Unmanned Aerial Vehicles Energy Research Articles

A survey of prototype and experiment for UAV communications

Unmanned aerial vehicle (UAV) communications have attracted significant attention from both academia and industry. To facilitate the large-scale usage of UAVs for various applications in practice, we provide a comprehensive survey on the prototype and experiment for UAV communications. To this end, we first provide an overview on the general architecture of the prototype and experiment for UAV communications, and then present experimental verification for air-to-ground channel models and UAV energy consumption models. Next, we discuss measurement experiments on two promising paradigms of UAV communications, namely cellular-connected UAVs and UAV-enabled aerial communication platforms. For the former, we focus on the feasibility study and address the interference mitigation issue. For UAV-enabled aerial communication platforms, we present three scenarios, namely UAV-enabled aerial base stations, UAV-enabled aerial relays and UAV-enabled aerial data collection/dissemination. Finally, we point out some promising future directions for prototype and experimental measurements of UAV communications.

A Blockchain-Enabled Energy-Efficient Data Collection System for UAV-Assisted IoT

With the rapid development of Internet of Things (IoT), more and more applications focus on the detection of unmanned areas. With the assistance of unmanned aerial vehicle (UAV), IoT devices are able to access the network via aerial base stations. These UAV-assisted IoT applications still face security and energy challenges. The open environment of IoT applications makes the application easy to encounter external invasion. Limited energy of UAV results in the limited lifetime of network access. To address these challenges, researches on IoT security and energy efficiency are becoming hotspots. Nevertheless, in the UAV continuous coverage scenario, there is still an enormous potential to improve the security and efficiency of data collection in IoT applications. In this article, blockchain is introduced into the scene of UAV-assisted IoT, and a data collection system considering security and energy efficiency is proposed. In this system, UAV, as an edge data collection node, provides a long-term network access for IoT devices through regular cruises with recharging. By forwarding data and recording transactions, UAVs get charging coins as rewards. UAVs use charging coins to exchange charging time. UAV swarm builds distributed ledgers based on blockchain to resist the invasion of malicious UAV. In order to reduce energy consumption, this article designs an adaptive linear prediction algorithm. Through this algorithm, IoT devices upload prediction model instead of original data to greatly reduce in-network transmissions. Simulation results show that the proposed system can effectively improve the security and efficiency of data collection.

Deep Reinforcement Learning for Delay-Oriented IoT Task Scheduling in SAGIN

In this article, we investigate a computing task scheduling problem in space-air-ground integrated network (SAGIN) for delay-oriented Internet of Things (IoT) services. In the considered scenario, an unmanned aerial vehicle (UAV) collects computing tasks from IoT devices and then makes online offloading decisions, in which the tasks can be processed at the UAV or offloaded to the nearby base station or the remote satellite. Our objective is to design a task scheduling policy that minimizes offloading and computing delay of all tasks given the UAV energy capacity constraint. To this end, we first formulate the online scheduling problem as an energy-constrained Markov decision process (MDP). Then, considering the task arrival dynamics, we develop a novel deep risk-sensitive reinforcement learning algorithm. Specifically, the algorithm evaluates the risk, which measures the energy consumption that exceeds the constraint, for each state and searches the optimal parameter weighing the minimization of delay and risk while learning the optimal policy. Extensive simulation results demonstrate that the proposed algorithm can reduce the task processing delay by up to 30% compared to probabilistic configuration methods while satisfying the UAV energy capacity constraint.

