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Federated Learning for Trust Enhancement in UAV-Enabled IoT Networks: A Unified Approach
Abstract
This study presents a federated learning (FL) framework tailored for UAV-enabled IoT networks, addressing challenges in efficiency, robustness, and scalability. The proposed system improves model learning with a 14.9 percentage point increase in accuracy (75.5% to 90.4%) and a 69.2% reduction in loss over ten training epochs. It demonstrates resilience, limiting accuracy reduction to 7% under simulated attacks, and scalability with a linear increase in processing times as network size grows. High anomaly detection rates (92%) further enhance network security and reliability. These results validate the framework’s effectiveness in UAV networks and highlight its broader potential for IoT applications. Future work will explore further enhancements and diverse applications.
Sixth generation (6G) networks deploy unmanned aerial vehicles and mobile edge computing to provide collaborative computing and reliable connectivity for resource-limited mobile devices (MDs). However, due to the untrusted and broadcast nature of wireless transmission among communicating MDs and computing resource providers, ensuring the security of resource transactions will be challenging. Blockchain-based resource-sharing systems have been proposed to address security issues. However, these systems use existing consensus mechanisms like proof-of-work that consume massive amounts of system resources. In addressing this, some studies attempted to use single-agent deep reinforcement learning (DRL) in leader selection. Nevertheless, these solutions overlooked the intelligence and flexibility of blockchain configuration, and a single-point of failure can cause the system to fail. We propose a multi-agent Distributed Deep Deterministic Policy Gradient (MAD3PG)-assisted consensus mechanism for blockchain-based collaborative resource-sharing to address these issues. First, we propose a stochastic game-based incentive-mechanism to encourage consensus nodes to participate in transaction validation. Then, we formulate the optimization problem of node selection and blockchain configuration as a Markov decision process and solve it with the MAD3PG algorithm. With MAD3PG, the agents select consensus nodes based on their experience and available resources and dynamically adjust blockchain settings. The simulation results show that MAD3PG outperforms the benchmarks in maximizing throughput and incentive while minimizing block production latency.
Forest fires cause damage to life, biodiversity, and properties, affecting natural ecosystems, healthy and only. During the practical application of fire prevention, numerous detection techniques have been thoroughly researched to prevent devastating fires. The techniques improve early fire detection and accelerate emergency response, reducing damage and optimizing system containment operations. Wildfire detection requires a robust infrastructure for equipment, maintenance, and ongoing monitoring. Effective cooperation between components and integration of technologies is key to a consistent and comprehensive response to fires. This article proposes a fire monitoring system using drones, cameras, and edge computing technologies. We use Stochastic Petri Nets (SPN) to model the structure and evaluate the system’s performance. The model is parameterizable, allowing adjustments to the components’ resource capabilities and service time. Twenty-four parameters can be defined, making it possible to evaluate a wide variety of different scenarios. The results obtained in different scenarios in this work have the potential for auxiliary administrators of monitoring systems to estimate more than six analyses and plan more optimized architectures as needed.
Reconfigurable intelligent surface (RIS) is viewed as a promising technique that can be utilized to improve the performance of systems by reconfiguring signal propagation environments. This paper investigates green and secure unmanned aerial vehicle (UAV) Internet-of-Things (IoT) communications with the aid of RIS, where multiple UAVs harvest energy from a power beacon (PB) and send information uplink to access point (AP) with non-orthogonal multiple access (NOMA). In particular, communication can be divided into two phases during each time frame: energy transfer and information transmission. Three RIS deployment strategies are proposed. In mode I, RISs are deployed between UAVs and AP to enhance the information transmission. In mode II, RISs are deployed between PB and UAVs to enhance the energy transfer. In mode III, RISs are deployed between UAVs and a hybrid access point (HAP) to enhance energy transfer and information transmission simultaneously. Considering phase compensation error caused by imperfect conditions, we define and evaluate ergodic capacity (EC), ergodic capacity probability (ECP) and ergodic secrecy capacity (ESC) of three modes to measure the reliability and security of the system. The asymptotic expressions are also derived for further insights. Numerical results are presented to validate the correctness of theoretical derivations. Results demonstrate that the passive beamforming gain promised by RIS can significantly enhance the performance of systems. Mode III outperforms other modes in terms of reliability and security. When the transmission power and the number of UAVs increase, the ESCs of modes I and III converge to the same performance floor. Index Terms-Reconfigurable intelligent surface (RIS), un-manned aerial vehicle (UAV), non-orthogonal multiple access (NOMA), performance analysis.
