Deep Reinforcement Learning (DRL) for traffic light control adaptively adjusts signals based on real-time traffic conditions to alleviate urban congestion. However, transmitting image data from intersection cameras results in high communication overhead and latency in practical deployments. Traditional DRL methods lack interpretability when selecting some common traffic features (e.g., traffic densities, speed) as state input. Inspired by 6G semantic communication that transmits semantic information rather than raw image data, this paper proposes xTL, an eXplainable AI (XAI)-guided DRL development approach to achieve semantic state design and address communication overhead. Unlike traditional DRL that heavily depends on empirical tuning, xTL allows human experts to efficiently guide and interpret model design by using XAI-generated explanations to distill lightweight semantic traffic features from image data. Utilizing SHapley Additive exPlanations (SHAP)-generated saliency maps, we identify a new critical feature: the location of the last vehicle in the first platoon on each incoming road, which can interpret intersection traffic dynamics and effectively improve traffic. Experiments on two urban intersections in SUMO demonstrate that xTL slashes communication costs by over 90% and shortens DRL training duration by about 21%, without compromising traffic control effectiveness.

Software-defined networking (SDN) has revolutionized modern mobile networks by enhancing flexibility and scalability, but its centralized architecture remains a prime target for Distributed Denial of Service (DDoS) attacks. This paper presents a novel detection framework that employs frequency-domain analysis to uncover hidden attack patterns within PacketIn message fluctuations. To further refine detection accuracy, eXplainable AI (XAI) is integrated to optimize the detection accuracy of unseen types of DDoS attacks and enhance the interpretability of the model. Our approach enables a more precise attack classification while minimizing false positives using XAI-driven knowledge transfer. Experimental evaluations confirm that this method significantly strengthens SDN resilience against evolving DDoS threats, providing a more adaptive and intelligent defense mechanism.

The Internet of Things (IoT) connects numerous heterogeneous devices that collect and generate substantial sensitive data to train intelligent models. To protect the privacy of such data, Federated Learning (FL) provides a privacy-preserving distributed machine learning approach. However, these IoT devices are highly heterogeneous, differing in computing and communication capabilities. During the FL training, these variations may result in significant communication delays and computational inefficiencies. We propose FedFleet, an efficient FL framework with a novel clustering method, to accelerate model training in heterogeneous IoT device scenarios. Unlike the existing FL clustering method, which first determines the cluster header, FedFleet clusters devices into different fleets corresponding to the number of edge servers. Specifically, FedFleet divides devices into multiple fleets based on their performance profiles, including computation and communication time. Devices in the same fleet synchronously aggregate their local model parameters at the edge server and then asynchronously update the global model to the central server. Experiments with various models and datasets show that FedFleet outperforms the state-of-the-art FL algorithm, reducing convergence time by 10.2%-56.4% while achieving the same accuracy rate. It also exhibits minimal performance fluctuations under varying device heterogeneity levels, dropout rates, and network conditions. Moreover, FedFleet demonstrates better scalability for high device heterogeneity environments.

As autonomous Unmanned Aerial Vehicles (UAVs) are becoming more and more prevalent in everyday life, it is paramount that UAVs are equipped with effective obstacle avoidance capabilities. Edge AI, which runs AI on-device (e.g., on-UAV) or on edge servers, offers many advantages to traditional cloud-based AI when applied to the problem of UAV obstacle avoidance. Literature shows that deep reinforcement learning (DRL) applied to robots (e.g., UAVs) is an effective method of obstacle avoidance. One key issue associated with DRL applied to robotics is the time required to train when the environment is complicated. In this paper, we propose a DRL-based UAV obstacle avoidance system that leverages edge AI. Our system distributes the training and inferencing processes of DRL by splitting large environments into multiple smaller environments. Our main goal is to make DRL training faster and more feasible under relatively large and complex environments. We demonstrate the effectiveness of our system in 3D simulation and all our code is open-sourced on GitHub.

Artificial Intelligence used in future networks is vulnerable to biases, misclassifications, and security threats, which seeds constant scrutiny in accountability. Explainable AI (XAI) methods bridge this gap in identifying unaccounted biases in black-box AI/ML models. However, scaffolding attacks can hide the internal biases of the model from XAI methods, jeopardizing any auditory or monitoring processes, service provisions, security systems, regulators, auditors, and end-users in future networking paradigms, including Intent-Based Networking (IBN). For the first time ever, we formalize and demonstrate a framework on how an attacker would adopt scaffoldings to deceive the security auditors in Network Intrusion Detection Systems (NIDS). Furthermore, we propose a detection method that auditors can use to detect the attack efficiently. We rigorously test the attack and detection methods using the NSL-KDD. We then simulate the attack on 5G network data. Our simulation illustrates that the attack adoption method is successful, and the detection method can identify an affected model with extremely high confidence.