The Energy-Aware Multi-UAV Dispatch and Handoff Algorithm for Maximizing the Event Communication Time in Disasters

For handling the broken-down communication infrastructure when a disaster event happens, this paper proposes to dispatch the unmanned aerial vehicle (UAV) to the disaster area as the relay node, which further forms a Flying Ad hoc Network (FANET). Since the UAV only owns limited energy and a disaster event may need multiple UAVs to cover its area, an efficient multi-UAV dispatch algorithm is critical to recover the communication link of the disaster area. In this paper, we adopt the mobile ground control station (GCS) to transport UAVs to the boundary of the disaster area first. According to the UAV energy consumption rate during flight and two communication modes, the UAV charging progress, and the number of required UAVs of the event, the mobile GCS then executes the proposed energy-aware multi-UAV dispatch algorithm (EAMUD) to dispatch multiple UAVs to this disaster area for building the FANET. Hence, the broken-down link in the disaster area is recovered after the FANET connects to nearby network infrastructure. Further, we propose the multi-UAV handoff scheme and exception handling processes to replace energy-exhausted UAVs for maximizing the event communication time of the disaster event. Finally, we execute simulations for related work and four EAMUD variants under different parameter values in the real scenario. These results exhibit that EAMUD with the Postpone method (EAMUD-P) achieves the highest event communication time among all these schemes.

An UAV-Enabled Intelligent Connected Transportation System With 6G Communications for Internet of Vehicles

6G networks provide faster communication for connected vehicles. These vehicles are connected to the Internet, forming the Internet of Vehicles (IoV). Due to the development of Intelligent Transportation Systems (ITS), more and more vehicles are deployed with data-intensive applications. These applications interact heavily with IoT devices at the edge of the network, which causes IoT devices to consume a lot of limited and valuable power. Task offloading can help overcome resource constraints of IoT devices by offloading task to edge server which has sufficient computational power in ITS. Unmanned Aerial Vehicles (UAV) is a promising solution by serving as Computing-Communications Edge Server (CCES) for resource-constrained IoT devices that there is no edge server nearby that can offload task. Due to the IoT devices' limited battery capacity and UAV energy budget, it is a challenging issue to reduce the energy for task offloading in UAV-enable edge network. In this paper, an UAV-enabled Computing-Communications Intelligent Offloading (UAV-CCIO) scheme is proposed to offload task energy-efficiently. First, some nodes with a large amount of data are selected as Task Gathering Nodes (TGNs), and TGNs collect all the tasks of the left nodes. In this way, the UAV can only fly the TGNs and so all the IoT devices' tasks can be offloaded. The distance needed for the UAV can be greatly reduced and energy is saved. On the other hand, tasks that route to TGNs have a relatively small amount of data, while nodes with a large amount of data have already been selected as TGNs without routing, thus saving energy. Second, an optimization strategy for collection tasks is proposed to reduce UAV's energy. The extensive experimental simulations indicate that the performance of UAV-CCIO scheme is better than the existing scheme.

Cooperative Multiple Task Assignment Problem With Target Precedence Constraints Using a Waitable Path Coordination and Modified Genetic Algorithm

Task assignment is a critical technology for heterogeneous unmanned aerial vehicle (UAV) applications. Target precedence has typically been ignored in previous studies, such that it is possible to obtain a task assignment solution with an unreasonable target execution order. For this reason, a cooperative multiple task assignment problem with target precedence constraints (CMTAPTPC) model is proposed in this paper, which considers not only kinematic, resource, and task precedence constraints of the UAV, but also target precedence to achieve more realistic scenarios. In addition, a graph method is improved to detect and eliminate deadlocks in solutions that include target precedence constraints. We also introduced a waitable path coordination (WPC) algorithm to generate conflict-free flight paths. Unlike the traditional path elongation method, this method can reduce the number of path elongation operations and save the energy of UAVs. Based on the characteristics of the CMTAPTPC model, this study proposes a modified genetic algorithm that integrates the graph-based method and WPC algorithm to solve the task assignment problem. In the simulation, three problem-scale scenarios were designed, and the superior performance of the modified genetic algorithm was demonstrated by comparing it with a traditional genetic algorithm. Finally, a time series diagram shows the task assignment solution that meets all time constraints and illustrates the rationality of the WPC algorithm.

