Energy-efficient Unmanned Aerial Vehicle Research Articles

Energy-Efficient UAV Movement Control for Fair Communication Coverage: A Deep Reinforcement Learning Approach.

Unmanned Aerial Vehicles (UAVs) are considered an important element in wireless communication networks due to their agility, mobility, and ability to be deployed as mobile base stations (BSs) in the network to improve the communication quality and coverage area. UAVs can be used to provide communication services for ground users in different scenarios, such as transportation systems, disaster situations, emergency cases, and surveillance. However, covering a specific area under a dynamic environment for a long time using UAV technology is quite challenging due to its limited energy resources, short communication range, and flying regulations and rules. Hence, a distributed solution is needed to overcome these limitations and to handle the interactions among UAVs, which leads to a large state space. In this paper, we introduced a novel distributed control solution to place a group of UAVs in the candidate area in order to improve the coverage score with minimum energy consumption and a high fairness value. The new algorithm is called the state-based game with actor–critic (SBG-AC). To simplify the complex interactions in the problem, we model SBG-AC using a state-based potential game. Then, we merge SBG-AC with an actor–critic algorithm to assure the convergence of the model, to control each UAV in a distributed way, and to have learning capabilities in case of dynamic environments. Simulation results show that the SBG-AC outperforms the distributed DRL and the DRL-EC3 in terms of fairness, coverage score, and energy consumption.

Energy-Efficient UAV-Aided Target Tracking Systems Based on Edge Computing

Unmanned-aerial-vehicle (UAV)-aided target tracking has been applied in many important practical scenarios such as target vehicle tracking missions. However, the limited computation capability of UAVs can hardly support computation-intensive tasks, like the target tracking with real-time video processing. Inspired by the strong computation capabilities of edge computing servers nowadays, this article develops an energy-efficient UAV-aided target tracking system, where the video processing tasks can be offloaded from a UAV to the edge nodes (ENs) along its flight trajectory. To select appropriate offloading ENs for efficient task processing and energy saving, we formulate a cost minimization problem by jointly optimizing the task execution time and the offloading energy consumption. To devise a practical offloading strategy, we propose an energy-efficient UAV’s task distribution (EUTD) algorithm by jointly taking the different computation capabilities among ENs, time and energy requirements for different tasks, and fast-changing wireless channel conditions into account. Extensive experimental results demonstrate that our proposed algorithm can achieve significantly higher energy efficiency and lower latency in UAV-aided target tracking as compared with existing methods.

Energy Efficient UAV Flight Path Model for Cluster Head Selection in Next-Generation Wireless Sensor Networks.

Wireless sensor networks (WSNs) are one of the fundamental infrastructures for Internet of Things (IoTs) technology. Efficient energy consumption is one of the greatest challenges in WSNs because of its resource-constrained sensor nodes (SNs). Clustering techniques can significantly help resolve this issue and extend the network’s lifespan. In clustering, WSN is divided into various clusters, and a cluster head (CH) is selected in each cluster. The selection of appropriate CHs highly influences the clustering technique, and poor cluster structures lead toward the early death of WSNs. In this paper, we propose an energy-efficient clustering and cluster head selection technique for next-generation wireless sensor networks (NG-WSNs). The proposed clustering approach is based on the midpoint technique, considering residual energy and distance among nodes. It distributes the sensors uniformly creating balanced clusters, and uses multihop communication for distant CHs to the base station (BS). We consider a four-layer hierarchical network composed of SNs, CHs, unmanned aerial vehicle (UAV), and BS. The UAV brings the advantage of flexibility and mobility; it shortens the communication range of sensors, which leads to an extended lifetime. Finally, a simulated annealing algorithm is applied for the optimal trajectory of the UAV according to the ground sensor network. The experimental results show that the proposed approach outperforms with respect to energy efficiency and network lifetime when compared with state-of-the-art techniques from recent literature.

Resource allocation in UAV assisted air ground intelligent inspection system

With the progress of power grid technology and intelligent technology, intelligent inspection robot (IR) came into being and are expected to become the main force of substation inspection in the future. Among them, mobile edge computing provides a promising architecture to meet the explosive growth of communication and computing needs of inspection robot. Inspection robot can transmit the collected High Definition (HD) video to adjacent edge servers for data processing and state research and judgment. However, the communication constraints of long-distance transmission, high reliability and low delay pose challenges to task offloading optimization. Therefore, this paper introduced Unmanned Aerial Vehicle (UAV) and established UAV assisted mobile edge computing system. UAV assisted and mobile edge computing are combined to form edge computing nodes. In this way, it provided communication and computing services to the IR for fast data processing. Specifically, in order to optimize the system energy consumption, a resource allocation strategy based on genetic algorithm is proposed. By optimizing the offloading decision and computing resource allocation of the IRs, the computing task of the IRs are offloaded to an energy-efficient UAV. The experimental results show that the resource allocation strategy based on genetic algorithm can effectively reduce the energy consumption and cost of UAVs and IRs, and effectively realize the reasonable allocation of resources. The results verify the effectiveness and reliability of the algorithm in the real scene.

