Age of Information minimization in UAV-aided data collection for WSN and IoT applications: A systematic review

The use of unmanned aerial vehicles (UAVs) for data gathering in wireless sensor networks (WSNs) and Internet of Things (IoT) applications has significantly gained interest in recent years. This shift is mainly attributed to the fast mobility and manoeuvrability of UAVs to deliver sensed data (by sensors and/or IoT devices) in remote, rural, or urban areas to the required destination (such as base stations or data centers). Age of Information (AoI) is a recent metric that measures the degree of freshness of information collected in data gathering applications, and in this context, data sensed by terrestrial sensing devices and transported by the UAV. Many researchers have thus focused on techniques to minimize AoI, mainly using machine learning and optimization methods. The research in this area is fast-growing, and some of the models studied are becoming more fine-grained, thus increasing the need for a comprehensive review of the proposed solutions, peculiar aspects, and diverse assumptions to draw lessons, identify challenges, and map out future considerations. This motivates the comprehensive study conducted in this paper, whereby 45 articles were meticulously selected from the Scopus database and systematically filtered to the 20 most relevant articles on AoI minimization for UAV-assisted data gathering in WSN and IoT applications. This paper provides a comprehensive review of problems and problem-solving solutions, detailed assumptions, algorithms, constraints, joint optimization objectives, metrics, and influencing factors on information freshness in UAV-assisted WSN/IoT. Optimal design of UAV trajectory, efficient UAV and sensor node/IoT device scheduling, and improved UAV energy source acquisition were also identified as some of the most preponderant themes surrounding AoI minimization. Consequently, a comprehensive discussion on AoI-optimal trajectory designs in different WSN/IoT architectural setups, namely clustered and non-clustered environments, has been presented. The outcomes of this systematic review include the categorization of the issues and proposed solutions, as well as tools and methods, joint optimization approaches, metrics, and UAV/SN-related assumptions from the reviewed articles. Finally, lessons learned, design considerations, challenges, and future directions have also been discussed.