A Deep Learning Trained by Genetic Algorithm to Improve the Efficiency of Path Planning for Data Collection With Multi-UAV

To collect data of distributed sensors located at different areas in challenging scenarios through artificial way is obviously inefficient, due to the numerous labor and time. Unmanned Aerial Vehicle (UAV) emerges as a promising solution, which enables multi-UAV collect data automatically with the preassigned path. However, without a well-planned path, the required number and consumed energy of UAVs will increase dramatically. Thus, minimizing the required number and optimizing the path of UAVs, referred as multi-UAV path planning, are essential to achieve the efficient data collection. Therefore, some heuristic algorithms such as Genetic Algorithm (GA) and Ant Colony Algorithm (ACA) which works well for multi-UAV path planning have been proposed. Nevertheless, in challenging scenarios with high requirement for timeliness, the performance of convergence speed of above algorithms is imperfect, which will lead to an inefficient optimization process and delay the data collection. Deep learning (DL), once trained by enough datasets, has high solving speed without worries about convergence problems. Thus, in this paper, we propose an algorithm called Deep Learning Trained by Genetic Algorithm (DL-GA), which combines the advantages of DL and GA. GA will collect states and paths from various scenarios and then use them to train the deep neural network so that while facing the familiar scenarios, it can rapidly give the optimized path, which can satisfy high timeliness requirements. Numerous experiments demonstrate that the solving speed of DL-GA is much faster than GA almost without loss of optimization capacity and even can outperform GA under some specific conditions.

Electrical and Reliability Characteristics of FinFETs With High-k Gate Stack and Plasma Treatments

Due to its agile maneuverability, unmanned aerial vehicles (UAVs) have shown great promise for ondemand communications. In practice, UAV-aided aerial base stations are not separate. Instead, they rely on existing satellites/terrestrial systems for spectrum sharing and efficient backhaul. In this case, how to coordinate satellites, UAVs and terrestrial systems is still an open issue. In this paper, we deploy UAVs for coverage enhancement of a hybrid satellite-terrestrial maritime communication network. Under the typical composite channel model including both large-scale and small-scale fading, the UAV trajectory and in-flight transmit power are jointly optimized, subject to constraints on UAV kinematics, tolerable interference, backhaul, and the total energy of UAV for communications. Different from existing studies, only the location-dependent large-scale channel state information (CSI) is assumed available, because it is difficult to obtain the small-scale CSI before takeoff in practice, and the ship positions can be obtained via the dedicated maritime Automatic Identification System. The optimization problem is non-convex. We solve it by problem decomposition, successive convex optimization and bisection searching tools. Simulation results demonstrate that the UAV fits well with existing satellite and terrestrial systems, using the proposed optimization framework.

Trajectory design and resource allocation for UAV energy minimization in a rotary-wing UAV-enabled WPCN

Due to the low efficiency of energy harvest (EH) for the traditional wireless powered communication network (WPCN), we propose a communication scheme based on unmanned aerial vehicle (UAV)-enabled WPCN, which can greatly improve the EH efficiency problem. In this paper, the UAV trajectory, time allocation, transmit power of UAVs, scheduling of wireless information transfer (WIT) and wireless power transfer (WPT) are jointly optimized to minimize the whole energy consumption. To solve the formulated non-convex problem with multiple constraints and attain locally optimal solution, an iterative algorithm is proposed. Simulations show that our proposed algorithm is superior to other benchmark algorithms.

Unmanned aerial vehicles optimal airtime estimation for energy aware deployment in IoT-enabled fifth generation cellular networks

Cellular networks based on new generation standards are the major enabler for Internet of things (IoT) communication. Narrowband-IoT and Long Term Evolution for Machines are the newest wide area network-based cellular technologies for IoT applications. The deployment of unmanned aerial vehicles (UAVs) has gained the popularity in cellular networks by using temporary ubiquitous coverage in the areas where the infrastructure-based networks are either not available or have vanished due to some disasters. The major challenge in such networks is the efficient UAVs deployment that covers maximum users and area with the minimum number of UAVs. The performance and sustainability of UAVs is largely dependent upon the available residual energy especially in mission planning. Although energy harvesting techniques and efficient storage units are available, but these have their own constraints and the limited onboard energy still severely hinders the practical realization of UAVs. This paper employs neglected parameters of UAVs energy consumption in order to get actual status of available energy and proposed a solution that more accurately estimates the UAVs operational airtime. The proposed model is evaluated in test bed and simulation environment where the results show the consideration of such explicit usage parameters achieves significant improvement in airtime estimation.