Federated Learning (FL) is a decentralized machine learning (ML) technique that allows a number of participants to train an ML model collaboratively without having to share their private local datasets with others. When participants are unmanned aerial vehicles (UAVs), UAV-enabled FL would experience heterogeneity due to the majorly skewed (non-independent and identically distributed -IID) collected data. In addition, UAVs may demonstrate unintentional misbehavior in which the latter may fail to send updates to the FL server due, for instance, to UAVs' disconnectivity from the FL system caused by high mobility, unavailability, or battery depletion. Such challenges may significantly affect the convergence of the FL model. A recent way to tackle these challenges is client selection, based on customized criteria that consider UAV computing power and energy consumption. However, most existing client selection schemes neglected the participants' reliability. Indeed, FL can be targeted by poisoning attacks, in which malicious UAVs upload poisonous local models to the FL server, by either providing targeted false predictions for specifically chosen inputs or by compromising the global model's accuracy through tampering with the local model. Hence, we propose in this paper a novel client selection scheme that enhances convergence by prioritizing fast UAVs with high-reliability scores, while eliminating malicious UAVs from training. Through experiments, we assess the effectiveness of our scheme in resisting different attack scenarios, in terms of convergence and achieved model accuracy. Finally, we demonstrate the performance superiority of the proposed approach compared to baseline methods.
The time–frequency analysis is highly suited technique for non-stationary signal analysis which studies a signal in both time and frequency domains simultaneously. The combination of real time signals of two systems hold quadrature property and become complex in nature. In such cases, information is distinct in positive and negative frequency ranges and can be utilized for signal analysis. In this paper, the flexible analytic wavelet transform (FAWT) is extended to decompose a complex signal in positive and negative frequency ranges. The Hilbert transform (HT) is applied to formulate the time frequency representation with positive and negative frequency ranges without using ideal band-pass filter. Moreover, genetic algorithm based method is developed for parameter optimization of FAWT with respect to minimization of bandwidth in low pass frequency of last level. Proposed method is compared with the existing method and extended for unmanned aerial vehicles (UAV) state identification using radio frequency (RF) signal intercepted in clean, Blue-tooth, Wi-Fi (WIFI), and both types of noisy environment. The complex RF signal is decomposed into positive and negative frequency components which are utilized for statistical features computation and classification. The UAV state
identification system employed two stage identification, initially for UAV type identification followed by state identification. The developed method gives promising results for UAV type and state identification which is useful for UAV surveillance system development.
With the high flexibility of supporting resource-intensive and time-sensitive applications, unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) is proposed as an innovational paradigm to support the mobile users (MUs). As a promising technology, digital twin (DT) is capable of timely mapping the physical entities to virtual models, and reflecting the MEC network state in real-time. In this paper, we first propose an MEC network with multiple movable UAVs and one DT-empowered ground base station to enhance the MEC service for MUs. Considering the limited energy resource of both MUs and UAVs, we formulate an online problem of resource scheduling to minimize the weighted energy consumption of them. To tackle the difficulty of the combinational problem, we formulate it as a Markov decision process (MDP) with multiple types of agents. Since the proposed MDP has huge state space and action space, we propose a deep reinforcement learning approach based on multi-agent proximal policy optimization (MAPPO) with Beta distribution and attention mechanism to pursue the optimal computation offloading policy. Numerical results show that our proposed scheme is able to efficiently reduce the energy consumption and outperforms the benchmarks in performance, convergence speed and utilization of resources.
Over the past decade, unmanned aerial vehicles (UAVs) have demonstrated increasing promise. In this context, we provide a review on swarm intelligence algorithms that play an extremely important role in multiple UAV collaborations. The study focuses on four aspects we consider relevant for the topic: collision avoidance, task assignment, path planning, and formation reconfiguration. A comprehensive investigation of selected typical algorithms that analyses their merits and demerits in the context of multi-UAV collaboration is presented. This research summarises the basic structure of swarm intelligence algorithms, which consists of several fundamental phases; and provides a comprehensive survey of swarm intelligence algorithms for the four aspects of multi-UAV collaboration. Besides, by analysing these key technologies and related applications, the research trends and challenges are highlighted. This broad review is an outline for scholars and professionals in the field of UAV swarms.
