Path optimization under minimum delay constarint for unmanned aerial vehicles

Nowadays, access to information is a very important need. This need can also be met by the applications of wireless communication systems in various fields. Unmanned Aerial Vehicles (UAVs), which has an significant place in studies in academia and industry, not only makes people's lives easier in many areas, but also have various applications in the field of wireless communication. Within the scope of this thesis, accessing information with a UAV-assisted system in sensor networks for time-critical applications has been examined. Data collection with UAV is a new technology with many practical applications. The data collected by the UAV should be brought to the data center where it will be used as quickly as possible. In academic studies, the delay in bringing the data collected by the UAV to the data center is entered into problems with the Age of Information (AoI) parameter as a objective or constraint. In this study, AoI is used as a performance metric that affects both communication time and flight time. Instead of the idea that the UAV, which is very common in the literature, travels the sensors one by one and performs the data collection process, the sensors are discussed in groups in this study. In this study, dual sensor groups were studied to see the effect of grouping idea on performance. Starting from the data center, the UAV aims to hover at one point on the line segments formed between the sensors in the groups, collect data via multiple access channels and then return to the data center. With the formation of dual sensor groups, the UAV will not be able to visit each sensor separately, so it will be able to follow a shorter path in its trajectory and reach shorter communication times with multiple access channel communication. Two trajectory design algorithms are proposed to minimize the maximum AoI in the sensors. In both algorithms, the UAV communicates with the sensors via multiple access channel. In the first algorithm, the UAV hovers on the line segment at the optimal point of communication time and performs data collection. In the second algorithm, the UAV hovers on the path length optimal locations and collects data by hovering. Algorithms are compared with time-sharing communication and single user scenarios. The results reveal that the trajectory design with multiple access communication optimal locations is better when the sensors are not too far apart and the packet lengths are long. On the other hand, path length optimal trajectory is better when the packet lengths are short.