# Distributed Visual Crowdsensing Framework for Area Coverage in Resource Constrained Environments
## 2 Related Works  
There are many applications of VCS, such as environmental monitoring, scientific data collection, public information gathering, and target area sensing [1; 6; 17]. In VCS, visual data typically requires more data processing and bandwidth to upload photos. In [13], a centralized VCS solution for full-view area coverage is proposed to address this issue. The metadata for all current photographs is uploaded first by the sensor nodes. The server next analyzes the photographs' metadata to determine the overall photo utility (i.e., how much and to what degree each photo covers the target region) and which set of photos should be uploaded to the server. They first used a centralized procedure to divide the coverage area of all images into sub-regions, with each sub-region being covered by the same collection of photos as in prior works [15]. The server then selects photographs with higher photo utility value and only requests valuable photos from the nodes using the utility area function. As a result, nodes upload the images with the highest added value and reduce the uploading of redundant photos. Similar to [13], a multi-coverage picture collection framework was proposed in [18] as a server-side scheme. They devised a pyramid-tree-based approach for dynamically clustering photographs and adapting to crowd-captured photo changes. To discover redundant photographs, the server does dynamic clustering of photos. Using the Ptree model, each photo is represented by a clustering object using a series of semantic and visual criteria to remove redundant photos. The work in [19] is similar to [13], where the metadata of the photos is first sent to the server and then the server analyzes the photos and selects the most valuable photos. All the proposed schemes in [13; 18; 19] adopted a centralized approach and considered the availability of constant contact between the nodes and the server side, which is not the ideal case in emergency scenarios such as a disaster. Several distributed systems were proposed to address the interrupted contact with the server side. In [12], a PicTree model is generated by each node to cluster its photo storage. When two nodes get to communicate with each other, each one should share its PicTree with the other. The tree fusion mechanism was proposed to allow the PicTrees of the two nodes to merge. They can cluster the photographs and remove the redundant photos between the two sensor nodes after fusing the two PicTrees of the two sensor nodes. The redundant photographs are those that match in all layers and have the same features. As a result, each node knows which photo needs to be sent to the other node and which picture needs to be received. In [20], the authors proposed a distributed framework for processing images captured by crowds within the target area following a natural disaster. The goal of this work is to reduce the overhead of processing photos on the server side. They, however, classified the photos based on their content features using image processing, which required more processing power. The work in [11] is an example of distributed photo coverage in VCS using a fixed list of Pols. For a dynamic list of Pols, the authors in [21; 22] proposed distributed photo crowdsourcing in delay-tolerant networks. They considered the changing and dynamic nature of events, and as a result, they offered a photo framework selection that adapts to changing list of Pols. They included a mechanism for adding new Pols, updating existing Pols, and removing existing Pols. However, they considered the whole view of points but not the full-view coverage of the area. The main challenge is to determine whether the target area is covered from all directions. The full-view coverage area was lowered to full-view coverage of points in CSN. In [15], the authors looked at the geometric metadata of the camera nodes as well as the geometric full-view coverage area conditions inside the target area to divide it into sub-regions, each with an identical combination of sensors and coverage conditions.