this work that all visual sensors are identical and so they are configured with the same values [ 21 ]. Each sensor is expected to be equipped with a camera, having a viewing angle

and an orientation

, as shown in Figure 1 , where the camera is represented by the small red circle.…”

Section: Definitions and Basic Conceptsmentioning

confidence: 99%

A Comprehensive Dependability Model for QoM-Aware Industrial WSN When Performing Visual Area Coverage in Occluded Scenarios

Jesus

Portugal

Costa

et al. 2020

Sensors

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In critical industrial monitoring and control applications, dependability evaluation will be usually required. For wireless sensor networks deployed in industrial plants, dependability evaluation can provide valuable information, enabling proper preventive or contingency measures to assure their correct and safe operation. However, when employing sensor nodes equipped with cameras, visual coverage failures may have a deep impact on the perceived quality of industrial applications, besides the already expected impacts of hardware and connectivity failures. This article proposes a comprehensive mathematical model for dependability evaluation centered on the concept of Quality of Monitoring (QoM), processing availability, reliability and effective coverage parameters in a combined way. Practical evaluation issues are discussed and simulation results are presented to demonstrate how the proposed model can be applied in wireless industrial sensor networks when assessing and enhancing their dependability.

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An Availability Metric and Optimization Algorithms for Simultaneous Coverage of Targets and Areas by Wireless Visual Sensor Networks

Cited by 4 publications

References 26 publications

A Survey on Monitoring Quality Assessment for Wireless Visual Sensor Networks

A Survey on Monitoring Quality Assessment for Wireless Visual Sensor Networks

FoV-Based Quality Assessment and Optimization for Area Coverage in Wireless Visual Sensor Networks

A Comprehensive Dependability Model for QoM-Aware Industrial WSN When Performing Visual Area Coverage in Occluded Scenarios

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