arXiv subjects

Julie McCann

Publications and source records attributed to Julie McCann.

15 recordsLinked to original sources

arXiv2026

Toward a Unified Security and Privacy Framework for AI-Native 6G Networks

Sixth Generation (6G) communication networks are expected to evolve into AI-native, highly autonomous ecosystems that integrate communication, computing, sensing, and artificial intelligence. While these capabilities enable unprecedented connectivity and intelligent services, they also create a highly heterogeneous security and privacy landscape that cannot be addressed through isolated, technology-specific solutions. This paper presents a comprehensive survey of security and privacy in AI-native 6G networks from a cross-layer perspective. We first examine the fragmentation of existing security and privacy approaches across emerging technologies, network architectures, AI systems, and standardization efforts, motivating the need for a unified security and privacy framework. Building upon this framework, we develop a cross-layer threat taxonomy encompassing infrastructure, network and architectural, AI, privacy, and security management domains, and analyze representative threats across key AI-native 6G technologies. Furthermore, we map these threats to corresponding cross-layer countermeasures, including standards harmonization as a security function, and identify critical research gaps and future priorities for secure, interoperable, and trustworthy AI-native 6G ecosystems. Finally, we discuss future research directions toward realizing secure, privacy-preserving, resilient, and globally interoperable 6G networks. This survey provides researchers, practitioners, and standardization communities with a holistic foundation for the design, evaluation, and deployment of trustworthy AI-native 6G systems.

cs.CR↗

arXiv2026

Dual Security for MIMO-OFDM ISAC Systems: Artificial Ghosts or Artificial Noise

Integrated sensing and communication (ISAC) enables the efficient sharing of wireless resources to support emerging applications, but it also gives rise to new sensing-based security vulnerabilities. Here, potential communication security threats whereby confidential messages intended for legitimate users are intercepted, but also unauthorized receivers (Eves) can passively exploit target echoes to infer sensing parameters without users being aware. Despite these risks, the joint protection of sensing and communication security in ISAC systems remains unexplored. To address this challenge, this paper proposes a two-layer dual-secure ISAC framework that simultaneously protects sensing and communication against passive sensing Eves and communication Eves, without requiring their channel state information (CSI). Specifically, transmit beamformers are jointly designed to inject artificial noise (AN) to introduce interference to communication Eves, while deliberately distorting the reference signal available to sensing Eves to impair their sensing capability. Furthermore, the proposed design generates artificial ghosts (AGs) with fake angle-range-velocity profiles observable by all receivers. Legitimate receivers can suppress these AGs, whereas sensing Eves cannot, thereby significantly reducing their probability of correctly detecting the true targets. Numerical results demonstrate that the proposed framework effectively enhances both communication and sensing security, while preserving the performance of communication users and legitimate sensing receivers.

eess.SP↗

arXiv2025

Integrated Sensing, Communication, and Over-The-Air Control of UAV Swarm Dynamics

Coordinated controlling a large UAV swarm requires significant spectrum resources due to the need for bandwidth allocation per UAV, posing a challenge in resource-limited environments. Over-the-air (OTA) control has emerged as a spectrum-efficient approach, leveraging electromagnetic superposition to form control signals at a base station (BS). However, existing OTA controllers lack sufficient optimization variables to meet UAV swarm control objectives and fail to integrate control with other BS functions like sensing. This work proposes an integrated sensing and OTA control framework (ISAC-OTA) for UAV swarm. The BS performs OTA signal construction (uplink) and dispatch (downlink) while simultaneously sensing objects. Two uplink post-processing methods are developed: a control-centric approach generating closed-form control signals via a feedback-looped OTA control problem, and a sensing-centric method mitigating transmission-induced interference for accurate object sensing. For the downlink, a non-convex problem is formulated and solved to minimize control signal dispatch (transmission) error while maintaining a minimum sensing signal-to-noise ratio (SNR). Simulation results show that the proposed ISAC-OTA controller achieves control performance comparable to the benchmark optimal control algorithm while maintaining high sensing accuracy, despite OTA transmission interference. Moreover, it eliminates the need for per-UAV bandwidth allocation, showcasing a spectrum-efficient method for cooperative control in future wireless systems.