Since the invention in 2016, federated learning (FL) has been a key concept of artificial intelligence, in which the data of FL users needs not to be uploaded to the central server. However, performing FL tasks may not be feasible due to the unavailability of terrestrial communications and the battery limitation of FL users. To address these issues, we make use of unmanned aerial vehicles (UAVs) and wireless powered communications (WPC) for FL networks. In order to enable sustainable FL solutions, the UAV equipped with edge computing and WPC capabilities is deployed as an aerial energy source as well as an aerial server to perform FL tasks. We propose a joint algorithm of UAV placement, power control, transmission time, model accuracy, bandwidth allocation, and computing resources , namely energy-efficient FL (E2FL), aiming at minimizing the total energy consumption of the aerial server and users. The E2FL overcomes the original nonconvex problem by an efficient algorithm. We show that sustainable FL solutions can be provided via UAV-enabled WPC through various simulation results. Moreover, the outperformance of E2FL in terms of energy efficiency over several benchmarks emphasizes the need for a joint resource allocation framework rather than optimizing a subset of optimization factors.
This paper proposes a novel design on the wireless powered communication network (WPCN) in dynamic environments under the assistance of multiple unmanned aerial vehicles (UAVs). Unlike the existing studies, where the low-power wireless nodes (WNs) often conform to the coherent harvest-then-transmit protocol, under our newly proposed double-threshold based WN type updating rule, each WN can dynamically and repeatedly update its WN type as an E-node for non-linear energy harvesting over time slots or an I-node for transmitting data over sub-slots. To maximize the total transmission data size of all the WNs over
T
slots, each of the UAVs individually determines its trajectory and binary wireless energy transmission (WET) decisions over times slots and its binary wireless data collection (WDC) decisions over sub-slots, under the constraints of each UAV's limited on-board energy and each WN's node type updating rule. However, due to the UAVs’ tightly-coupled trajectories with their WET and WDC decisions, as well as each WN's time-varying battery energy, this problem is difficult to solve optimally. We then propose a new multi-agent based hierarchical deep reinforcement learning (MAHDRL) framework with two tiers to solve the problem efficiently, where the soft actor critic (SAC) policy is designed in tier-1 to determine each UAV's continuous trajectory and binary WET decision over time slots, and the deep-Q learning (DQN) policy is designed in tier-2 to determine each UAV's binary WDC decisions over sub-slots under the given UAV trajectory from tier-1. Both of the SAC policy and the DQN policy are executed distributively at each UAV. Finally, extensive simulation results are provided to validate the outweighed performance of the proposed MAHDRL approach over various state-of-the-art benchmarks.
Wireless sensor networks are essential for monitoring, control, and surveillance operations, but the limited battery capacity of wireless rechargeable sensor nodes (WRSNs) poses a significant challenge. To address this, on-demand charging from an external power source is fundamental for the effective operation of energy-critical WRSNs. In this article, the wireless charging of the independent WRSNs in a network is modeled as a service in a common competitive market, where multiple charging service providers (CSPs) indulge in a pricing war to maximize their profits by achieving fair market share based on area division. A competitive market scenario is considered where multiple non-cooperative CSPs are strategically located at their fixed grounded stations, and the most economical CSP gets requested from a WRSN for charging. The CSPs are associated with UAV-enabled chargers (UAVEC), which are dispatched to the concerned WRSNs with known positions and recharged wirelessly in a time-bound manner. This paper study a suitable pricing strategy for on-demand charging of the WRSNs in a competitive market scenario; and investigate the existence of Nash equilibrium (NE) for prices and profits of CSPs using a game-theoretic approach. The closed-form expressions of NE conditions and the CSP selection criterion for WRSNs are obtained, and the results are verified through simulations.
