Connectivity and collision constrained opportunistic routing for emergency communication using UAV

Unmanned aerial vehicles (UAVs) have been conceived as an available solution to substitute terrestrial base stations (TBSs) to provide downloading services for user equipment (i.e. mobile devices) that have difficulty communicating directly with TBSs. However, the mobility of user equipment (UE) and the random nature of the number of UE will cause several challenges including 1) the hardness of determining optimal user association and UAV resource (i.e., bandwidth and transmit power) allocation decision, 2) the burden of network function maintenance owing to the necessity of shutting down the entire system. Therefore, in this article, joint user association and resource allocation are designed for a software-defined network (SDN)-adopted leaderless softwarized UAV network, where each UAV is regarded as a flying SDN controller to enhance the control ability of the considered network. The purpose is to maximize energy efficiency (EE) with satisfying the quality of service (QoS). To this end, a joint method based on hierarchical agglomerative clustering (HAGC) and multi-agent deep deterministic policy gradient (MADDPG) is proposed. Specifically, the HAGC approach is utilized to determine the optimal MDs association with UAVs. Afterward, MADDPG approach is leveraged to obtain the best policy for resource allocation, aiming to achieve the maximum EE. Finally, the effectiveness of the proposed method is confirmed by the evaluation results.

Recent developments in unmanned aerial vehicle (UAV) technology have given UAVs more processing and storage resources, paving the way for the concept of edge computing-enabled UAV networks. In this paper, we propose a cooperative multi-agent reinforcement learning-based computation offloading framework for a UAV swarm. Flying UAVs with missions can offload part of their tasks to neighboring UAVs or to fixed edge servers at terrestrial base stations. This approach reduces the total energy consumption of all devices during a core mission. Our framework helps UAVs form stable sequences of offloading decisions under uncertainties in a dynamic environment. This study demonstrates the superiority of the proposed deep Q-learning (DQN) algorithm to the existing Q-learning, heuristic, and random decision-making algorithms.

With line-of-sight mode deployment and fast response, unmanned aerial vehicle (UAV), equipped with the cutting-edge integrated sensing and communication (ISAC) technique, is poised to deliver high-quality communication and sensing services in maritime emergency scenarios. In practice, however, the real-time transmission of ISAC signals at the UAV side cannot be realized unless the reliable wireless fronthaul link between the terrestrial base station and UAV are available. This paper proposes a multicarrier-division duplex based joint fronthaul-access scheme, where mutually orthogonal subcarrier sets are leveraged to simultaneously support four types of fronthaul/access transmissions. In order to maximize the end-to-end communication rate while maintaining an adequate sensing quality-of-service (QoS) in such a complex scheme, the UAV trajectory, subcarrier assignment and power allocation are jointly optimized. The overall optimization process is designed in two stages. As the emergency area is usually far away from the coast, the optimal initial operating position for the UAV is first found. Once the UAV passes the initial operating position, the UAV’s trajectory and resource allocation are optimized during the mission period to maximize the end-to-end communication rate under the constraint of minimum sensing QoS. Simulation results demonstrate the effectiveness of the proposed scheme in dealing with the joint fronthaul-access optimization problem in maritime ISAC networks, offering the advantages over benchmark schemes.

Unmanned aerial vehicles (UAVs) have recently been envisaged as an enabling technology of 5G. UAVs act as an intermediate relay node to facilitate uninterrupted, high quality communication between information sources and their destination. However, UAV energy management has been a major issue of consideration due to limited power supply, affecting flight duration. Thus, we introduce in this paper a unified energy management framework by resorting to wireless power transfer (WPT), simultaneous wireless information and power transfer (SWIPT) and self-interference (SI) energy harvesting (EH) schemes, in cooperative relay communications. In our new technique, UAVs are deployed as relays equipped with a decode and forward protocol and EH capability operating in a full-duplex (FD) mode. The UAV assists information transmission between a terrestrial base station and a user. The UAV's transmission capability is powered exclusively by the energy harvested from WPT, radio frequency signal transmitted from the source via time-switching SWIPT protocol and SI exploitation. We improve the overall system throughput by the use of FD based UAV-assisted cooperative system. In this proposed system, we formulate two optimization problems to minimize end-to-end outage probability, subject to UAV's power profile and trajectory for a DF relay scheme, respectively. The KKT conditions have been used to obtain closed-form solutions for the two formulated problems. Numerical simulation results validate all the theoretical results. We demonstrate that the performance of our proposed unified EH scheme outperforms that of existing techniques in the literature.

