# Distributed Visual Crowdsensing Framework for Area Coverage in Resource Constrained Environments
## 1 Introduction  
People nowadays use smartphones extensively in their daily lives. More and more crowdsensing solutions are being researched as a result of advanced sensing capabilities integrated within mobile devices. Visual crowdsensing (VCS) applications, which employ the built-in cameras of smartphones to provide more detailed information (i.e., images or videos) about the sensing region, have recently attracted the attention of respective research groups [1]. Visual data has been incorporated into a wide range of new applications, including visual tracking and the Internet of multimedia things. Visual information, basedon the extensive knowledge that can be gathered from it, can undoubtedly influence the performance of many applications. As a result, VCS has broadened the range of mobile crowdsensing applications. Flexsensing [2], LuckyPhoto [3], SmartPhoto [4], Picpick [5], FlierMeet [6], and CrowdTracking [7] are examples of previous projects that demonstrate that VCS is useful and, in many cases, superior to traditional visual sensing solutions that rely on the deployment of stationary cameras for monitoring. The VCS paradigm differs from traditional mobile crowdsensing in its nature. When opposed to scalar data, visual data typically requires more data processing and bandwidth to convey. Furthermore, data redundancy is regarded as one of the most significant issues in VCS. For example, in disaster recovery applications, making the most out of limited resources to collect the most useful photos from the public is a challenge. Methods for detecting and eliminating redundant data provide a number of advantages, including lower transmission costs and overall energy consumption. There are two types of redundancy in data: content redundancy and context redundancy. Visual similarity is quantified in content redundancy using visual features and color histogram [8], whereas, in context redundancy, it is quantified using location or shooting angle information. Moreover, the geographical and geometrical details are utilized to check the similarity of the photographs in context redundancy. For example, different buildings may have similar visual features, but there is no context resemblance since their locations are different. In [9; 10], content photodetection algorithms were used to confirm the similarity of photo contents. On the other hand, mobile devices have limited computing capacity and energy to run such algorithms in disaster recovery circumstances. As a result, context-based solutions are more suitable for disaster recovery scenarios. Nonetheless, depending on the requirements of the VCS applications, redundant context data detection may vary according to the type of visual information that needs to be covered. One-way coverage of the point of interest (PoI) is required in classical visual sensor networks. In some cases, multiple aspects or \(k\)-views of the same PoI should be observed from various directions. In other words, redundancy detection is dependent on the type of coverage required in the VCS application. For instance, if the goal is to cover the building regardless of which direction it is covered and there are two photographs of the same building (i.e., content similarity), then these two images are redundant. They may not be redundant, however, if the task needed is to provide a full view of the building. Previous works [11; 12] presented a framework for a full view of PoIs, in which each PoI within the target area must be covered from all directions. On the other hand, the authors of [13] developed a framework for full-view sub-regions, in which each sub-region should be covered from all directions. It is worth noting that most existing VCS systems are proposed for full-view point coverage [13]. On the other hand, the existing systems for full-view area coverage use a centralized framework, assuming all sensor nodes in the network communicate with a server that uses a photo redundancy elimination method based on global knowledge of the geometric locations of all nodes' photos.