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Internet of Things (IoT) applications are composed of massive quantities of resource-limited devices that collect sensitive data with long-term operational and security requirements. With the threat of emerging quantum computers, Post-Quantum Cryptography (PQC) is a critical requirement for IoTs. In particular, digital signatures offer scalable authentication with non-repudiation and are an essential tool for IoTs. However, as seen in NIST PQC standardization, post-quantum signatures are extremely costly for resource-limited IoTs. Hence, there is a significant need for quantum-safe signatures that respect the processing, memory, and bandwidth limitations of IoTs. In this paper, we created a new lightweight quantum-safe digital signature referred to as INFinity-HORS (INF-HORS), which is (to the best of our knowledge) the first signer-optimal hash-based signature with (polynomially) unbounded signing capability. INF-HORS enables a verifier to non-interactively construct one-time public keys from a master public key via encrypted function evaluations. This strategy avoids the performance bottleneck of hash-based standards (e.g., SPHINCS+) by eliminating hyper-tree structures. It also does not require a trusted party or non-colliding servers to distribute public keys. Our performance analysis confirms that INF-HORS is magnitudes of times more signer computation efficient than selected NIST PQC schemes (e.g., SPHINCS+, Dilithium, Falcon) with a small memory footprint.

When deploying machine learning (ML) applications, the automated allocation of computing resources-commonly referred to as autoscaling-is crucial for maintaining a consistent inference time under fluctuating workloads. The objective is to maximize the Quality of Service metrics, emphasizing performance and availability, while minimizing resource costs. In this paper, we compare scalable deployment techniques across three levels of scaling: at the application level (TorchServe, RayServe) and the container level (K3s) in a local environment (production server), as well as at the container and machine levels in a cloud environment (Amazon Web Services Elastic Container Service and Elastic Kubernetes Service). The comparison is conducted through the study of mean and standard deviation of inference time in a multi-client scenario, along with upscaling response times. Based on this analysis, we propose a deployment strategy for both local and cloud-based environments.

As broadband Internet speeds continue to increase, the home wireless ("WiFi") network may more frequently become a performance bottleneck. Past research, now nearly a decade old, initially documented this phenomenon through indirect inference techniques, noting the prevalence of WiFi bottlenecks but never directly measuring them. In the intervening years, access network (and WiFi) speeds have increased, warranting a re-appraisal of this important question, particularly with renewed private and federal investment in access network infrastructure. This paper studies this question, developing a new system and measurement technique to perform direct measurements of WiFi and access network performance, ultimately collecting and analyzing a first-of-its-kind dataset of more than 13,000 joint measurements of WiFi and access network throughputs, in a real-world deployment spanning more than 50 homes, for nearly two years. Using this dataset, we re-examine the question of whether, when, and to what extent a user's home wireless network may be a performance bottleneck, particularly relative to their access connection. We do so by directly and continuously measuring the user's Internet performance along two separate components of the Internet path -- from a wireless client inside the home network to the wired point of access (e.g., the cable modem), and from the wired point of access to the user's ISP. Confirming and revising results from more than a decade ago, we find that a user's home wireless network is often the throughput bottleneck. In particular, for users with access links that exceed 800~Mbps, the user's home wireless network was the performance bottleneck 100% of the time.

Although robust statistical estimators are less affected by outlying observations, their computation is usually more challenging. This is particularly the case in high-dimensional sparse settings. The availability of new optimization procedures, mainly developed in the computer science domain, offers new possibilities for the field of robust statistics. This paper investigates how such procedures can be used for robust sparse association estimators. The problem can be split into a robust estimation step followed by an optimization for the remaining decoupled, (bi-)convex problem. A combination of the augmented Lagrangian algorithm and adaptive gradient descent is implemented to also include suitable constraints for inducing sparsity. We provide results concerning the precision of the algorithm and show the advantages over existing algorithms in this context. High-dimensional empirical examples underline the usefulness of this procedure. Extensions to other robust sparse estimators are possible.

We present a novel hybrid (model- and learning-based) architecture for fusing the most significant features from conventional aerial images and integral aerial images that result from synthetic aperture sensing for removing occlusion caused by dense vegetation. It combines the environment's spatial references with features of unoccluded targets. Our method out-beats the state-of-the-art, does not require manually tuned parameters, can be extended to an arbitrary number and combinations of spectral channels, and is reconfigurable to address different use-cases.