Energy model for UAV communications: Experimental validation and model generalization

Wireless communication involving unmanned aerial vehicles (UAVs) is expected to play an important role in future wireless networks. However, different from conventional terrestrial communication systems, UAVs typically have rather limited onboard energy on one hand, and require additional flying energy consumption on the other hand. This renders energy-efficient UAV communication with smart energy expenditure of paramount importance. In this paper, via extensive flight experiments, we aim to firstly validate the recently derived theoretical energy model for rotary-wing UAVs, and then develop a general model for those complicated flight scenarios where rigorous theoretical model derivation is quite challenging, if not impossible. Specifically, we first investigate how UAV power consumption varies with its flying speed for the simplest straight-and-level flight. With about 12,000 valid power-speed data points collected, we first apply the model-based curve fitting to obtain the modelling parameters based on the theoretical closed-form energy model in the existing literature. In addition, in order to exclude the potential bias caused by the theoretical energy model, the obtained measurement data is also trained using a model-free deep neural network. It is found that the obtained curve from both methods can match quite well with the theoretical energy model. Next, we further extend the study to arbitrary 2-dimensional (2-D) flight, where, to our best knowledge, no rigorous theoretical derivation is available for the closed-form energy model as a function of its flying speed, direction, and acceleration. To fill the gap, we first propose a heuristic energy model for these more complicated cases, and then provide experimental validation based on the measurement results for circular level flight.

Distributed reinforcement learning based framework for energy-efficient UAV relay against jamming

Unmanned aerial vehicle (UAV) network is vulnerable to jamming attacks, which may cause severe damage like communication outages. Due to the energy constraint, the source UAV cannot blindly enlarge the transmit power, along with the complex network topology with high mobility, which makes the destination UAV unable to evade the jammer by flying at will. To maintain communication with a limited battery capacity in the UAV networks in the presence of a greedy jammer, in this paper, we propose a distributed reinforcement learning (RL) based energy-efficient framework for the UAV networks with constrained energy under jamming attacks to improve the communication quality while minimizing the total energy consumption of the network. This framework enables each relay UAV to independently select its transmit power based on historical state-related information without knowing the moving trajectory of other UAVs as well as the jammer. The location and battery level of each UAV need not be shared with other UAVs. We also propose a deep RL based anti-jamming relay approach for UAVs with portable computation equipment like Raspberry Pi to achieve higher and faster performance. We study the Nash equilibrium (NE) and the performance bounds based on the formulated power control game. Simulation results show that the proposed schemes can reduce the bit error rate (BER) and reduce energy consumption of the UAV network compared with the benchmark method.

Energy‐efficient full‐duplex UAV relaying networks: Trajectory design for channel‐model‐free scenarios

In this paper, we propose an energy-efficient unmanned aerial vehicle (UAV) relaying network. In this network, the channels between UAVs and ground transceivers are model-free. A UAV acting as a flying relay explores better channels to assist in efficient data delivery between two ground nodes. The full-duplex relaying mode is applied for potential energy efficiency (EE) improvements. With the genetic algorithm, we manage to optimize the UAV trajectory for any arbitrary radio map scenario. Numerical results demonstrate that compared to other schemes (eg, fixed trajectory/speed policies), the proposed algorithm performs better in terms of EE. Additionally, the impact of self-interference on average EE is also investigated.

An Efficient Cluster Based Routing Protocol (ECCRP) Technique Based on Weighted Clustering Algorithm for Different Topologies in Manets using Network Coding

All nodes are energy constrained in MANET. In such a scenario, reducing energy consumption is necessary. The goal of this study is to minimize the energy utilization of various types and environments in MANETs using network coding in a CBRP. Consider other CBRP such as energy-efficient unmanned aerial vehicle fitness (EEFUAV) and compare its performance with the ECCRP and CBRP by taking into account node mobility, traffic and transmission range. Network coding is a way to enhance the efficiency of wireless networks. Energy Requirements, of the EEFUAV approach is intended to be developed to improve the performance of the CBRP and Energy ECCRP.

