The increasing demand for high-quality real-time 360° video streams from mobile platforms, such as 5G-connected Unmanned Aerial Vehicles (UAVs), is challenging modern B5G networks. Vehicular mobility and fluctuating conditions in high-altitude, high-speed scenarios, known as high volatility, complicate maintaining an effective Quality of Experience (QoE) for cellular networks. This work introduces FlyBit, a Deep Reinforcement Learning (DRL)-based bitrate selection framework for live 360° video streaming in 5G-connected UAV applications, designed to enhance video quality, reduce packet loss, and minimize End-to-End (E2E) latency. We developed and deployed a real-world testbed to evaluate the impact of dynamic network conditions, UAV mobility, and trajectory on streaming performance, analyzing FlyBit with real-world data. Experimental results show that FlyBit improves Video Multimethod Assessment Fusion (VMAF) by ~29% and average bitrate by ~50%, while maintaining low latency and packet loss compared to baseline approaches, demonstrating its ability to adjust bitrate in real-time and significantly improve QoE for ultra-low-latency video streaming.

Wireless communication has profoundly transformed the way we experience the world. For instance, at most events, attendees commonly utilize their smartphones to document and share their experiences. This shift in user behavior largely stems from the cellular network’s capacity for communication. However, as networks become increasingly sophisticated, new opportunities arise to leverage the network for services beyond mere communication, collectively termed Beyond Communication Services (BCS). These services encompass joint communications and sensing, network as a service, and distributed computing. This paper presents examples of BCS and identifies the enablers necessary to facilitate their realization in sixth generation (6 G). These enablers encompass exposing data and network capabilities, optimizing protocols and procedures for BCS, optimizing compute offloading protocols and signalling, and employing application and device-driven optimization strategies.

This paper addresses the challenging problem of enabling reliable immersive teleoperation in scenarios where an Unmanned Aerial Vehicle (UAV) is remotely controlled by an operator via a cellular network. Such scenarios can be quite critical particularly when the UAV lacks advanced equipment (e.g., Lidar-based auto stop) or when the network is subject to some performance constraints (e.g., delay). To tackle these challenges, we propose a novel architecture leveraging Digital Twin (DT) technology to create a virtual representation of the physical environment. This virtual environment accurately mirrors the physical world, accounting for 3D surroundings, weather constraints, and network limitations. To enhance tele-operation, the UAV in the virtual environment is equipped with advanced features that may be absent in the real UAV. Furthermore, the proposed architecture introduces an intelligent logic that utilizes information from both virtual and physical environments to approve, deny, or correct actions initiated by the UAV operator. This anticipatory approach helps to mitigate potential risks. Through a series of field trials, we demonstrate the effectiveness of the proposed architecture in significantly improving the reliability of UAV teleoperation.

This paper investigates the fundamental traffic steering issue for cellular-enabled unmanned aerial vehicles (UAVs), where each UAV needs to select one from different Mobile Network Operators (MNOs) to steer its traffic for improving the Quality-of-Service (QoS). To this end, we first formulate the issue as an optimization problem aiming to minimize the maximum outage probabilities of the UAVs. This problem is non-convex and non-linear, which is generally difficult to be solved. We propose a solution based on the framework of deep reinforcement learning (DRL) to solve it, in which we define the environment and the agent elements. Furthermore, to avoid sharing the learned experiences by the UAV in this solution, we further propose a federated deep reinforcement learning (FDRL)-based solution. Specifically, each UAV serves as a distributed agent to train separate model, and is then communicated to a special agent (dubbed coordinator) to aggregate all training models. Moreover, to optimize the aggregation process, we also introduce a FDRL with DRL-based aggregation (DRL2A) approach, in which the coordinator implements a DRL algorithm to learn optimal parameters of the aggregation. We consider deep Q-learning (DQN) algorithm for the distributed agents and Advantage Actor-Critic (A2C) for the coordinator. Simulation results are presented to validate the effectiveness of the proposed approach.