Dynamic Spectrum Sharing (DSS) is a pivotal technology for optimizing spectrum utilization and fostering efficient sharing among diverse users. However, existing DSS approaches face significant challenges related to security and privacy vulnerabilities, leading to fraudulent practices within spectrum marketplaces. In this paper, we introduce Spect-NFT, a novel framework leveraging Non Fungible Tokens (NFTs) to address these limitations and enhance the spectrum-sharing ecosystem’s efficiency and revenue. Spect-NFT employs NFTs to authenticate ownership of spectrum bands, mitigating fraudulent activities and improving trust among participants. Additionally, Spect-NFT introduces digital Permission Tokens (PTs) to facilitate seamless spectrum sharing between primary users (PUs) and secondary users (SUs) enabling the sharing of a single NFT among multiple owners. We present a methodology for converting spectrum licenses into NFTs and demonstrate the feasibility of our approach using the Ethereum Blockchain. Our proof of concept solution showcases Spect-NFT’s tamperresistant characteristics and its potential to revolutionize DSS paradigms.

We demonstrate SPATIAL, a proof-of-concept system that augments modern applications with capabilities to analyze trustworthy properties of AI models. The practical analysis of trustworthy properties is key to guaranteeing the safety of users and overall society when interacting with AI -driven applications. SPATIAL implements AI dashboards to introduce human-in-the-loop capabilities for the construction of AI models. SPATIAL allows different stakeholders to obtain quantifiable insights that characterize the decision making process of AI. This information can then be used by the stakeholders to comprehend possible issues that influence the performance of AI models, such that the issues can be resolved by human operators. Through rigorous benchmarks and experiments in a real-world industrial application, we demonstrate that SPATIAL can easily augment modern applications with metrics to gauge and monitor trustworthiness. However, this, in turn, increases the complexity of developing and maintaining the systems implementing AI. Our work paves the way towards augmenting modern applications with trustworthy AI mechanisms and human oversight approaches.

Despite its enormous economical and societal impact, lack of human-perceived control and safety is re-defining the design and development of emerging AI-based technologies. New regulatory requirements mandate increased human control and oversight of AI, transforming the development practices and responsibilities of individuals interacting with AI. In this paper, we present the SPATIAL architecture, a system that augments modern applications with capabilities to gauge and monitor trustworthy properties of AI inference capabilities. To design SPATIAL, we first explore the evolution of modern system architectures and how AI components and pipelines are integrated. With this information, we then develop a proof-of- concept architecture that analyzes AI models in a human-in-the- loop manner. SPATIAL provides an AI dashboard for allowing individuals interacting with applications to obtain quantifiable insights about the AI decision process. This information is then used by human operators to comprehend possible issues that influence the performance of AI models and adjust or counter them. Through rigorous benchmarks and experiments in real- world industrial applications, we demonstrate that SPATIAL can easily augment modern applications with metrics to gauge and monitor trustworthiness, however, this in turn increases the complexity of developing and maintaining systems implementing AI. Our work highlights lessons learned and experiences from augmenting modern applications with mechanisms that support regulatory compliance of AI. In addition, we also present a road map of on-going challenges that require attention to achieve robust trustworthy analysis of AI and greater engagement of human oversight.

The target of reducing travel time only is insufficient to support the development of future smart transportation systems. To align with the United Nations Sustainable Development Goals (UN-SDG), a further reduction in fuel consumption and emissions, improvements in traffic safety, and the ease of infrastructure deployment and maintenance should also be considered. Most existing research in sustainable urban traffic control adjusts either traffic light signals or vehicle speed. Adaptive traffic light signal control can increase the intersection throughput and reduce travel time as well as energy consumption and emissions. Connected Autonomous Vehicles (CAVs) can proactively control vehicle acceleration to achieve more stable traffic nearby with relatively higher driving velocity (i.e., lower fuel consumption and CO 2 emissions) and maintain a safe distance from the surrounding traffic (i.e., longer time-to-collision).

With the rapid progression of communication and localisation of big data over billions of devices, distributed Machine Learning (ML) techniques are emerging to cater for the development of Artificial Intelligence (AI)-based services in a distributed manner. Federated Learning (FL) is such an innovative approach to achieve a privacy-preserved AI that facilitates ML model sharing and aggregation while keeping the participants’ data at the original source. However, recent research has investigated threats from poisoning attacks in FL. Several robust algorithms based on techniques such as similarity metrics or anomaly filtering are proposed as solutions. Yet, these approaches do not focus on investigating the intentions of the attackers or providing justifications and evidence for suspecting the behaviour of clients who are considered poisoners. Therefore, we propose SHERPA, a robust algorithm that uses Shapley Additive Explanations (SHAP) to identify potential poisoners in an FL system. Based on this, we develop a novel algorithm to differentiate poisoners via feature attribution clustering. We launch data poisoning attacks for different scenarios on multiple datasets and showcase our solution to mitigate the attacks. Furthermore, we show that privacy-targeted poisoning attacks can be mitigated with our approach. Accompanying the Explainable AI (XAI) technique for defence, our study reveals the potential for post-hoc feature attributions in countering data poisoning attacks with better explainability and improved justification in eliminating potentially malicious clients in the aggregation process.

