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

Abstract

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.

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

“…The dependability assessment methodology proposed in this article is aimed at solving an important quality evaluation gap in critical applications that employ scalar and visual sensors to perform monitoring tasks. For that, we integrate and unify some previous methodologies [ 5 , 10 , 11 ], achieving a more complete mathematical solution for such scenarios. In this way, the proposed methodology for dependability evaluation considers different visual coverage failures, which occur when the required area coverage or quality of monitoring level are not fulfilled, either by an incorrect deployment or by occlusion.…”

“…In Reference [ 5 ], QoM is addressed extrapolating several deployment constraints imposed in Reference [ 6 ], such as cameras’ position, orientation and viewing angle. New QoM-based metrics are proposed, allowing new researches related to optimization, comparison or exploitation of different quality aspects, such as redundancy and dependability.…”

“…The farther is the monitored region from the camera, the lower is the level of details related to that region in captured images, and consequently, the lower should be the amount of visual information extracted from that region. The QoM modeling used as reference in this article was initially proposed in Reference [ 5 ], being summarized next.…”

“…When assessing the dependability of wireless visual sensor networks in industrial environments, in a broader perspective, the concept of Quality of Monitoring (QoM) can be defined. The QoM refers to the capability of a network to perform the expected monitoring functions over a region of interest [ 5 ]. In other words, it is not only considered the amount of retrieved visual information when assessing quality, but also definition and sharpness parameters [ 6 ].…”

BikeWay: A Multi-Sensory Fuzzy-Based Quality Metric for Bike Paths and Tracks in Urban Areas

“…Generally speaking, such quality assessment can be performed concerning the networking and sensing structure in different scenarios [21], evaluating the efficiency of the employed protocols and algorithms. Sensing accuracy has been also considered when assessing quality, with some interesting results when concerning more complex sensing units such as cameras [22]. Such metrics can give important indications of how a system will behave in a smart city scenario, indirectly impacting its perceived quality.…”