Trajectory and Resource Optimization in OFDM-Based UAV-Powered IoT Network

Abstract

The Internet of Things (IoT) is playing an increasingly vital role in multiple industries and our everyday life. A pressing practical problem of IoT devices (IoT-Ds) is their explosive growth of connectivity which leads to large energy consumption. One of the most promising solutions to achieve a sustainable green IoT network is unmanned aerial vehicle (UAV) enabled wireless power transfer (WPT) due to its flexibility, mobility and cost advantage. In this paper, we propose an UAVpowered IoT network based on Orthogonal Frequency Division Multiplexing (OFDM). In the proposed network, two ground nodes (GNs) are powered by two UAVs through down link WPT. In the uplink, the data collected by GNs are transmitted to the corresponding UAVs with the harvested energy by utilizing orthogonal subcarriers, which can effectively avoid the interference. UAVs' trajectories and resource allocation are optimized to maximize the sum average transmission rate of two GNs while ensuring the minimum average transmission rate of each GN. In this paper, we utilize successive convex programming (SCP) technique to solve the proposed optimization problem. Simulation results show that our proposed scheme achieves larger sum average transmission rate than the benchmark schemes.

Secure NOMA-Based UAV-MEC Network Towards a Flying Eavesdropper

“…(37) Note that the convex function can be obtained by the global lower bound with the first-order Taylor expansion [5]. In Lemma 2, the lower bound of…”

“…Pham et al illustrated that the computation cost of the system can be reduced and the computation capacity can be improved by dividing the backhaul bandwidth and allocating the computing resource to MEC networks [4]. Unmanned aerial vehicle (UAV) with high mobility and low cost can quickly provide an efficient emergency and auxiliary means for internet-of-thing (IoT) deployment in the remote areas [5]- [6]. Mozaffari et al proposed a novel framework to maximize the average number of bits transmitted to the users by finding the optimal cell partitions associated to the UAVs under a fair resource allocation policy with given the maximum possible flight time of UAVs [6].…”

Joint Wireless Resource and Computation Offloading Optimization for Energy Efficient Internet of Vehicles

“…Alternatively, the maximization of the throughput is the focus of the research works presented in [20]- [22] The authors in [20] investigated the resource allocation in networks consisting of unmanned aerial vehicles and formulated a mixedinteger non-linear problem, aiming to maximize the average throughput while satisfying energy constraints. In addition, Ning et al [21] leveraged non-orthogonal multiple access and MEC technologies to develop a method that maximizes the link throughput, by optimizing power allocation, subchannel assignment, and task assignment.…”

“…In addition, Ning et al [21] leveraged non-orthogonal multiple access and MEC technologies to develop a method that maximizes the link throughput, by optimizing power allocation, subchannel assignment, and task assignment. Furthermore, Lu et al [22] considered a network, where two unmanned aerial vehicles (UAVs) provide wireless power transfer to two ground devices and developed a solution based on successive convex programming in order to maximize the sum average transmission rate.…”

“…To find the optimal power and bandwidth allocation, we employ the Lagrange multiplier and subgradient methods. Consequently, the respective Lagrangian of P2 is obtained by (22) shown at the top of the next page. In (22), set X p,w = {β n , λ n , µ, π n } denotes the non-negative Lagrange multipliers.…”