Federated learning is defined as one of the solutions that use distributed clients to train and aggregate the target model without sharing the private information of clients in the network. Hence, federated learning may easily handle and tackle the generated information in a more convenient, feasible and secure manner , while involving the intelligent devices to perform testing and training at the local edges. However, the design of such federated learning solutions faces multiple difficulties due to the cost and complexity of information computation , along with a high energy consumption. Furthermore, the identification of resource-constrained devices which ensures significant power consumption along with privacy and security to the devices makes it critical to attain a sustainable federated learning environment. The aim of this paper is to integrate feedback-based and objective trust models to accurately identify the behavior of communicating devices in the network. The secure transmission of information forwarded by trusted IoT devices is computed by the objective model. In addition, the energy consumption required for measuring the authenticity of a device can be further diminished using a behavior and feedback trusted model. In addition, the past history behavior and feedback received from neighboring devices may also contribute to achieving an efficient and sustainable security mechanism with reduced communication steps and computational delay in the network. The proposed solution is evaluated to analyze the feasibility and performance in terms of energy consumption and resource utilization, while also considering various security metrics for comparison against existing methods. The proposed mechanism out-performed 87% improvement in terms of security metrics in comparison of existing approaches.
Unmanned aerial vehicles (UAVs) foster the development of multifield applications and also present opportunities for malicious activities, and thus, comprehensive surveillance of UAVs is indispensable. Taking advantage of rapidly developing communication technology, perceptive mobile networks (PMNs) are emerging to integrate sensing capability into cellular networks, which paves the way for ubiquitous networked sensing with cooperation among interconnected base stations (BSs). This article explores key issues in designing PMNs for reliable UAV surveillance, e.g., sensing coverage, network clutter, and the performance tradeoff between sensing and communication. Procedures and key strategies of cooperative sensing in PMNs are also presented, highlighting the method of conducting cooperative sensing by multiple BSs to reap spatial diversity gain. Finally, we depict our vision for future research directions of cooperative sensing in PMNs.
Unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) systems have emerged as a promising technology with the capability to expand terrestrial networks. UAVs, working as edge computing nodes and mobile base stations, can be deployed closer to user equipment (UEs). However, with the rapid increase of UEs, the scarcity of spectrum resources and computing resources has become a critical challenge for future mobile communication systems. Additionally, the inherent characteristics of wireless transmission and untrusted broadcasting pose significant security and privacy concerns for multi-UAV networks. To address these issues, this paper presents a blockchain-based resource trading mechanism (BRTM) and a double auction-based resource trading algorithm (DARA) for multi-UAV edge computing systems. It combines blockchain technology with double auction theory to ensure the security and fairness of resource trading. The relations between UEs and UAVs as a two-stage Stackelberg game is formulated and a pricing-based incentive strategy is proposed. The proposed scheme encourages active participation from both UEs and UAVs while maximizing the sum of their utilities. The security assessment and numerical outcomes show that the proposed method is effective and outperforms other benchmark schemes.
The effects of transport development on people’s lives are diverse, ranging from economy to tourism, health care, etc. Great progress has been made in this area, which has led to the emergence of the Internet of Vehicles (IoV) concept. The main objective of this concept is to offer a safer and more comfortable travel experience through making available a vast array of applications, by relying on a range of communication technologies including the fifth-generation mobile networks. The proposed applications have personalized Quality of Service (QoS) requirements, which raise new challenging issues for the management and allocation of resources. Currently, this interest has been doubled with the start of the discussion of the sixth-generation mobile networks. In this context, Network Slicing (NS) was presented as one of the key technologies in the 5G architecture to address these challenges. In this article, we try to bring together the effects of NS implications in the Internet of Vehicles field and show the impact on transport development. We begin by reviewing the state of the art of NS in IoV in terms of architecture, types, life cycle, enabling technologies, network parts, and evolution within cellular networks. Then, we discuss the benefits brought by the use of NS in such a dynamic environment, along with the technical challenges. Moreover, we provide a comprehensive review of NS deploying various aspects of Learning Techniques for the Internet of Vehicles. Afterwards, we present Network Slicing utilization in different IoV application scenarios through different domains; terrestrial, aerial, and marine. In addition, we review Vehicle-to-Everything (V2X) datasets as well as existing implementation tools; besides presenting a concise summary of the Network Slicing-related projects that have an impact on IoV. Finally, in order to promote the deployment of Network Slicing in IoV, we provide some directions for future research work. We believe that the survey will be useful for researchers from academia and industry. First, to acquire a holistic vision regarding IoV-based NS realization and identify the challenges hindering it. Second, to understand the progression of IoV powered NS applications in the different fields (terrestrial, aerial, and marine). Finally, to determine the opportunities for using Machine Learning Techniques (MLT), in order to propose their own solutions to foster NS-IoV integration.