An Efficient Cluster Based Routing Protocol (ECCRP) Technique Based on Weighted Clustering Algorithm for Different Topologies in Manets using Network Coding

All nodes are energy constrained in MANET. In such a scenario, reducing energy consumption is necessary. The goal of this study is to minimize the energy utilization of various types and environments in MANETs using network coding in a CBRP. Consider other CBRP such as energy-efficient unmanned aerial vehicle fitness (EEFUAV) and compare its performance with the ECCRP and CBRP by taking into account node mobility, traffic and transmission range. Network coding is a way to enhance the efficiency of wireless networks. Energy Requirements, of the EEFUAV approach is intended to be developed to improve the performance of the CBRP and Energy ECCRP.

Energy-Efficient Multi-UAVs Deployment and Movement for Emergency Response

Unmanned aerial vehicles (UAVs) can be utilized to provide communication services for the areas where the communication is unavailable due to the high mobility, agility and low cost. In this letter, we investigate the energy efficient UAVs deployment and the movement for emergency response in the multi-UAVs enabled wireless communication system. The positions of the UAVs, the users association and the transmit power of the ground users are jointly optimized to maximize the energy efficiency of all ground users. And when one UAV leaves because of the breakdown or the drained battery, how the other UAVs move with the minimum energy cost is also studied. An alternating algorithm based on successive convex approximation (SCA) technique is proposed to solve the formulated optimization problems. Numerical results show the proposed multi-UAVs deployment and movement outperform the benchmarks.

Energy-Efficient UAV Backscatter Communication With Joint Trajectory Design and Resource Optimization

Backscatter communication which enables wireless-powered backscatter devices (BDs) to transmit information by reflecting incident signals, is an energy- and cost-efficient communication technology for Internet-of-Things. This paper considers an unmanned aerial vehicle (UAV)-assisted backscatter communication network (UBCN) consisting of multiple BDs and carrier emitters (CEs) on the ground as well as a UAV. A communicate-while-fly scheme is first designed, in which the BDs illuminated by their associated CEs transmit information to the flying UAV in a time-division-multiple-access manner. Considering the critical issue of the UAV's limited on-board energy and the CEs' transmission energy, we maximize the energy efficiency (EE) of the UBCN by jointly optimizing the UAV's trajectory, the BDs' scheduling, and the CEs' transmission power, subject to the BDs' throughput constraints and harvested energy constraints, as well as other practical constraints. Furthermore, we propose an iterative algorithm based on the block coordinated decent method to solve the formulated mixed-integer non-convex problem, in each iteration of which the variables are alternatively optimized by leveraging the cutting-plane technique, the Dinkelbach's method and the successive convex approximation technique. Also, the convergence and complexity of the proposed algorithm are analyzed. Finally, simulation results show that the proposed communicate-while-fly scheme achieves significant EE gains compared with the hover-and-fly scheme, the state-of-the-art scheme, and the CE-relay scheme. Useful insights on the optimal trajectory design and resource allocation are also obtained.

Data Freshness and Energy-Efficient UAV Navigation Optimization: A Deep Reinforcement Learning Approach

In this paper, we design a navigation policy for multiple unmanned aerial vehicles (UAVs) where mobile base stations (BSs) are deployed to improve the data freshness and connectivity to the Internet of Things (IoT) devices. First, we formulate an energy-efficient trajectory optimization problem in which the objective is to maximize the energy efficiency by optimizing the UAV-BS trajectory policy. We also incorporate different contextual information such as energy and age of information (AoI) constraints to ensure the data freshness at the ground BS. Second, we propose an agile deep reinforcement learning with experience replay model to solve the formulated problem concerning the contextual constraints for the UAV-BS navigation. Moreover, the proposed approach is well-suited for solving the problem, since the state space of the problem is extremely large and finding the best trajectory policy with useful contextual features is too complex for the UAV-BSs. By applying the proposed trained model, an effective real-time trajectory policy for the UAV-BSs captures the observable network states over time. Finally, the simulation results illustrate the proposed approach is 3.6% and 3.13% more energy efficient than those of the greedy and baseline deep Q Network (DQN) approaches.