eess.SP↗

arXiv2024

Overcoming Growth-Induced Forgetting in Task-Agnostic Continual Learning

In continual learning (CL), model growth enhances adaptability to new data. However, when model growth is applied improperly, especially in task-agnostic CL, where the entire grown model is used for inference, it can lead to severe degradation of learned knowledge, a problem we term growth-induced forgetting. Most existing methods that adopt model growth to improve adaptability often overlook the forgetting issue, resulting in compromised knowledge retention, making them unsuitable for task-agnostic settings. To promote both adaptability and knowledge retention with model growth, we identify the key: gradient and parameter sparsity. Introducing SparseGrow, which increases gradient sparsity through layer expansion and gradient gating to enable focused updates on parameters while preserving critical parameters, thus inhibiting forgetting. Moreover, it promotes parameter sparsity with sparse initialization and training, aiming at better control of model plasticity, improving adaptability over new data. Extensive experiments across diverse datasets, task-agnostic settings, and a large number of tasks demonstrate the necessity of controlled layer expansion and validate the effectiveness of SparseGrow in achieving high adaptability while minimizing forgetting in continual learning. By enabling model growth with sparsified gradients and parameters, SparseGrow paves the way for building scalable lifelong learning systems capable of continual adaptation with better knowledge retention.

cs.LG↗

arXiv2024

Belt and Braces: When Federated Learning Meets Differential Privacy

Federated learning (FL) has great potential for large-scale machine learning (ML) without exposing raw data.Differential privacy (DP) is the de facto standard of privacy protection with provable guarantees.Advances in ML suggest that DP would be a perfect fit for FL with comprehensive privacy preservation. Hence, extensive efforts have been devoted to achieving practically usable FL with DP, which however is still challenging.Practitioners often not only are not fully aware of its development and categorization, but also face a hard choice between privacy and utility. Therefore, it calls for a holistic review of current advances and an investigation on the challenges and opportunities for highly usable FL systems with a DP guarantee. In this article, we first introduce the primary concepts of FL and DP, and highlight the benefits of integration. We then review the current developments by categorizing different paradigms and notions. Aiming at usable FL with DP, we present the optimization principles to seek a better tradeoff between model utility and privacy loss. Finally, we discuss future challenges in the emergent areas and relevant research topics.

cs.CR↗

arXiv2024

Explainable Adversarial Learning Framework on Physical Layer Secret Keys Combating Malicious Reconfigurable Intelligent Surface

Reconfigurable intelligent surfaces (RIS) can both help and hinder the physical layer secret key generation (PL-SKG) of communications systems. Whilst a legitimate RIS can yield beneficial impacts, including increased channel randomness to enhance PL-SKG, a malicious RIS can poison legitimate channels and crack almost all existing PL-SKGs. In this work, we propose an adversarial learning framework that addresses Man-in-the-middle RIS (MITM-RIS) eavesdropping which can exist between legitimate parties, namely Alice and Bob. First, the theoretical mutual information gap between legitimate pairs and MITM-RIS is deduced. From this, Alice and Bob leverage adversarial learning to learn a common feature space that assures no mutual information overlap with MITM-RIS. Next, to explain the trained legitimate common feature generator, we aid signal processing interpretation of black-box neural networks using a symbolic explainable AI (xAI) representation. These symbolic terms of dominant neurons aid the engineering of feature designs and the validation of the learned common feature space. Simulation results show that our proposed adversarial learning- and symbolic-based PL-SKGs can achieve high key agreement rates between legitimate users, and is further resistant to an MITM-RIS Eve with the full knowledge of legitimate feature generation (NNs or formulas). This therefore paves the way to secure wireless communications with untrusted reflective devices in future 6G.

cs.CR↗

arXiv2022

ACE: Towards Application-Centric Edge-Cloud Collaborative Intelligence

Intelligent applications based on machine learning are impacting many parts of our lives. They are required to operate under rigorous practical constraints in terms of service latency, network bandwidth overheads, and also privacy. Yet current implementations running in the Cloud are unable to satisfy all these constraints. The Edge-Cloud Collaborative Intelligence (ECCI) paradigm has become a popular approach to address such issues, and rapidly increasing applications are developed and deployed. However, these prototypical implementations are developer-dependent and scenario-specific without generality, which cannot be efficiently applied in large-scale or to general ECC scenarios in practice, due to the lack of supports for infrastructure management, edge-cloud collaborative service, complex intelligence workload, and efficient performance optimization. In this article, we systematically design and construct the first unified platform, ACE, that handles ever-increasing edge and cloud resources, user-transparent services, and proliferating intelligence workloads with increasing scale and complexity, to facilitate cost-efficient and high-performing ECCI application development and deployment. For verification, we explicitly present the construction process of an ACE-based intelligent video query application, and demonstrate how to achieve customizable performance optimization efficiently. Based on our initial experience, we discuss both the limitations and vision of ACE to shed light on promising issues to elaborate in the approaching ECCI ecosystem.

cs.DC↗

arXiv2020

Spatiotemporal Modelling of Multi-Gateway LoRa Networks with Imperfect SF Orthogonality

