Autonomous aerial vehicle (AAV) crowdsensing, as a complement to mobile crowdsensing, can provide ubiquitous sensing in extreme environments and has gathered significant attention in recent years. In this article, we investigate the issue of sensing data storage in AAV crowdsensing without edge assistance, where sensing data is stored locally in the AAVs. In this scenario, replication scheme is usually adopted to ensure data availability, and our objective is to find an optimal replica distribution scheme to maximize data availability while minimizing system energy consumption. Given the NP-hard nature of the optimization problem, traditional methods cannot achieve optimal solutions within limited timeframes. Therefore, we propose a centralized training and decentralized execution deep reinforcement learning (DRL) algorithm based on actor-critic, named “MUCRS-DRL.” Specifically, this method derives the optimal replica placement scheme based on AAV state information and data file information. Simulation results show that compared to the baseline methods, the proposed algorithm reduces data loss rate, time consumption, and energy consumption by up to 88%, 11%, and 11%, respectively.

Visual Crowdsensing (VCS) has gradually become an emerging research field as the built-in cameras of smart mobile devices have become a common recording tool in daily life. To meet the task requirements of VCS applications in the sensing process, sensing platforms usually use distributed data acquisition to collect image data from different sources. However, this leads to a large amount of redundant data in the final collected data set, which seriously affects the data quality. To solve the above problems, this paper proposes a dynamic redundancy detection method (VCSRD) for visual crowdsensing data based on contrastive learning. The method fuses the multimodal information of metadata and visual content through comparative clustering to realize the redundancy detection of data. Not only improves the accuracy of redundant data detection but also has some flexibility for data under different tasks. The effectiveness and flexibility of the proposed method under different data sizes are verified through experiments on real image datasets. Compared to the baseline method, VCSRD shows superior performance on all three clustering metrics.

Sparse Mobile Crowdsensing (Sparse MCS) is an emerging paradigm for data collection that significantly reduces the cost of smart city data acquisition while ensuring data quality by collecting data only from a subset of sensing cells and inferring data from the remaining unsensed cells using spatial and temporal correlations. Existing research has primarily focused on single sensing task scenarios, yet practical applications often involve multiple types of sensor data. These data types exhibit correlations that can be leveraged to optimize task allocation, thereby reducing sensing costs. This paper addresses the task assignment problem in multi-task scenarios, proposing the MDIA method based on information entropy theory and similarity matrices to finely assess the importance of data points. Building upon this, we further propose the MTA method, which employs a hybrid sensing mode (combining participatory sensing and opportunistic sensing) to select appropriate participants for sensing critical areas. Extensive experiments conducted on two real-world sensing datasets containing multiple data types validate the effectiveness of the proposed methods.

Voluntary Computing systems or Desktop Grids (DGs) enable sharing of commodity computing resources across the globe and have gained tremendous popularity among scientific research communities. Data management is one of the major challenges of adopting the Voluntary Computing paradigm for large data-intensive applications. To date, middleware for supporting such applications either lacks an efficient cooperative data distribution scheme or cannot easily accommodate existing data-intensive applications due to the requirement for using middleware-specific APIs.To address this challenge, in this paper we introduce Gator-Share, a data management framework that offers a file system interface and an extensible architecture designed to support multiple data transfer protocols, including BitTorrent, based on which we implement a cooperative data distribution service for DGs. It eases the integration with Desktop Grids and enables high-throughput data management for unmodified data-intensive applications. To improve the performance of BitTorrent in Desktop Grids, we have enhanced BitTorrent by making it fully decentralized and capable of supporting partial file downloading in an on-demand fashion.To justify this approach we present a quantitative evaluation of the framework in terms of data distribution efficiency. Experimental results show that the framework significantly improves the data dissemination performance for unmodified data-intensive applications compared to a traditional client/server architecture.