Federated Learning (FL) is an emerging privacy-preserved distributed Machine Learning (ML) technique where multiple clients can contribute to training an ML model without sharing private data. Even though FL offers a certain level of privacy by design, recent works show that FL is vulnerable to numerous privacy attacks. One of the key features of FL is the continuous training of FL models over many cycles through time. Observing changes in FL models over time can lead to inferring information on changes to private and sensitive data used in the FL process. However, this potential leakage of private information is not yet investigated significantly. Therefore, this paper introduces a new form of inference-based privacy attacks called FL Time Inference Attacks (FL-TIA). These attacks can reveal private time-related properties such as the presence or absence of a sensitive feature over time and if it is periodical. We consider two forms of such FL-TIA: i.e. identifying changes in membership of target data records over training rounds and detecting significant events in clients over time by observing differences in FL models. We use the network Intrusion Detection System (IDS) as a use case to demonstrate the impact of our attack. We propose a continuous updating attack model method for membership variation detection by sustaining the accuracy of the attack. Furthermore, we provide an efficient detection method that can identify model changes using cosine similarity metric and one-shot mapping on shadow model training.

Reducing unexpected urban traffic congestion caused by en-route events (e.g., road closures, car crashes, etc.) often requires fast and accurate reactions to choose the best-fit traffic signals. Traditional traffic light control systems, such as SCATS and SCOOT, are not efficient as their traffic data provided by induction loops has a low update frequency (i.e., longer than 1 minute). Moreover, the traffic light signal plans used by these systems are selected from a limited set of candidate plans pre-programmed prior to unexpected events’ occurrence. Recent research demonstrates that camera-based traffic light systems controlled by deep reinforcement learning (DRL) algorithms are more effective in reducing traffic congestion, in which the cameras can provide high-frequency high-resolution traffic data. However, these systems are costly to deploy in big cities due to the excessive potential upgrades required to road infrastructure. In this paper, we argue that Unmanned Aerial Vehicles (UAVs) can play a crucial role in dealing with unexpected traffic congestion because UAVs with onboard cameras can be economically deployed when and where unexpected congestion occurs. Then, we propose a system called "AVARS" that explores the potential of using UAVs to reduce unexpected urban traffic congestion using DRL-based traffic light signal control. This approach is validated on a widely used open-source traffic simulator with practical UAV settings, including its traffic monitoring ranges and battery lifetime. Our simulation results show that AVARS can effectively recover the unexpected traffic congestion in Dublin, Ireland, back to its original uncongested level within the typical battery life duration of a UAV.

The target of reducing travel time only is insufficient to support the development of future smart transportation systems. To align with the United Nations Sustainable Development Goals (UN-SDG), a further reduction of fuel and emissions, improvements of traffic safety, and the ease of infrastructure deployment and maintenance should also be considered. Different from existing work focusing on optimizing the control in either traffic light signal (to improve the intersection throughput), or vehicle speed (to stabilize the traffic), this paper presents a multi-agent Deep Reinforcement Learning (DRL) system called CoTV, which Cooperatively controls both Traffic light signals and Connected Autonomous Vehicles (CAV). Therefore, our CoTV can well balance the reduction of travel time, fuel, and emissions. CoTV is also scalable to complex urban scenarios by cooperating with only one CAV that is nearest to the traffic light controller on each incoming road. This avoids costly coordination between traffic light controllers and all possible CAVs, thus leading to the stable convergence of training CoTV under the large-scale multi-agent scenario. We describe the system design of CoTV and demonstrate its effectiveness in a simulation study using SUMO under various grid maps and realistic urban scenarios with mixed-autonomy traffic.

