Performance Analysis of UAVassisted Ferrying for the Internet of Things IoT sensor networks are applied in several areas, including research, security, and monitoring. All these applications are based on data collection. A practical solution is a use of unmanned aerial vehicles (UAVs) as aerial relays to ensure connectivity between IoTdevices and the destination node. Most applications have been based on using a single UAV to transmit information, while our work focuses on forming a network of multiple UAVs. In this paper, a novel deployment of UAVs moving along nonconcentric rings is analyzed. First, they collect packets generated by IoTdevices and then transmit them throughout a UAVtoUAV forwarding schema to the destination. We derive a closed formula for the endtoend throughput of the proposed gathering network architecture.

3D Trajectory Optimization in Rician Fading for UAVEnabled Data Harvesting In this paper, we consider a UAVenabled WSN where a flying UAV is employed to collect data from multiple sensor nodes (SNs). Our objective is to maximize the minimum average data collection rate from all SNs subject to a prescribed reliability constraint for each SN by jointly optimizing the UAV communication scheduling and threedimensional (3D) trajectory. Different from the existing works that assume the simplified lineofsight (LoS) UAVground channels, we consider the more practically accurate angledependent Rician fading channels between the UAV and SNs with the Rician factors determined by the corresponding UAVSN elevation angles. However, the formulated optimization problem is intractable due to the lack of a closedform expression for a key parameter termed effective fading power that characterizes the achievable rate given the reliability requirement in terms of outage probability. To tackle this difficulty, we first approximate the parameter by a logistic (u0027Su0027 shape) function with respect to the 3D UAV trajectory by using the data regression method. Then the original problem is reformulated to an approximate form, which, however, is still challenging to solve due to its nonconvexity. As such, we further propose an efficient algorithm to derive its suboptimal solution by using the block coordinate descent technique, which iteratively optimizes the communication scheduling, the UAVu0027s horizontal trajectory, and its vertical trajectory. The latter two subproblems are shown to be nonconvex, while locally optimal solutions are obtained for them by using the successive convex approximation technique. Last, extensive numerical results are provided to evaluate the performance of the proposed algorithm and draw new insights on the 3D UAV trajectory under the Rician fading as compared to conventional LoS channel models.