Research On Resource Allocation For UAV-assisted Emergemcy IoT Networks

In recent years,natural disasters and its derivate disasters have caused great damage to human beings,property and environment.When a disaster event accurs,the Internet of Things(Io T)system can obtain the relevant information of disaster areas in real time,and then send to the control center.This leads to the improvement of emergency response speed and the efficiency of disaster relief.However,Io T-enabled mobile networks are generally composed of Io T devices,which collect,transmit,process massive information,and face particular challenges including communication disruptions and energy-limited devices.To overcome these two challenges,an unmanned aerial vehicle(UAV)can dispatch as a flying base station,and can be used for rapid development in disaster areas to provide wireless services(i.e.,communication,wireless power transfer(WPT)and computing)due to advantages of portability,wide coverage area and mobility.In this paper,we establish an emergency communications framework for UAV-assisted Io T networks,in order to provide wireless coverage for Io T devices.For the four typical application scenarios,we propose resource allocation schemes for UAV-assisted emergency Io T networks,and build a platform for UAVassisted Io T systems in data transamission.The main contributions of this paper are listed as follows.(1)For the scenario with power constrained Io T devices in disaster areas,the joint beamforming design and resource allocation scheme for UAV-enabled WPT networks is proposed.In particular,we aim to maximize the energy harvested at all Io T devices by considering the UAV’s 3D placement,beampattern and charging time.To solve this problem,we first exploit the sequential unconstrained convex minimization(SUCM)based algorithm to obtain the optimal UAV 2D position.Then,we propose the multiobjective evolutionary algorithm based on decomposition(MOEA/D)based algorithm to control the phase of antenna array elements.Finally,we employ the branch and bound(B&B)method to design the UAV trajectory which can be constructed as a traveling salesman problem(TSP)to minimize flight distance.Numerical results demonstrate that significant performance gain in terms of sum received power can be achieved by the proposed algorithms.(2)For the scenario with massive connections in disaster areas,a UAV-aided multipleinput-multiple-output(MIMO)non-orthogonal multiple access(NOMA)network is proposed.Different from the traditional multiple access schemes,this paper considers the combination of MIMO-NOMA systems and UAV-enabled communication networks,which can increase the connection density and coverage area.In particular,we aim to maximize the sum rate by jointly optimizing the 3D placement of the UAV,beam pattern and transmit power.To tackle this problem,we first convert the non-convex problem into a total path loss minimization problem,and hence the optimal 3D placement of the UAV can be achieved via standard convex optimization techniques.Then,the MOEA/D based algorithm is proposed for the shaped-beam pattern synthesis of an antenna array.Finally,by using the Karush-Kuhn-Tucker(KKT)conditions and fraction programming(FP),we propose two power optimization schemes.Numerical results validate that the proposed algorithms achieve a significant performance gain in terms of sum rate for all Io T devices,as compared with the orthogonal frequency division multiple access(OFDMA)scheme.(3)For the scenario with computation-intensive applications in disaster areas,a resource scheduling scheme for UAV-aided wireless powered mobile edge computing(MEC)networks is proposed to provide computing services for Io T devices.In particular,our aim is to maximize the sum computation rate at all Io T devices whilst satisfying the constraints of energy harvesting and coverage.To address this problem,by applying the polyhedral annexation method and semi-definite relaxation(SDR),we develop an effective algorithm to derive the closed-form solution for the optimal 3D deployment of the UAV,and obtain the solution for the hybrid beamformer.Two resource allocation algorithms for partial and binary offloading patterns are thereby proposed.Numerical results verify that our designed algorithms achieve a significant computation performance enhancement as compared to the benchmark schemes.(4)For the scenario with cell-edge Io T devices in disaster areas,a resource allocation scheme for reconfigurable intelligent surface(RIS)-assisted multi-UAV networks is proposed.Different from relay systems,we construct the “virtual” line-of-sight(Lo S)links between UAVs and Io T devices by using a RIS,which can decrease the consumed power of the system.In particular,our objective is to minimize the power consumption of the system while meeting the constraints of minimum data rate for users and minimum inter-UAV distance.To solve this problem,we first obtain the solutions of beamforming vectors and UAV’s placement via the maximum ratio transmission(MRT)and successive convex approximation(SCA).By using the Gaussian randomization procedure,we yield the closed-form expression for the RIS reflection coefficients.Finally,a dynamic-order decoding scheme is proposed to optimize the NOMA decoding order in order to guarantee fairness among Io T devices.Simulation results verify that the joint UAV deployment and resource allocation scheme can effectively reduce the total power consumption compared to the benchmark methods.(5)Based on the above resource allocation schemes,a platform for UAV-assisted Io T system in data transmission is constructed.In particular,this system is composed of UAV platform and mission platform.To minimize the flight distance of UAV,we use the B&B based path planning stragety to design the UAV trajectory.In addition,a cooperative routing algorithm is proposed to determine optimal routes for data transmission,in order to reduce the transmission delay.The experminental results show that the UAV-assisted emergency Io T system can collect the temperature,humidity,longitude and latitude,altitude around the waypoints in real time and send back to the ground station system by using the wireless Mesh networks.