Mobile users may experience frequent service outages or poor quality of service in rural areas due to the scarcity of base stations in the proximity. Therefore, a cost effective and flexible solution is introduced in this paper with unmanned aerial vehicles (UAVs) and terrestrial base stations (TBSs), coexisting under a stochastic network with the combination of millimeter waves and non-orthogonal access (NOMA) techniques. This is achieved by deploying both the UAVs (millimeter wave frequencies) and TBSs (sub-6 GHz frequencies) in the same system, and selecting the transmitter based on a user association scheme. The maximum signal-to-interference-plus-noise ratio (SINR) and the closest transmitter user association schemes are compared using extensive simulations to identify the most suitable scheme in terms of the outage probability and the achievable downlink rate. The results show that the closest transmitter user association policy results in lower outage probability and higher downlink rates for UAV-assisted millimeter wave NOMA Networks.

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is a prominent strategy where a UAV equipped with an MEC server is deployed to serve terminal devices. This paper considers a multi-UAV assisted network in which multiple UAVs and a terrestrial base station (BS) are deployed to provide MEC services to mobile users. The objective is to minimize an energy and latency-based cost function by jointly optimizing task offloading and MEC server selection decision, transmission power, UAV trajectory, and CPU frequency allocation. An alternating iterative approach based on the block descent method is proposed to solve this problem. In the first layer, task offloading and server selection decision subproblem is solved using a game theoretic approach. The second layer handles offloading and downloading transmission power allocations by utilizing a simplistic geometric waterfilling (GWF) technique, and the UAV trajectory by successive convex approximation (SCA). Whereas, the third layer solves the computation resource subproblem by performing CPU frequency allocation using a gradient descent method. The proposed method uses a segment-by-segment approach, which divides the entire UAV flight trajectory into shorter timeframe segments to reduce the computation time. Simulation results are presented to show that the proposed approach outperforms various benchmark schemes.

Hierarchical aerial offload computing algorithm based on the Stackelberg-evolutionary game model

Unmanned Aerial Vehicles (UAVs) are used in various applications, including crowd management, crime prevention, accident detection, and rescue operations. However, since UAVs perform their tasks independently, some UAV applications are dynamic and geographically distributed, which may require extensive real-time processing capabilities. Thus, processing UAV data locally can be challenging due to their limited computing capabilities. To overcome such limitations, fog and cloud computing can facilitate UAV application development by providing additional resource capacities when needed. Despite this, designing sophisticated and efficient UAV task offloading strategies that collaborate with fog and cloud technologies considering their service latency and energy consumption, is rarely addressed in the literature. Therefore, a collaborative offloading strategy for UAV applications is presented in this work, leveraging fog and cloud computing advantages and capabilities. This approach aims to minimize UAVs’ service latency and energy consumption, as well as provide the required resources and services in real time. In addition, task offloading decisions are formulated using the Mixed-Integer Linear Programming (MILP) model to reduce the energy consumption of the entire UAV-fog-cloud system by optimizing the allocation of computation resources and communication requested by each UAV. The simulation results demonstrate that the proposed strategy can significantly reduce UAV service latency by 15.38%, 35.29%, and 59.26%, as well as decrease overall energy consumption (including processing and networking) by 3.3%, 7.37%, and 12% when compared to alternative standalone strategies (namely UAV, fog, and cloud).