Unmanned aerial vehicles (UAVs) are regarded as a promising solution for mobile edge computing (MEC) systems due to their flexibility and capability to provide computing services to ground terminals (GTs). By leveraging UAVs, the latency in computation tasks can be reduced significantly, particularly in disaster scenarios. Additionally, Reconfigurable Intelligent Surfaces (RIS) have emerged as a novel technology for enhancing the wireless propagation environment in wireless networks. This paper proposes a multi-UAV assisted MEC system where computation tasks of GTs can be computed locally or partially offloaded to UAVs. Furthermore, practical RIS phase shift designs are considered to enhance the communication performance between GTs and UAVs. To minimize the system delay and achieve fairness among GTs, the computation offloading strategy, trajectory of the UAVs are optimized using a markov decision process. Simultaneously, the RIS phase shift is optimized through an alternating optimization algorithm. Additionally, a cooperative multi-agent deep reinforcement learning framework is developed to obtain a optimal solution by employing the multi-agent twin delayed deep deterministic policy gradient (MATD3) algorithm. Numerical results indicate that MATD3 can effectively improve the system delay and fairness performance of the RIS-assisted multi-UAV MEC system, as compared to benchmark solutions.
With the exponential growth in mobile data volume, cellular networks are under severe capacity pressure. To address this issue, Unmanned Aerial Vehicles (UAVs) are being used as mobile Base Stations (BSs) for traffic offloading. However, coordinating and scheduling traffic across multiple UAVs and BSs remains a challenge in complex environments. This paper proposes a solution that optimizes UAV deployment locations and user resource allocation, the goal is to maximize traffic offloading and minimize UAV energy consumption simultaneously. We introduce a hierarchical intelligent traffic offloading network optimization framework based on Deep Federated Learning (DFL). Through federated learning, the UAV swarm is organized hierarchically. Additionally, we developed the CPRAFT algorithm, which uses capacity values as criterion to select the Leader UAV (L-UAV). The L-UAV then becomes the top-level central server for model aggregation in the federated learning environment. Furthermore, we formalize the traffic offloading problem as a Markov Decision Process (MDP). Based on MDP, this paper proposes FL-SNTD3 algorithm to optimize dynamic decision-making, which adapts to the ever-changing network environment and fluctuating traffic demands. Simulation experiments demonstrate that the proposed framework and algorithm exhibit outstanding performance in various aspects, providing robust support for future research in intelligent traffic offloading networks.
Unmanned Aerial Vehicles (UAVs) are vulnerable to network attacks. Designing an effective intrusion detection system (IDS) for UAVs is crucial. However, UAVs have limited computing resources and need to deal with massive amounts of network data, which further increases the difficulty of detection. Moreover, most existing IDSs have large parameters. In this study, we develop a tiny machine learning-based IDS to solve the above issue. We first establish an improved fuzzy rough set (FRS) model based on adaptive neighborhoods. Then, using the proposed FRS model, we employ a feature selection (FS) method to select optimal features and reduce overall computational cost of the IDS. Furthermore, we proposed a tiny intrusion detection model that attains high-precision detection via shallow deep learning. Additionally, the proposed method can address intrusion detection problems in scenarios with partial data missing. According to the evaluations, the proposed method can effectively address intrusion detection in UAVs.