Meticulous modelling and performance analysis of Low-Power Wide-Area (LPWA) networks are essential for large scale dense Internet-of-Things (IoT) deployments. As Long Range (LoRa) is currently one of the most prominent LPWA technologies, we propose in this paper a stochastic-geometry-based framework to analyse the uplink transmission performance of a multi-gateway LoRa network modelled by a Matern Cluster Process (MCP). The proposed model is first to consider all together the multi-cell topology, imperfect spreading factor (SF) orthogonality, random start times, and geometric data arrival rates. Accounting for all of these factors, we initially develop the SF-dependent collision overlap time function for any start time distribution. Then, we analyse the Laplace transforms of intra-cluster and inter-cluster interference, and formulate the uplink transmission success probability. Through simulation results, we highlight the vulnerability of each SF to interference, illustrate the impact of parameters such as the network density, and the power allocation scheme on the network performance. Uniquely, our results shed light on when it is better to activate adaptive power mechanisms, as we show that an SF-based power allocation that approximates LoRa ADR, negatively impacts nodes near the cluster head. Moreover, we show that the interfering SFs degrading the performance the most depend on the decoding threshold range and the power allocation scheme.

eess.SP↗

arXiv2019

XPC: Fast and Reliable Synchronous Transmission Protocols for 2-Phase Commit and 3-Phase Commit

One of the major challenges for the engineering of wireless sensing systems is to improve the software abstractions and frameworks that are available to programmers while ensuring system reliability and efficiency. The distributed systems community have developed a rich set of such abstractions for building dependable distributed systems connected using wired networks, however after 20 years research many of these elude wireless sensor systems. In this paper we present X Process Commit (XPC) an atomic commit protocol framework that utilizes Synchronous Transmission (ST). We also introduce Hybrid, a technique that allows us to exploit the advantages of the Glossy and Chaos Synchronous Transmission primitives to get lower latency and higher reliability than either. Using XPC and Hybrid we demonstrate how to build protocols for the classical 2-phase and 3-phase commit abstractions and evaluate these demonstrating significantly improved performance and reliability than the use of Glossy or Chaos individually as dissemination primitives. We address how we overcame the timing challenges of bringing Glossy and Chaos together to form Hybrid and through extensive experimentation demonstrate that it is robust to in-network radio interference caused by multiple sources. We are first to present testbed results that show that Hybrid can provide almost 100% reliability in a network of nodes suffering from various levels of radio interference.

cs.DC↗

arXiv2019

Resource Allocation for Non-Orthogonal Multiple Access (NOMA) Enabled LPWA Networks

In this paper, we investigate the resource allocation for uplink non-orthogonal multiple access (NOMA) enabled low-power wide-area (LPWA) networks to support the massive connectivity of users/nodes. Here, LPWA nodes communicate with a central gateway through resource blocks like channels, transmission times, bandwidths, etc. The nodes sharing the same resource blocks suffer from intra-cluster interference and possibly inter-cluster interference, which makes current LPWA networks unable to support the massive connectivity. Using the minimum transmission rate metric to highlight the interference reduction that results from the addition of NOMA, and while assuring user throughput fairness, we decompose the minimum rate maximization optimization problem into three sub-problems. First, a low-complexity sub-optimal nodes clustering scheme is proposed assigning nodes to channels based on their normalized channel gains. Then, two types of transmission time allocation algorithms are proposed that either assure fair or unfair transmission time allocation between LPWA nodes sharing the same channel. For a given channel and transmission time allocation, we further propose an optimal power allocation scheme. Simulation evaluations demonstrate approximately 100dB improvement of the selected metric for a single network with 4000 active nodes.

eess.SP↗

arXiv2019

Minimum Throughput Maximization in LoRa Networks Powered by Ambient Energy Harvesting

In this paper, we investigate the uplink transmissions in low-power wide-area networks (LPWAN) where the users are self-powered by the energy harvested from the ambient environment. Demonstrating their potential in supporting diverse Internet-of-Things (IoT) applications, we focus on long range (LoRa) networks where the LoRa users are using the harvested energy to transmit data to a gateway via different spreading codes. Precisely, we study the throughput fairness optimization problem for LoRa users by jointly optimizing the spreading factor (SF) assignment, energy harvesting (EH) time duration, and the transmit power of LoRa users. First, through examination of the various permutations of collisions among users, we derive a general expression of the packet collision time between LoRa users, which depends on the SFs and EH duration requirements. Then, after reviewing prior SF allocation work, we develop two types of algorithms that either assure fair SF assignment indeed purposefully 'unfair' allocation schemes for the LoRa users. Our results unearth three new findings. Firstly, we demonstrate that, to maximize the minimum rate, the unfair SF allocation algorithm outperforms the other approaches. Secondly, considering the derived expression of packet collision between simultaneous users, we are now able to improve the performance of the minimum rate of LoRa users and show that it is protected from inter-SF interference which occurs between users with different SFs. That is, imperfect SF orthogonality has no impact on minimum rate performance. Finally, we have observed that co-SF interference is the main limitation in the throughput performance, and not the energy scarcity.