Multi-services are of fundamental importance in Unmanned Aerial Vehicle (UAV)-enabled aerial communications for the Internet of Things (IoT). However, the multi-services are challenging in terms of requirements and use of shared resources such that the traditional solutions for a single service are unsuitable for the multi-services. In this paper, we consider a UAV-enabled aerial access network for ground IoT devices, each of which requires two types of services, namely ultra Reliable Low Latency Communication (uRLLC) and enhanced Mobile Broadband (eMBB), measured by transmission delay and effective rate, respectively. We first consider a communication model that accounts for most of the propagation phenomena experienced by wireless signals. Then, we derive the expressions of the effective rate and the transmission delay, and formulate each service type as an optimization problem with the constraints of resource allocation and UAV deployment to enable multi-service support for the IoT. These two optimization problems are nonlinear and nonconvex and are generally difficult to be solved. To this end, we transform them into linear optimization problems, and propose two iterative algorithms to solve them. Based on them, we further propose a linear program algorithm to jointly optimize the two service types, which achieves a trade-off of the effective rate and the transmission delay. Extensive performance evaluations have been conducted to demonstrate the effectiveness of the proposed approach in reaching a trade-off optimization that enhances the two services.

This paper proposes the concept of Ahead-Me Cov-erage (AMC) aiming to get the coverage of a cellular network ahead of the mobile users for maintaining enhanced Quality- of-Service (QoS) in cellular-connected unmanned aerial vehicle (UAV) networks. In such networks, each base station (BS) with an intelligent logic can automatically tilt the direction of its radio antennas based on the trajectory of UAV s. For this purpose, we first formulate AMC as an integer optimization problem for maximizing the minimum transmission rate of UAVs by jointly optimizing the angles of the different radio antenna, the resource allocation and the selection of the appropriate serving BS for the UAVs throughout their path. For this complex optimization problem, we then propose a solution based on Deep Reinforcement Learning (DRL) to solve it. Under this solution, we adopt a multi-heterogeneous agent-based approach (MHA-DRL) including two types of agents, namely the UAV agents and the BS agents. Each agent implements an Advantage Actor Critic (A2C) to learn optimal policies. Specifically, the BS agents aim to tilt their antennas to get ahead of the UAV s throughout their mobility, and the UAV agents target selecting the appropriate serving BSs along with resource allocation. Performance evaluations are presented to validate the effectiveness of the proposed approach.

This paper considers the scenario of Unmanned Aerial Vehicles (UAVs) acting as flying base stations (UAV-BSs) to provide network connectivity to ground Internet of Things (IoT) devices. More precisely, we investigate the issue where a UAV-BS needs to be replaced by a new one in a seamless way. First, we formulate the issue as an optimization problem aiming to maximize the minimum transmission rate of the served IoT devices during the UAV-BS replacement process. This is translated into jointly optimizing the trajectory of the source UAV-BS (the one to be replaced) and the target UAV-BS (the replacing one), while pushing the IoT devices to seamlessly transfer their connections to the target UAV-BS. We therefore consider a target replacement zone where the UAV-BS replacement can happen, along with IoT connections transfer. Furthermore, we propose a solution based on Deep Reinforcement Learning (DRL). More precisely, we introduce a Multi-Heterogeneous Agent-based approach (MHA-DRL), where two types of agents are considered, namely the UAV-BS agents and the IoT agents. Each agent implements a DQN (Deep Q-Learning) algorithm, where UAV-BS agents learn optimal policies to perform replacement while IoT agents learn optimal policies to transfer their connections to the target UAV-BS. The conducted performance evaluations show that the proposed approach can achieve near optimal optimization.

This paper investigates the fundamental connection steering issue in cellular-enabled Unmanned Aerial Vehicles (UAVs), whereby a UAV steers the cellular connection across multiple Mobile Network Operators (MNOs) for ensuring enhanced Quality-of-Service (QoS). We first formulate the issue as an optimization problem for minimizing the maximum outage probability. This is a nonlinear and nonconvex problem that is generally difficult to be solved. To this end, we propose a new approach for solving the optimization problem based on Deep Reinforcement Learning (DRL), considering two important reinforcement learning algorithms (i.e., Deep Q-Learning (DQN) and Advantage Actor Critic (A2C)). Simulation results show that under the proposed approach, the UAVs can make optimal decisions to select the most suitable connection with MNOs for achieving the minimization of the maximum outage probability. Furthermore, the results also show that in our new approach, the A2C-based algorithm is better than the DQN-based one, especially when the number of MNOs increases, while the DQN-based algorithm can be executed in a shorter time.