As a quite interesting subject, there are an increasing number of researches about UAV (Unmanned Aerial Vehicle) in the literature.These robots are quite suitable to dull, dirty and dangerous missions (e.g., over flights at low altitude, especially at night, and longrunning operations, which exposes the crew to extreme fatigue).Thus, an important application of these vehicles is the search operations involving multiple UAVs.However, despite the huge potential use of the UAVs, cooperative search operations with this kind of flying robots are not yet occurring.This research topic is a multidisciplinary area of study in its beginning.Several issues can be studied related to cooperative UAV for search operations, such as centralized versus decentralized control, path planning for cooperative flies, agent reasoning for UAV tactical planning, safety assessments, reliability in automatic target reconnaissance by cameras, agent coordination mechanisms applied for UAV cooperation and the application itself (e.g., patrolling, remote sensing, search and rescue operations supporting, etc.).This paper proposes a model that combines path planning algorithms, search patterns and multiagent coordination techniques to obtain a cooperative UAV model.Different path planning algorithms were studied aiming to get the most suitable to these kinds of operations, and the conclusions are presented.In addition, official documents of Search and Rescue operations are also studied in order to know the best practices already established for this kind of operations, and, finally, an overview of the coordination multi-agent theory is presented and evaluated to achieve the UAV coordination.The great goal for cooperative UAV is to achieve such performance that the performance of the group overcomes the sum of the individual performances isolatedly.Comparing two cooperative UAVs with two non-cooperative UAVs, simulations of previous work showed an average increase in efficiency by about 100%.Besides, another aspect was simulated and evaluated: the impact of the density of the initial sweep.The computational simulations show a high potential of reducing even more the time of search.Furthermore, it is worth noting that the usefulness of cooperative UAVs is not only the increase in efficiency, but also the reduction of costs and risks for the crew.

Unmanned Aerial Vehicles (UAV), also known as drones, are receiving increasing attention as enablers for many emerging technologies and applications, a trend likely to continue in the next future. In this regard, using Unmanned Aerial Base Stations (UABSs), i.e. base stations carried by UAVs, is one of the most promising means to offer coverage and capacity in 5G applications to those users that are not being served by terrestrial base stations. In this paper, we propose a novel approach for trajectory design and Radio Resource Management (RRM) in UAV-aided networks using information retrieved from precise Radio Environmental Map (REM) based on Ray Launching (RL) simulations for RF propagation and narrow band estimations. Furthermore, we consider different possible models for antennas to be installed on multiple UABSs as well as proper RRM strategies which are able to take advantage of REM inputs. Simulation results will show the performance achieved by the system for the different approaches and it will compare them with the previous use of statistical models.

It is well known that unmanned aerial vehicles (UAVs) can help terrestrial base stations (BSs) offload data traffic from crowded areas to improve coverage and boost throughput. However, the limited backhaul capacity cannot cope with the ever-increasing data demands, for which caching is introduced to relieve the backhaul bottleneck. In this paper, we focus on a multi-UAV assisted wireless network, and target to fully utilize the benefits of wireless caching and UAV mobility for multiuser content delivery. By taking into account the limited storage, our goal is to maximize the minimum throughput among UAV-served users by jointly optimizing cache placement, UAV trajectory, and transmission power in a finite period. The resultant problem is a mixed-integer non-convex optimization problem. To facilitate solving this problem, an alternating iterative algorithm is proposed by adopting the block alternating descent and successive convex approximation methods. Specifically, this problem is split into three subproblems, namely cache placement optimization, trajectory optimization, and power allocation optimization. Then these subproblems are solved alternately in an iterative manner. We show that the proposed algorithm can converge to the set of stationary solutions of this problem. Besides, we further analyze the computational complexity of this algorithm. Numerical results show that great throughput enhancement is achieved by applying our proposed joint design in comparison with other benchmarks without trajectory design and power control.

The unmanned aerial vehicles (UAVs) have been widely considered as one of the key applications for future wireless communication systems, where UAVs can be used as aerial base stations (BSs) for coverage extension, transmission improvement, emergency communication, and etc. Against this background, each UAV BS is expected to select the optimal codeword to form directional analog beams, and it is capable of achieving concurrent transmissions from multiple other UAV BSs simultaneously. However, in such a kind of UAV networks, due to the vast number of connected mobile users (MUEs), UAV BSs cannot timely and preciously select the codeword from the pre-defined codebook. Fortunately, machine learning (ML) is suitable for decreasing complexity in codeword selection, because ML could extract features from the data samples acquired in real environments. In this paper, we propose an ML approach to achieve an efficient and low complexity codeword selection for UAV networks. Specifically, we first derive the probabilities that multiple UAV BSs serve one MUE to obtain the average sum rate (ASR) in UAV networks. On that basis, we develop an ML approach to maximize the ASR, where we design a classifier based on support vector machine (SVM), where our ML approach is used for selecting the optimal codeword and maximizing the ASR in UAV networks. Third, we proposed an iterative sequential minimal optimization (SMO) training algorithm to train the data of all links between UAV BSs and MUEs, where the algorithm convergence is also discussed. Finally, we show the comparison between our proposed algorithm and the traditional methods by the simulation results. The simulation at last demonstrate our method is a more efficient solution for obtain a higher performance, where a much lower computational complexity can achieved than the traditional algorithm based on channel estimation.