We propose a robust transceiver design for a covert integrated sensing and communications (ISAC) system with imperfect channel state information (CSI). Considering both bounded and probabilistic CSI error models, we formulate worst-case and outage-constrained robust optimization problems of joint trasceiver beamforming and radar waveform design to balance the radar performance of multiple targets while ensuring communications performance and covertness of the system. The optimization problems are challenging due to the non-convexity arising from the semi-infinite constraints (SICs) and the coupled transceiver variables. In an effort to tackle the former difficulty, S-procedure and Bernstein-type inequality are introduced for converting the SICs into finite convex linear matrix inequalities (LMIs) and second-order cone constraints. A robust alternating optimization framework referred to alternating double-checking is developed for decoupling the transceiver design problem into feasibility-checking transmitter- and receiver-side subproblems, transforming the rank-one constraints into a set of LMIs, and verifying the feasibility of beamforming by invoking the matrix-lifting scheme. Numerical results are provided to demonstrate the effectiveness and robustness of the proposed algorithm in improving the performance of covert ISAC systems.

Low-resolution analog-to-digital converters (ADCs) in massive multi-user (MU) multiple-input multiple-output (MIMO) wireless systems can significantly reduce the power, cost, and interconnect data rates of infrastructure basestations. Thus, recent research on the theory and algorithm sides has extensively focused on such architectures, but with idealistic quantization models. However, real-world ADCs do not behave like ideal quantizers, and are affected by fabrication mismatches. We analyze the impact of capacitor-array mismatches in successive approximation register (SAR) ADCs, which are widely used in wireless systems. We use Bussgang's decomposition to model the effects of such mismatches, and we analyze their impact on the performance of a single ADC. We then simulate a massive MU-MIMO system to demonstrate that capacitor mismatches should not be ignored, even in basestations that use low-resolution SAR ADCs.

Computing Continuum (CC) systems are challenged to ensure the intricate requirements of each computational tier. Given the system's scale, the Service Level Objectives (SLOs) which are expressed as these requirements, must be broken down into smaller parts that can be decentralized. We present our framework for collaborative edge intelligence enabling individual edge devices to (1) develop a causal understanding of how to enforce their SLOs, and (2) transfer knowledge to speed up the onboarding of heterogeneous devices. Through collaboration, they (3) increase the scope of SLO fulfillment. We implemented the framework and evaluated a use case in which a CC system is responsible for ensuring Quality of Service (QoS) and Quality of Experience (QoE) during video streaming. Our results showed that edge devices required only ten training rounds to ensure four SLOs; furthermore, the underlying causal structures were also rationally explainable. The addition of new types of devices can be done a posteriori, the framework allowed them to reuse existing models, even though the device type had been unknown. Finally, rebalancing the load within a device cluster allowed individual edge devices to recover their SLO compliance after a network failure from 22% to 89%.

Federated learning effectively addresses issues such as data privacy by collaborating across participating devices to train global models. However, factors such as network topology and device computing power can affect its training or communication process in complex network environments. A new network architecture and paradigm with computing-measurable, perceptible, distributable, dispatchable, and manageable capabilities, computing and network convergence (CNC) of 6G networks can effectively support federated learning training and improve its communication efficiency. By guiding the participating devices' training in federated learning based on business requirements, resource load, network conditions, and arithmetic power of devices, CNC can reach this goal. In this paper, to improve the communication efficiency of federated learning in complex networks, we study the communication efficiency optimization of federated learning for computing and network convergence of 6G networks, methods that gives decisions on its training process for different network conditions and arithmetic power of participating devices in federated learning. The experiments address two architectures that exist for devices in federated learning and arrange devices to participate in training based on arithmetic power while achieving optimization of communication efficiency in the process of transferring model parameters. The results show that the method we proposed can (1) cope well with complex network situations (2) effectively balance the delay distribution of participating devices for local training (3) improve the communication efficiency during the transfer of model parameters (4) improve the resource utilization in the network.

Deep neural networks have revolutionized many machine learning tasks in power systems, ranging from pattern recognition to signal processing. The data in these tasks is typically represented in Euclidean domains. Nevertheless, there is an increasing number of applications in power systems, where data are collected from non-Euclidean domains and represented as the graph-structured data with high dimensional features and interdependency among nodes. The complexity of graph-structured data has brought significant challenges to the existing deep neural networks defined in Euclidean domains. Recently, many studies on extending deep neural networks for graph-structured data in power systems have emerged. In this paper, a comprehensive overview of graph neural networks (GNNs) in power systems is proposed. Specifically, several classical paradigms of GNNs structures (e.g., graph convolutional networks, graph recurrent neural networks, graph attention networks, graph generative networks, spatial-temporal graph convolutional networks, and hybrid forms of GNNs) are summarized, and key applications in power systems such as fault diagnosis, power prediction, power flow calculation, and data generation are reviewed in detail. Furthermore, main issues and some research trends about the applications of GNNs in power systems are discussed.