Energy-efficient UAV trajectory design for backscatter communication: A deep reinforcement learning approach

Recently, backscatter communication (BC) has been introduced as a green paradigm for Internet of Things (IoT). Meanwhile, unmanned aerial vehicles (UAVs) can serve as aerial base stations (BSs) to enhance the performance of BC system thanks to their high mobility and flexibility. In this paper, we investigate the problem of energy efficiency (EE) for an energy-limited backscatter communication (BC) network, where backscatter devices (BDs) on the ground harvest energy from the wireless signal of a flying rotary-wing quadrotor. Specifically, we first reformulate the EE optimization problem as a Markov decision process (MDP) and then propose a deep reinforcement learning (DRL) algorithm to design the UAV trajectory with the constraints of the BD scheduling, the power reflection coefficients, the transmission power, and the fairness among BDs. Simulation results show the proposed DRL algorithm achieves close-to-optimal performance and significant EE gains compared to the benchmark schemes.

Energy-efficient unmanned aerial vehicle scanning approach with node clustering in opportunistic networks

The opportunistic networks are challenging due to their inherent characteristics of intermittent and unreliable communication between nodes. In order to alleviate the communication issues, the unmanned aerial vehicles (UAVs) can be used for delivering packets within the opportunistic networks.This paper investigates how to leverage the UAVs in Unmanned Aerial Vehicle aided Opportunistic Networks (UAON). The UAVs are considered responsible for relaying the messages generated by the nodes on the ground. The simulation study is conducted on the real-world datasets of the nodes moving around Orlando and Korea Advanced Institute of Science & Technology (KAIST). Our proposed approach, State-based Campus Routing (SCR) with Density-based spatial clustering of applications with noise (DBSCAN), meander, random, and random spiral scanning approaches, as well as SCR and Epidemic protocols without UAV usage, have been evaluated on both datasets. The simulation metrics included the success rate, the message delay, the number of packets sent, and the distance traveled by the UAVs. SCR with DBSCAN and meander scan approaches were also tested with two UAVs using the Orlando dataset. Furthermore, spiral density and message creation frequency parameters were evaluated for SCR with DBSCAN protocol on North Carolina State University (NCSU) dataset. The simulation results showed improvements in terms of message delay and success rate when the UAVs were used in an opportunistic network setting. The proposed approach showed around 12% less total number of packets sent by the UAVs and the nodes. Similarly, the message delay distributions of the SCR with the DBSCAN achieve 90% of the message delay results, whereas the message delay distributions of random scanning form only 70% in less than an hour.

Energy-Efficient Multi-UAV-Enabled Multiaccess Edge Computing Incorporating NOMA

Multiaccess edge computing (MEC) is regarded as a promising solution to overcome the limit on the computation capacity of mobile devices. This article investigates an energy-efficient unmanned aerial vehicle (UAV)-enabled MEC framework incorporating nonorthogonal multiple access (NOMA), where multiple UAVs are deployed as edge servers to provide computation assistance to terrestrial users and NOMA is adopted to reduce the energy consumption of task offloading. A utility is formed to mathematically evaluate the weighted energy cost of the system. Due to the coupling of parameters, the minimization of utility is a highly nonconvex problem and therefore, the problem is decomposed into two more tractable subproblems, i.e., the optimal allocation of radio and computation resources given UAV trajectories, and the trajectory planning based on given resource allocation schemes. These two problems are converted to convex ones via successive convex approximation (SCA) and quadratic approximation, respectively. Then, an efficient iterative algorithm is proposed where these two subproblems are alternately solved to gradually approach the optimal resource management of the proposed system. Sufficient numerical results show that our proposed strategy has a remarkable advantage over existing systems in terms of energy efficiency.

Energy-Efficient UAV Relaying Communications to Serve Ground Nodes

This letter studies the energy-efficient unmanned aerial vehicle (UAV) communications to support ground nodes (GNs). The system considers the UAV working as a relay while there is a base station (BS) on the ground. We analyze the UAV energy consumption model to design the energy-efficient UAV trajectory path. We formulate the energy-efficient UAV relaying communication, which considers both throughput and UAV propulsion energy consumption. We optimize joint transmit power of UAV and BS; UAV trajectory, acceleration, and flying speed to maximize the energy-efficient UAV relaying problem. We also introduce a constraint named as information causality constraint (ICC). The main idea of ICC is to guarantee that the UAV receives information from BS in any time slot and forward the only received information to GNs in remaining time slots. The formulated energy-efficiency maximization problem is not convex. Thus, we solve it sub-optimally using the iterative method. Finally, we present the simulation results to validate the efficacy of the proposed algorithm.