The Next Generation of Mobile Networks (NGMN) alliance advocates the use of different means to support vehicular communications. This aims to cope with the massive data generated by these devices which could affect the Quality of Service (QoS) of the associated applications, but also the overall operation carried out by the vehicles. However, efficient communication strategies must be considered in order to select, for each vehicle, the communication mean ensuring the best QoS. In this paper, we tackle this issue and we propose efficient communication strategies for Unmanned Aerial Vehicles (UAVs). In addition to direct UAV-to-Infrastructure communications (U2I), we also consider UAV-to-UAV scheme (U2U) to transmit data via relay UAVs. The goal is to select for each UAV the best communication strategy and the relay node to maximize the spectral efficiency. The expressions of the effective rate are derived for the different strategies and the problem is formulated using linear programming. Performance evaluations are conducted and the obtained results demonstrate the effectiveness of the proposed solution.

The challenging problem of energy efficiency in security of the Internet of Things (IoT) is tackled in this article. The authors consider the upcoming generation of mobile networks, 5G, as a communication architecture for the IoT. The concept of adaptive security is adopted, which is based on adjusting the security level as per the changing context. It has the potential of reducing energy consumption by adapting security rather than always considering the worst case, which is energy consuming. The consideration of 5G introduces new dynamics that can be exploited to perform more adaptation. The proposed solution introduces an intelligence in the application of security, from the establishment phase to the use phase (end-to-end). The security level related to the used cryptographic algorithm/key is adapted for each node during the establishment phase, so to match with the duration of the provided services. A new strategy is formulated that considers both IoT and 5G characteristics. In addition, a solution based on the framework of the coalitional game is proposed in order to associate the deployed objects with the optimized security levels. Moreover, the application of security is also adapted during the use phase according to the threat level. Trust management is used to evaluate the threat level among the network nodes, while existing works focus on performing the adaptation during the use phase. The proposed approach achieves more adaptation through the consideration of both IoT and 5G dynamics. The analysis and performance evaluations are conducted to show the effectiveness of the proposed end-to-end approach.

Cellular networks are expected to be the main communication infrastructure to support the expanding applications of Unmanned Aerial Vehicles (UAVs). As these networks are deployed to serve ground User Equipment (UEs), several issues need to be addressed to enhance cellular UAVs' services. In this article, we propose a realistic communication model on the downlink, and we show that the Quality of Service (QoS) for the users is affected by the number of interfering BSs and the impact they cause. The joint problem of sub-carrier and power allocation is therefore addressed. Given its complexity, which is known to be NP-hard, we introduce a solution based on game theory. First, we argue that separating between UAVs and UEs in terms of the assigned sub-carriers reduces the interference impact on the users. This is materialized through a matching game. Moreover, in order to boost the partition, we propose a coalitional game that considers the outcome of the first one and enables users to change their coalitions and enhance their QoS. Furthermore, a power optimization solution is introduced, which is considered in the two games. Performance evaluations are conducted, and the obtained results demonstrate the effectiveness of the propositions.

The consideration of mobile networks as a communication infrastructure for unmanned aerial vehicles (UAVs) creates a new plethora of emerging services and opportunities. In particular, the availability of different mobile network operators (MNOs) can be exploited by the UAVs to steer connection to the MNO ensuring the best quality of experience (QoE). While the concept of traffic steering is more known at the network side, extending it to the device level would allow meeting the emerging requirements of today's applications. In this vein, an efficient steering solutions that take into account the nature and the characteristics of this new type of communication is highly needed. The authors introduce, in this paper, a mechanism for steering the connection in mobile network-enabled UAVs. The proposed solution considers a realistic communication model that accounts for most of the propagation phenomena experienced by wireless signals. Moreover, given the complexity of the related optimization problem, which is inherent from this realistic model, the authors propose a solution based on coalitional game. The goal is to form UAVs in coalitions around the MNOs, in a way to enhance their QoE. The conducted performance evaluations show the potential of using several MNOs to enhance the QoE for mobile network-enabled UAVs and prove the effectiveness of the proposed solution.