math.OC↗

arXiv2019

Optimal Sampling of Water Distribution Network Dynamics using Graph Fourier Transform

Water Distribution Networks (WDNs) are critical infrastructures that ensure safe drinking water. One of the major threats is the accidental or intentional injection of pollutants. Data collection remains challenging in underground WDNs and in order to quantify its threat to end users, modeling pollutant spread with minimal sensor data is can important open challenge. Existing approaches using numerical optimisation suffer from scalability issues and lack detailed insight and performance guarantees. Applying general data-driven approaches such as compressed sensing (CS) offer limited improvements in sample node reduction. Graph theoretic approaches link topology (e.g. Laplacian spectra) to optimal sensing locations, it neglects the complex dynamics. In this work, we introduce a novel Graph Fourier Transform (GFT) that exploits the low-rank property to optimally sample junction nodes in WDNs. The proposed GFT allows us to fully recover the full network dynamics using a subset of data sampled at the identified nodes. The proposed GFT technique offers attractive improvements over existing numerical optimisation, compressed sensing, and graph theoretic approaches. Our results show that, on average, with nearly 30-40\% of the junctions monitored, we are able to fully recover the dynamics of the whole network. The framework is useful beyond the application of WDNs and can be applied to a variety of infrastructure sensing for digital twin modeling.

cs.IT↗

arXiv2019

Making Sense of the World: Models for Reliable Sensor-Driven Systems

Sensor-driven systems are increasingly ubiquitous: they provide both data and information that can facilitate real-time decision-making and autonomous actuation, as well as enabling informed policy choices by service providers and regulators. But can we guarantee these system do what we expect, can their stake-holders ask deep questions and be confident of obtaining reliable answers? This is more than standard software engineering: uncertainty pervades not only sensors themselves, but the physical and digital environments in which these systems operate. While we cannot engineer this uncertainty away, through the use of models we can manage its impact in the design, development and deployment of sensor network software. Our contribution consists of two new concepts that improve the modelling process: frames of reference bringing together the different perspectives being modelled and their context; and the roles of different types of model in sensor-driven systems. In this position paper we develop these new concepts, illustrate their application to two example systems, and describe some of the new research challenges involved in modelling for assurance.

cs.SE↗

arXiv2017

BRPL: Backpressure RPL for High-throughput and Mobile IoTs

RPL, an IPv6 routing protocol for Low power Lossy Networks (LLNs), is considered to be the de facto routing standard for the Internet of Things (IoT). However, more and more experimental results demonstrate that RPL performs poorly when it comes to throughput and adaptability to network dynamics. This significantly limits the application of RPL in many practical IoT scenarios, such as an LLN with high-speed sensor data streams and mobile sensing devices. To address this issue, we develop BRPL, an extension of RPL, providing a practical approach that allows users to smoothly combine any RPL Object Function (OF) with backpressure routing. BRPL uses two novel algorithms, QuickTheta and QuickBeta, to support time-varying data traffic loads and node mobility respectively. We implement BRPL on Contiki OS, an open-source operating system for the Internet of Things. We conduct an extensive evaluation using both real-world experiments based on the FIT IoT-LAB testbed and large-scale simulations using Cooja over 18 virtual servers on the Cloud. The evaluation results demonstrate that BRPL not only is fully backward compatible with RPL (i.e. devices running RPL and BRPL can work together seamlessly), but also significantly improves network throughput and adaptability to changes in network topologies and data traffic loads. The observed packet loss reduction in mobile networks is, at a minimum, 60% and up to 1000% can be seen in extreme cases.

cs.NI↗

arXiv2017

Rapid, User-Transparent, and Trustworthy Device Pairing for D2D-Enabled Mobile Crowdsourcing

Mobile Crowdsourcing is a promising service paradigm utilizing ubiquitous mobile devices to facilitate largescale crowdsourcing tasks (e.g. urban sensing and collaborative computing). Many applications in this domain require Device-to-Device (D2D) communications between participating devices for interactive operations such as task collaborations and file transmissions. Considering the private participating devices and their opportunistic encountering behaviors, it is highly desired to establish secure and trustworthy D2D connections in a fast and autonomous way, which is vital for implementing practical Mobile Crowdsourcing Systems (MCSs). In this paper, we develop an efficient scheme, Trustworthy Device Pairing (TDP), which achieves user-transparent secure D2D connections and reliable peer device selections for trustworthy D2D communications. Through rigorous analysis, we demonstrate the effectiveness and security intensity of TDP in theory. The performance of TDP is evaluated based on both real-world prototype experiments and extensive trace-driven simulations. Evaluation results verify our theoretical analysis and show that TDP significantly outperforms existing approaches in terms of pairing speed, stability, and security.

cs.NI↗