Optimization for Wireless-Powered IoT Networks Enabled by an Energy-Limited UAV Under Practical Energy Consumption Model

This letter investigates an energy-limited unmanned aerial vehicle (UAV)-enabled wireless-powered Internet-of-Things (IoT) networks. Herein, we propose an energy-constrained/limited fly-and-hover energy harvest (EFH-EH) scheme. The UAV charges the IoT sensors during its flying time, while the hovering time of the UAV above each sensor is divided into two parts, one for energy transfer and the other for information transmission. In particular, a maximum available energy constraint is imposed on the UAV to capture UAV’s real-world energy-limited feature. In addition, a practical UAV energy consumption model is adopted, which differentiates the hovering, flying, and charging energy consumption of the UAV. Under these practical constraints, we study the optimal time allocation and hovering time ratio to maximize the sum throughput. Numerical results show that the proposed EFH-EH scheme outperforms existing schemes, and the maximum available energy constraint limits the system performance when the transmit power is high.

Energy‐aware decision‐making for dynamic task migration in MEC‐based unmanned aerial vehicle delivery system

AbstractNowadays, unmanned aerial vehicles (UAVs) are widely used in many smart systems such as smart logistics, smart agriculture, and environmental monitoring systems. However, the limited computing capability and restricted battery lifetime of existing UAVs could significantly impact the quality of service (QoS) of UAV‐based smart systems and the quality of experience (QoE) of end users. Recently, Mobile Edge Computing (MEC) which provisions computing resources close to the mobile end devices has become a promising solution. However, since high‐speed UAV often flies through the signal range of the different edge nodes, the interruption of services in the MEC‐based UAV delivery system is a critical issue. A challenging question is when and how to perform dynamic task migration among the edge nodes to ensure service continuity. In this paper, we investigate the task migration issue for multiple UAVs in the MEC‐based UAV delivery system. Specifically, we propose an energy‐aware decision‐making strategy for the dynamic task migration named GAD to optimize the UAV energy consumption. Given the real‐time system status and QoS constraints, and through a dynamic two‐tier decision‐making mechanism, GAD can efficiently make the task migration decision from four candidate decisions, viz. No Migration, Data Migration Only, Cold Migration, and Live Migration. Experimental results based on a real‐world scenario show that our strategy can well outperform other baseline strategies in various metrics including the flying distances and the energy consumption of UAVs.

Securing Energy-Constrained UAV Communications Against Both Internal and External Eavesdropping

This letter investigates an unmanned aerial vehicle (UAV) enabled network where an energy-constrained rotary-wing UAV is employed as a mobile base station to serve multiple ground users (GUs), while the data transmission between the UAV and each GU faces security threats from both multiple external eavesdroppers (Eves) and other GUs that potentially act as internal Eves. To ensure the user fairness, we maximize the minimum average secrecy rate among all GUs via jointly optimizing the UAV's trajectory and transmit power as well as the user scheduling, subject to the constraint on the UAV energy budget. To this end, an iterative suboptimal algorithm is proposed by leveraging the block coordinate descent and successive convex approximation techniques. Simulation results are provided to demonstrate the efficacy of the proposed algorithm.

Adaptive autonomous UAV scouting for rice lodging assessment using edge computing with deep learning EDANet

Rice is a globally important crop that will continue to play an essential role in feeding our world as we grapple with climate change and population growth. Lodging is a primary threat to rice production, decreasing rice yield, and quality. Lodging assessment is a tedious task and requires heavy labor and a long duration due to the vast land areas involved. Newly developed autonomous crop scouting techniques have shown promise in mapping crop fields without any human interaction. By combining autonomous scouting and lodged rice detection with edge computing, it is possible to estimate rice lodging faster and at a much lower cost than previous methods. This study presents an adaptive crop scouting mechanism for Autonomous Unmanned Aerial Vehicles (UAV). We simulate UAV crop scouting of rice fields at multiple levels using deep neural networks and real UAV energy profiles, focusing on areas with high lodging. Using the proposed method, we can scout rice fields 36% faster than conventional scouting methods at 99.25% accuracy.