Future worldwide 6G research will drive the evolution of emerging intelligent control technologies, such as intelligent speed advisory systems (ISA), to a more advanced generation. As a special type of ISA, consensus-based speed advisory systems (CSAS) can be widely used to recommend a consensus speed for a vehicle platoon, enabling minimizing energy consumption or emissions over a planned route. Recently, speed recommendation services that protect data privacy (i.e., how to obtain an optimal speed in a privacy-preserving way) have drawn tremendous attention. However, current approaches could still encounter service trust issues with central servers and the malicious behavior of vehicles. Furthermore, existing research lacks considering road safety constraints (i.e., safe distance between adjacent vehicles and road speed limits) that are essential for the practical deployment of CSAS. To address the above issues, this paper proposes Eco-CSAS, a safe and eco-friendly consensus speed advisory system using blockchain. We formulate an optimization problem subject to the minimum following distance and maximum road speed limit to minimize the energy consumption of the automatic vehicle platoon. In addition, we introduce a consortium blockchain and cryptographic primitives to ensure service trust and data privacy. We implement the system on the Hyperledger platform, and experimental results show that the system can achieve speed recommendations in a trustworthy and privacy-preserving manner while ensuring a secure platoon.

The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial intelligence (AI) technologies, for instance, computer vision and path planning. These AI methods must process data and provide decisions while ensuring low latency and low energy consumption. However, the existing cloud-based AI paradigm finds it difficult to meet these strict UAV requirements. Edge AI, which runs AI on-device or on edge servers close to users, can be suitable for improving UAV-based IoT services. This article provides a comprehensive analysis of the impact of edge AI on key UAV technical aspects (i.e., autonomous navigation, formation control, power management, security and privacy, computer vision, and communication) and applications (i.e., delivery systems, civil infrastructure inspection, precision agriculture, search and rescue (SAR) operations, acting as aerial wireless base stations (BSs), and drone light shows). As guidance for researchers and practitioners, this article also explores UAV-based edge AI implementation challenges, lessons learned, and future research directions.

The number of drivers using on-board systems to navigate through urban areas is increasing. Drivers get real time information regarding traffic conditions and change their routes accordingly. Adapting a route clearly enables drivers to avoid closed roads or circumvent major hotspots. However, given the non-linearity of the traffic dynamics in urban environments, choosing a route based only on current traffic load or current average vehicle speed is not a guaranty of a lower overall travel time. In this work, we design an evolutionary system to search for better surrogate travel cost that drivers could optimise in their rerouting to achieve better overall travel times. Our system uses the Grammar-Guided Genetic Programming algorithm to evolve surrogate travel cost expressions and evaluate their performances on a micro traffic simulator. Our system is able to evolve different expressions that meet characteristics of specific urban environments instead of a one size fits all expression. We have seen in our experimental study on a traffic scenario representing Dublin city centre that our system is able to evolve surrogate travel cost expressions with ~34% and ~10% improvements in average travel time over the no rerouting and the average travel speed based rerouting algorithms.

Flight efficiency indicators reported monthly in the European area by the Performance Review Unit (PRU) help the air traffic management (ATM) community determine if excessive distances are being flown (compared with the ideal lengths of flight routes). Recent research, however, provides more indicators that comprehensively capture flight efficiencies in terms of other factors including fuel consumption, time adherence, and route charges. The efficacy of all of these indicators, however, is diminished as they are currently only available almost a month after flights take place. This is not sufficiently timely to use these indicators for the alleviation of unpredictable hotspots (i.e. sectors with congested air traffic), which often leads to unexpected ground delays. This paper proposes a methodology to calculate general flight efficiency indicators on-line in near real-time using nearest point search. A prototype system called ROGER (compRehensive On-line fliGht Efficiency monitoRing) is implemented using Apache Kafka and Spark. ROGER can digest large-scale heterogeneous datasets (i.e. mainly ADS-B data, the next generation aircraft surveillance technology) to compute indicators every 5 seconds. Our experiments on realistic datasets demonstrate that the proposed on-line indicator calculation method can achieve high accuracy compared with existing off-line approaches, and that ROGER can achieve desirable system performance in throughput and latency. A use case is also described showing how ROGER can assist in alleviating hotspots more effectively.

During peak hours in urban areas, unpredictable traffic congestion caused by en route events (e.g., vehicle crashes) increases drivers' travel time and, more seriously, decreases their travel time reliability. In this paper, an original and highly practical vehicle rerouting system, which is called Next Road Rerouting (NRR), is proposed to aid drivers in making the most appropriate next road choice to avoid unexpected congestions. In particular, this heuristic rerouting decision is made upon a cost function that takes into account the driver's destination and local traffic conditions. In addition, the newly designed multiagent system architecture of NRR allows the positive rerouting impacts on local traffic to be disseminated to a larger area through the natural traffic flow propagation within connected local areas. The simulation results based on both synthetic and realistic urban scenarios demonstrate that, compared with the existing solutions, NRR can achieve a lower average travel time while guaranteeing a higher travel time reliability in the face of unexpected congestion. The impacts of NRR on the travel time of both rerouted and nonrerouted vehicles are also assessed, and the corresponding results reveal its higher practicability.