The efficient control of mobile network-enabled unmanned aerial vehicles (UAVs) is targeted in this paper. In particular, a downlink scenario is considered, in which control messages are sent to UAVs via cellular base stations (BSs). Unlike terrestrial user equipment (UEs), UAVs perceive a large number of BSs, which can lead to increased interference causing poor or even unacceptable throughput. This paper proposes a framework for efficient control of UAVs. First, a communication model is introduced for flying UAVs taking into account interference, path loss and fast fading. The characteristics of UAVs make such model different compared to traditional ones. Thereafter, in order to ensure the efficient control, a solution is proposed for reducing interference. This is achieved by efficiently assigning sub-carriers to the UAVs in a way to reduce interference. A maximum independent set formulation is proposed along with an algorithm for optimal sub-carrier allocation. The obtained results demonstrate the efficiency of the proposed solution in terms of enhancing the link quality of UAVs.

The next generation of Unmanned Aerial Vehicles (UAVs) will rely on mobile networks as a communication infrastructure. Several issues need to be addressed to enable the expected potentials from this communication. In particular, it was demonstrated that flying UAVs perceive a high number of base stations (BSs), consequently causing more interferences on non-serving BSs. This unfortunately results in decreased throughput for ground user equipments (UEs) already connected. Such a problem could be a limiting factor for mobile network-enabled UAVs, due to its consequences on the quality of experience (QoE) of served UEs. This underpins the focus of this article, wherein the effect of UAVs' communication on ground UEs in the uplink scenario is studied. First, given the fact that the nature of flying UAVs introduces particularities that make the underlying communication models different from traditional ones, this work proposes a model for mobile network-enabled UAVs (considering interferences, path loss, and fast fading). Moreover, we also tackle the QoE issue and propose an optimization solution based on adjusting the transmission power of UAVs. Simulations are conducted to evaluate the mobile network performance in the presence of flying UAVs. Our results reveal that as the number of added UAVs increases, a significant increase in the outage is observed. We demonstrate that our power optimization strategy guarantees the QoE for UEs, offers good communication links for UAVs, and reduces the overall interference in the network.

Massive Internet of Things (mIoT) is an important use case of 5G. The main challenge for mIoT is the huge amount of uplink traffic as it dramatically overloads the radio access network (RAN). To mitigate this shortcoming, a new RAN technology has been suggested, where small cells are used for interconnecting different devices to the network. The use of small cells will alleviate congestion at the RAN, reduce the end-to-end (E2E) delay, and increase the link capacity for communications. In this paper, we devise three solutions for deploying and interconnecting small cells that would handle mIoT traffic. A realistic physical model is considered in these solutions. The physical model is based on a composite fading channel that captures path loss, fast fading, shadowing, and interference to derive the signal-to-interference-plus-noise ratio. The three solutions consider two conflicting objectives, namely the cost and the E2E delay for deploying and backhauling small cells. The first solution minimizes the cost while the second reduces the E2E delay. The third solution uses bargaining game theory for reducing both the cost and the E2E delay. The proposed solutions are evaluated through simulations. The obtained results demonstrate the efficiency of each solution in achieving its design goals.

Providing efficient security services in dynamic low-power environments as the Internet of Things (IoT) is a challenging task. The deployment of static security services will consume the energy even if it is not required in some situations, so this induces a waste of resources. In this paper, we introduce an efficient model for adaptive security in the IoT based on trust management. Most of existing adaptive security approaches lack of practical means to evaluate threats. On the other hand, trust management systems are designed to deal with selfish behaviors or internal attacks and not to assist cryptographic measures. Our solution evaluates the trust level related to the presence of security threats among nodes, and adapt consequently cryptographic measures. The obtained simulation results show that our solution reduces considerably energy consumption and remains yet secure.