# Distributed Visual Crowdsensing Framework for Area Coverage in Resource Constrained Environments
## 1 Introduction
In VCS, there is currently no structure for removing duplicates between photographs based on full-view area coverage. On the other hand, VCS solutions are crucial in disaster assistance or emergency circumstances since they provide real-time and detailed information about the target area. VCS systems should appropriately address various limitations in such critical situations, including restricted bandwidth and disrupted communication with the command center or server. In this paper, we present a distributed VCS system for full-view area coverage that addresses interrupted communication with the command center. The nodes within the target area make use of free transmissions such as Wi-Fi and Bluetooth to share photos. The nodes not only store their own acquired images but they may also store shared images received from other sensor nodes, which duplicates and expands the amount of data stored on each sensor node. As a result, if any node can communicate with the server, then more photos of the target region will be available to the command center. This condition allows the command center to learn more about the target area and, as a result, take appropriate actions to deal with difficult situations. For such scenarios, many routing schemes were proposed, such as routing protocols in delay-tolerant networks [11; 12] and mobile opportunistic networks [14]. The contributions of this work can be summarized as follows:  
1. Proposing a distributed VCS framework for full-view area coverage in resource-constrained environments. In the proposed approach, the target area is divided into virtual sub-regions, each of which is represented by a set of boundary points of interest (BoI). An appropriate pattern of Bols that can be used to investigate the full-view area coverage is found. The area is full-view covered if each Bol has full-view coverage in all \(k\)-view directions around the full scope. To the best of our knowledge, this is the first distributed framework in VCS to investigate full-view area coverage by utilizing a set of Bols rather than the target region's continuous domain or using dense grid methods. Because there are infinite points inside the target area, mobile devices have limited processing ability and energy to investigate the area coverage utilizing the continuous domain of the target region. The BoI scheme is proposed to investigate the full-view area coverage for applications with limited resources for this aim. 2. Developing a clustering scheme to represent the geometric coverage conditions of each photo in VCS, based on the full-view area coverage conditions used in traditional camera sensor networks (CSN). Although there are data redundancy removal algorithms for full-view area coverage in CSN [15; 16], these approaches are ineffective for VCS since users take different images from different positions each time. For instance, the authors in [15] proposed an approach that considers fixed locations of sensors and thus performs data redundancy elimination based on predefined locations. 3. Developing adaptive photo redundancy elimination procedures based on the coverage of each photo in the target area. This allows the distributed VCS system to select the minimum number of photos with maximum area coverage. As a result, each sensor node can filter redundant photos without requiring high computational capabilities or a global understanding of all photos from all sensor nodes inside the target area. The proposed procedures prevent sending repeated photos across sensor nodes, which saves bandwidth and energy.