Variable Pitch Propeller for UAV-Experimental Tests

Growth in application fields of unmanned aerial vehicles (UAVs) and an increase in their total number are followed by higher and higher expectations imposed on improvements in UAV propulsion and energy management systems. Most commercial vertical takeoff and landing (VTOL) UAVs employ a constant pitch propeller that forces a mission execution tradeoff in the majority of cases. An alternative solution, presented here, consists of the use of a variable pitch propeller. The paper summarizes experimental measurements of the propulsion system equipped with an innovative variable pitch rotor. The investigations incorporated characteristics of the rotor for no wind conditions and a new approach to optimize pitch settings in hover flight as a function of UAV weight and energy consumption. As UAV battery capacity is always limited, efficient energy management is the only way to increase UAV mission performance. The study shows that use of a variable pitch propeller can increase the maximal takeoff weight of the aircraft and improve power efficiency in hover, especially if load varies for different missions. The maximal thrust measured was 31% higher with respect to the original blade settings. The coefficient of thrust during hover showed an increase of 2.6% up to 7.5% for various pitch angles with respect to the original fixed propeller.

Autonomous Navigation of a Solar-Powered UAV for Secure Communication in Urban Environments with Eavesdropping Avoidance

This paper considers the navigation of a solar-powered unmanned aerial vehicle (UAV) for securing the communication with an intended ground node in the presence of eavesdroppers in urban environments. To complete this task, the UAV needs to not only fly safely in the complex urban environment, but also take into account the communication performance with the intended node and eavesdroppers. To this end, we formulate a multi-objective optimization problem to plan the UAV path. This problem jointly considers the maximization of the residual energy of the solar-powered UAV at the end of the mission, the maximization of the time period in which the UAV can securely communicate with the intended node and the minimization of the time to reach the destination. We pay attention to the impact of the buildings in the urban environments, which may block the transmitted signals and also create some shadow region where the UAV cannot harvest energy. A Rapidly-exploring Random Tree (RRT) based path planning scheme is presented. This scheme captures the nonlinear UAV motion model, and is computationally efficient considering the randomness nature. From the generated tree, a set of possible paths can be found. We evaluate the security of the wireless communication, compute the overall energy consumption as well as the harvested amount for each path and calculate the time to complete the flight. Compared to a general RRT scheme, the proposed method enables a large time window for the UAV to securely transmit data.

The multiple flying sidekicks traveling salesman problem with variable drone speeds

This research considers unmanned aerial vehicles (UAVs) that may travel at varying speeds in a last-mile delivery system involving a single truck and a fleet of UAVs. In existing truck-and-UAV delivery models that assume constant or unlimited UAV endurance, the natural conclusion is that operating UAVs at higher speeds will either decrease, or have no adverse effect on, total delivery times. However, in reality, UAV power consumption is a nonlinear function of both speed and parcel weight; flying at high speeds can dramatically reduce flight ranges, thus limiting the effectiveness of these logistics systems. This paper addresses the tradeoffs between speed and range in a new variant of the combined truck/UAV delivery problem in which UAV speeds are treated as decision variables. A three-phased algorithm is provided that dynamically adjusts UAV speeds to achieve superior performance, with the goal of minimizing the total delivery time (or makespan). Results indicate that significant time savings can be achieved by operating UAVs at variable speeds. Furthermore, under certain conditions, optimizing UAV speeds also results in shorter truck travel distances, reduced UAV energy consumption per trip, and less UAV loitering while waiting to rendezvous with the truck.