Energy Efficient UAV Communication With Energy Harvesting

This paper investigates an unmanned aerial vehicle (UAV)-enabled wireless communication system with energy harvesting, where the UAV transfers energy to the users in half duplex or full duplex, and the users harvest energy for data transmission to the UAV. We minimize the total energy consumption of the UAV while accomplishing the minimal data transmission requests of the users. The original optimization problem is decomposed into two subproblems: path planning subproblem and energy minimization subproblem with fixed path planning. For path planning subproblem, the optimal visiting order is obtained by using the dual method and the trajectory is optimized via the successive convex approximation technique. For energy minimization subproblem with fixed path planning, we firstly obtain the optimal portion of data transmission time within the entire procedure and the optimal transmission power of each user. Then, the the energy minimization subproblem is greatly simplified and it is efficiently solved via a one-dimensional search method. Simulation results are illustrated to verify the theoretical findings.

Energy-Efficient UAV-to-User Scheduling to Maximize Throughput in Wireless Networks

The unmanned aerial vehicle (UAV) communication is a potential technology to meet the excessive next-generation cellular users’ demand due to its reliable connectivity and cost-effective deployment. However, UAV communications have to be energy efficient so that it can save energy. Thus, the UAV flies sufficiently long enough time to serve the ground users with limited on-board energy. In this paper, we investigate an energy-efficient UAV communication via designing the UAV trajectory path. We consider throughput and the UAV propulsion energy consumption jointly. We assume that the UAV flies at a fixed altitude such that it can avoid tall obstacles. A binary decision variable is assigned to schedule UAV-to-user communication. First, we derive the UAV-to-user channel model based on the line of sight and non-line of sight communication links and jointly optimize the trajectory, transmit power, and the speed of UAV; and UAV-to-user scheduling to maximize throughput. Then, we apply the UAV propulsion energy consumption, which is a function of the UAV trajectory and speed. Finally, we formulate the UAV energy-efficiency maximization problem, which is defined as the total bits of information sent to the ground users by consuming the UAV energy for a given UAV flight duration. The formulated energy-efficiency maximization problem is non-convex, fractional, and mixed-integer non-linear programming in nature. We propose an efficient algorithm based on successive convex approximation and classical Dinkelbach method to achieve the optimal solution of energy-efficient UAV. We present simulation results to validate the efficacy of our proposed algorithms. The results show a significant performance improvement compared to the benchmark methods.

Energy-Efficient UAV Routing for Wireless Sensor Networks

Recently, an unmanned aerial vehicle (UAV) has been widely adopted to make efficient use of network resources in such areas as internet of things (IoT), sensor networks and three dimensional (3D) wireless networks. Especially, in wireless sensor networks (WSNs) where energy consumption of sensors in data transmission is the most conspicuous feature, data collection by UAV provides a promising solution. To address this issue, we consider a UAV-enabled WSN, where a UAV is dispatched to collect data from sensors distributed in networks. We formulate an optimization problem to maximize the minimum residual energy of sensors after data transmission for energy-efficient UAV routing subject to data collection and UAV traveling distance constraints. To solve the non-convex optimization problem, we first derive a feasible solution, i.e., the shortest UAV route that guarantees data collection at all the sensors, where a Voronoi diagram is modified to find a set of UAV hovering locations. The proposed algorithm preferentially determines each UAV hovering location at Voronoi vertex so that UAV can collect data from as many adjacent sensors as possible. Then with an initial shortest UAV route, a UAV route is proposed by adjusting each UAV hovering location sequentially based on sensor energy status, which is easily accomplished by the properties of Voronoi diagram. Lastly, to find the proposed solution more quickly, we propose a sensor-energy based initial UAV route determination method. Simulation results are provided to validate the performance of our proposed algorithm, and to compare with other UAV route determination schemes.

A Near-Optimal UAV-Aided Radio Coverage Strategy for Dense Urban Areas

Unmanned aerial vehicles (UAVs) may be used for providing seamless network coverage in urban areas for improving the performance of conventional cellular networks. Given the predominantly line-of-sight channel of drones, UAV-aided seamless coverage becomes particularly beneficial in case of emergency situations. However, a single UAV having a limited cruising capability is unable to provide seamless long-term coverage, multiple drones relying on sophisticated recharging and reshuffling schemes are necessary. In this context, both the positioning and the flight strategy directly affect the efficiency of the system. Hence, we first introduce a novel UAV energy consumption model, based on which an energy-efficiency-based objective function is derived. Second, we propose an energy-efficient rechargeable UAV deployment strategy optimized under a seamless coverage constraint. Explicitly, a two-stage joint optimization algorithm is conceived for solving both the optimal UAV deployment and the cyclic UAV recharging and reshuffling strategy. Our simulation results quantify the efficiency of our proposed algorithm.