IoT (Internet of Things) has revolutionized the world with its applications like home automation,
transportation, agriculture, etc. IoT-based Unmanned Aerial Vehicle (UAV) networks are an
emerging field that introduces the UAV network with the power of the Internet.IoT-based
UAV networks are a network of interconnected UAVs which are then equipped with sensors, a
microcontroller to exchange collected data with each other, and a GCS (Ground Control Station)
over the internet. IoT-based UAV network system is used for surveillance, monitoring, payload
delivery, and much more which generate a plethora of sensitive information which can be
obtained by adversaries in the middle. The communication between UAVs and GCS is vulnerable
to security threats using jamming and eavesdropping attacks. The message between UAV and
GCS is not enciphered which makes them error-prone. Attacks like GPS (Global Positioning
System) spoofing can be possible by sending fake coordinates to trick UAV’s control. Although
much effort has been made toward UAV security. However, there is a dearth of descriptive
reviews on secure communication in IoT-based UAV networks. The purpose of this paper is to
find and examine peer-reviewed literature that discusses previous six-year established papers
with existing attacks such as physical, and logical attacks with suggested solutions such as
trajectory planning, lightweight schemes, solutions based on blockchain, quantum cryptography,
etc. This paper analyzes the secure communication network between UAVs by systematically
answering research questions based on the research methodology for the relevant study. Finally,
the paper addresses several issues and guidelines for future research.
It is crucial that communication between an unmanned aerial vehicle (UAV) and the ground station (GS) be secure, and both devices should mutually authenticate each other to ensure that an adversary cannot obtain communicated information. Moreover, dynamically adapting the session time of an authenticated session can decrease the idle time of a session and consequently reduce the window of opportunity for an adversary to interfere with the communication link. In light of these considerations, we design a
physically unclonable function (PUF)
and
fuzzy extractor
-based UAV-GS authentication mechanism called
UAV Authentication with Adaptive Session (UAAS)
. In UAAS, both UAV-GS and UAV-UAV authentication are two-way. We use a
Thompson Sampling (TS)
-based approach to intelligently adapt the duration of a session. Both formal and informal security proofs are presented along with a computation and communication cost analysis to analyze the performance of UAAS. It is noted that UAAS is secure against various well-known attacks and has a lower communication cost than several baseline mechanisms. Due to the noise reduction that occurs in PUFs, the computational cost of UAAS is higher than that of baselines that ignore noise. Also, our simulation shows that UAAS significantly outperforms baselines when it comes to network performance.
Unmanned aerial vehicles (UAVs) are projected to be utilized in a variety of unexpected applications, including agriculture, firefighting, emergency response, intelligent transportation, and so on. Wireless communication is one of the primary facilitators in bringing UAVs into a new phase in such applications. To realize the vision in fifth-generation (5G) networks, we propose a 5G-integration of the flexible multi-UAV system and the Open Radio Access Network (O-RAN) architecture, named U-ORAN. Although different studies have been proposed to optimize the UAV trajectory and resource allocation in the radio access network (RAN), our work is the first study to investigate the benefits of adopting UAVs in the O-RAN architecture. In U-ORAN, we consider a flying base station system and propose a joint optimization problem of multi-UAV trajectory and offloading tasks (UTOT) in which UTOT can optimize the routes from users to the core network as well as resource allocation to process offloading tasks. We decompose UTOT into two sub-problems and provide learning solutions based on the multi-agent reinforcement learning and online learning methodologies, both of which are well supported by the O-RAN architecture. Our intensive numerical simulations show that the proposed approaches outperform in a variety of settings and validation scenarios.
This paper considers the problem of an unmanned aerial vehicle (UAV)-enabled cloud network under partial computation offloading scenario, where multiple UAV-mounted aerial base stations are employed to serve a group of remote Internet of Things ground-based smart devices (ISDs). The main objective of this work is to maximize energy efficiency by minimizing the number of needed drones while minimizing the cost associated with serving the ISDs under some realistic quality of service constraints. To that end, we aim to jointly optimize the three-dimensional (3D) UAV placements, transmit power, and cloud resources. This represents a challenging, non-convex and NP-hard optimization problem. In this work, we decompose the optimization problem into three separate subproblems namely, two-dimensional (2D) UAV positioning, UAV altitude optimization, and UAV-cloud resource association. These subproblems are solved using a modified global K-means, successive convex approximation, and successive linear programming techniques. A comprehensive simulation study and comparative evaluation against the state-of-the-art (SOTA) algorithms are conducted to demonstrate the utility of the proposed approach and its benefits in applications of interest.