Fly Slower, Deliver Faster: the Multiple Flying Sidekicks Traveling Salesman Problem with Variable Drone Speeds

This research considers unmanned aerial vehicles (UAVs) that may travel at varying speeds in a last-mile delivery system involving a single truck and a fleet of UAVs. In existing truck-and-UAV delivery models that assume constant or unlimited UAV endurance, the natural conclusion is that operating UAVs at higher speeds will either decrease, or have no adverse effect on, total delivery times. However, in reality, UAV power consumption is a nonlinear function of both speed and parcel weight; flying at high speeds can dramatically reduce flight ranges, thus limiting the effectiveness of these logistics systems. This paper addresses the tradeoffs between speed and range in a new variant of the combined truck/UAV delivery problem in which UAV speeds are treated as decision variables. A three-phased algorithm is provided that dynamically adjusts UAV speeds to achieve superior performance, with the goal of minimizing the total delivery time (or makespan). Results indicate that significant time savings can be achieved by operating UAVs at variable speeds. Furthermore, under certain conditions, optimizing UAV speeds also results in shorter truck travel distances, reduced UAV energy consumption per trip, and less UAV loitering while waiting to rendezvous with the truck.

Energy-Efficient UAVs Deployment for QoS-Guaranteed VoWiFi Service.

This paper formulates a new problem for the optimal placement of Unmanned Aerial Vehicles (UAVs) geared towards wireless coverage provision for Voice over WiFi (VoWiFi) service to a set of ground users confined in an open area. Our objective function is constrained by coverage and by VoIP speech quality and minimizes the ratio between the number of UAVs deployed and energy efficiency in UAVs, hence providing the layout that requires fewer UAVs per hour of service. Solutions provide the number and position of UAVs to be deployed, and are found using well-known heuristic search methods such as genetic algorithms (used for the initial deployment of UAVs), or particle swarm optimization (used for the periodical update of the positions). We examine two communication services: (a) one bidirectional VoWiFi channel per user; (b) single broadcast VoWiFi channel for announcements. For these services, we study the results obtained for an increasing number of users confined in a small area of 100 m2 as well as in a large area of 10,000 m2. Results show that the drone turnover rate is related to both users’ sparsity and the number of users served by each UAV. For the unicast service, the ratio of UAVs per hour of service tends to increase with user sparsity and the power of radio communication represents 14–16% of the total UAV energy consumption depending on ground user density. In large areas, solutions tend to locate UAVs at higher altitudes seeking increased coverage, which increases energy consumption due to hovering. However, in the VoWiFi broadcast communication service, the traffic is scarce, and solutions are mostly constrained only by coverage. This results in fewer UAVs deployed, less total power consumption (between 20% and 75%), and less sensitivity to the number of served users.

Completion Time and Energy Optimization in the UAV-Enabled Mobile-Edge Computing System

Completion time and energy consumption of the unmanned aerial vehicle (UAV) are two important design aspects in UAV-enabled applications. In this article, we consider a UAV-enabled mobile-edge computing (MEC) system for Internet-of-Things (IoT) computation offloading with limited or no common cloud/edge infrastructure. We study the joint design of computation offloading and resource allocation, as well as UAV trajectory for minimization of energy consumption and completion time of the UAV, subject to the IoT devices' task and energy budget constraints. We first consider the UAV energy minimization problem without predetermined completion time, a discretized nonconvex equivalent problem is obtained by using the path discretization technique. An efficient alternating optimization algorithm for the discretized problem is proposed by decoupling it into two subproblems and addressing the two subproblems with successive convex approximation (SCA)-based algorithms iteratively. Subsequently, we focus on the completion time minimization problem, which is nonconvex and challenging to solve. By using the same path discretization approximation model to reformulate problem, a similar alternating optimization algorithm is proposed. Furthermore, we study the Pareto-optimal solution that balances the tradeoff between the UAV energy and completion time. The simulation results are provided to corroborate this article and show that the proposed designs outperform the baseline schemes. Our results unveil the tradeoff between completion time and energy consumption of the UAV for the MEC system, and the proposed solution can provide the performance close to the lower bound.