Unmanned aerial vehicle (UAV) swarms are extensively used in emergency communications, area monitoring, and disaster relief. Their operations are coordinated by control centers, making them well-suited for federated learning (FL) frameworks. However, current UAV FL methods ignore the rich information contained in UAV images and the potential of using a single dataset to accomplish multiple tasks. For instance, in disaster relief scenarios, images acquired by UAVs can support tasks like crowd detection, road passability analysis, and disaster impact assessment. These tasks exhibit time-varying demands and may have potential correlations. To meet these requirements, this paper introduces two core mechanisms: a dynamic task attention mechanism to evaluate task importance for efficient resource allocation, and a task affinity (TA) metric to capture inter-task correlations for knowledge sharing. Building on these innovations, we propose FedDya, a novel UAV swarm-based one-dataset multi-task FL framework, where ground emergency vehicles (EVs) collaborate with UAVs to accomplish multiple tasks leveraging a single dataset. To optimize resource allocation, we formulate a two-layer optimization problem to jointly optimize UAV transmission power, computation frequency, bandwidth allocation, and UAV-EV associations. For the inner problem, we derive closed-form solutions for transmission power, computation frequency, and bandwidth allocation and apply the block coordinate descent method for optimization. For the outer problem, a novel two-stage algorithm is designed to determine optimal UAV-EV associations. Furthermore, theoretical analysis reveals a trade-off between UAV energy consumption violation and multi-task performance, characterized by an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i>( √ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</i> , 1/<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</i> ) relationship. Extensive simulation results further validate the effectiveness of the proposed scheme.

Unmanned aerial vehicles (UAVs) are playing an important role in serving ground users as base stations (BSs) during temporary events, or after terrestrial BSs fail. UAV deployment is a fundamental and vital problem which directly affects the coverage capacity and user experience. Especially, while deploying a UAV backbone network, the cost and the quality of service (QoS) of the ground users as well as the connectivity among UAVs need to be considered jointly. In this paper, the deployment problem is modeled as minimizing the amount of deployed UAVs while guaranteeing the coverage capacity and connectivity among UAVs. Further, a novel UAV deployment scheme is proposed where a heuristic algorithm is adopted to delete locations from the initially defined candidate UAV locations iteratively, and UAVs are deployed at the remaining locations finally. The simulation results show that our UAV deployment scheme can achieve minimum deployed UAVs while ensuring QoS requirements and a robust network among UAVs.

An Unmanned Aerial Vehicle (UAV) is an airborne system or pilotless aircraft which is remotely controlled by a human operator on ground or by an onboard computer such that the vehicle moves autonomously. The range of applications in which UAVs are used is very large. This paper describes the application of developing an autonomous surveillance system using an UAV to identify a given target and/or objects of interest in the terrain over which it flies. Such a system can be used in rescue operations, especially in remote areas where physical access is difficult. It can also be used for military operations, farming or any field where surveillance of a given land area is required. The UAV developed in this work is capable of object detection. A mounted camera is used to give visual feedback, and an onboard processing unit runs image recognition software to identify the target in real time. Optimal algorithms are used to search and find the target from the given search area. After recognition of the target, the UAV can either be used to hold its position so as to have a video feed of the target, or return to its base station once the coordinates have been estimated using GPS modules or relay the GPS location to the base station. This paper describes the implementation of the hardware and software components that lead to the realization of the UAV and the application of object detection. The details of a new search algorithm and an example of object detection is presented . The work presented in this paper is the first part in the attempt to develop a cluster of UAVs meant to work in collaboration to be deployed for search and rescue operations.