This paper presents a vendor-agnostic architecture for secure pre-shared key (PSK) exchange between Quantum Key Distribution (QKD) nodes, leveraging post-quantum cryptography (PQC) tools. The proposed system combines PQC-OpenVPN and OQS-OpenSSH with USB mass storage emulation and single-board computers (SBCs) to automate the transfer of initial authentication secrets. This design significantly reduces manual intervention and mitigates risks associated with physical key handling. The solution was experimentally validated on IDQ Clavis3 and Cerberis3 devices and is broadly applicable to other QKD platforms that support only USB-based key input. Integration of lattice-based algorithms such as Kyber, Dilithium, and ML-DSA enables encapsulation and authentication of quantum-safe keys. Furthermore, a layered design using VPN and SSH channels provides robust cryptographic isolation for authentication material in transit. The work contributes a reproducible and cost-effective testbed for post-quantum hardened QKD deployments and demonstrates the practical feasibility of combining PQC mechanisms with QKD systems to enhance trust in future quantum-safe infrastructures.

Mobility prediction has been a popular research topic for many decades. With the advent of new generation technologies (5G and beyond) and smaller coverage cells, hand-over operations have become more frequent. Cellular system companies are therefore taking increasing interest in using the available predictive information on node movements to optimize and manage their bandwidth resources. In particular, the main challenging scope of our contribution consists in solving the issue of reliable next-cell prediction, aimed to call dropping probability minimization. In addition, our proposal is based on the innovative concept of mobility data to image encoding. The scheme is able to a-priori determine the next visited cells during host movements by applying a convolutional neural approach to mobility images. The power of machine learning is used to advantage, and highly accurate image classification is achieved for mobility prediction. We performed numerous simulation campaigns related to next-cell prediction in mobile cellular environments, obtaining very satisfactory results by the application of convolutional neural networks, which have an impressive history of effectiveness with image classification problems. The trained network has been associated to each coverage cell and the prediction accuracy has been evaluated.

With the low integration costs and quick development cycle of all-IP-based 5G+ technologies, it is not surprising that the proliferation of IP devices for residential or industrial purposes is ubiquitous. Energy scheduling/management and automated device recognition are popular research areas in the engineering community, and much time and work have been invested in producing the systems required for smart city networks. However, most proposed approaches involve expensive and invasive equipment that produces huge volumes of data (high-frequency complexity) for analysis by supervised learning algorithms. In contrast to other studies in the literature, we propose an approach based on encoding consumption data into vehicular mobility and imaging systems to apply a simple convolutional neural network to recognize certain scenarios (devices powered on) in real-time and based on the Non-Intrusive Load Monitoring (NILM) paradigm. Our idea is based on a very cheap device and can be adapted at a very low cost for any real scenario. We have also created our own dataset, taken from a real domestic environment, contrary to most existing works based on synthetic data. The results of the study’s simulation demonstrate the effectiveness of this innovative and low-cost approach and its scalability in function of the number of considered appliances.

In the last decade, the investigation of mobility features has gained enormous significance in many scenarios as a result of the significant diffusion and deployment of mobile devices covered by high-speed technologies (e.g., 5G). Many contributions in the literature have attempted to discover mobility properties, but most studies are based on the time features of the mobility process. No study has yet considered the effects of setting a proper sampling frequency (generally set to 1 s), in order to avoid information loss. Following our previous works, we propose a novel predictive spectral approach for mobility sampling based on the concept of a predictive wavelet. With this method, the choice of sampling frequency is governed by the current spectral components of the mobility process and derived from an analysis of future, predicted components. To assess whether our proposal may yield a helpful method, we conducted several simulation campaigns to test sampling accuracy and obtained results that confirmed our expectations.

Quantum cryptography can provide a very high level of data security. However, a big challenge of this technique is errors in quantum channels. Therefore, error correction methods must be applied in real implementations. An example is error correction based on artificial neural networks. This paper considers the practical aspects of this recently proposed method and analyzes elements which influence security and efficiency. The synchronization process based on mutual learning processes is analyzed in detail. The results allowed us to determine the impact of various parameters. Additionally, the paper describes the recommended number of iterations for different structures of artificial neural networks and various error rates. All this aims to support users in choosing a suitable configuration of neural networks used to correct errors in a secure and efficient way.

Free Space Optics (FSO) represent a promising technology for secure communications in several types of architectures: from Quantum Key Distribution Networks (QKDNs) to satellite communications. In this paper, in particular, we take into account terrestrial point-to-point laser communications and evaluate the performance in terms of Signal-to-Noise Ratio (SNR) and Bit Error Rate (BER), taking into account different scenarios, that can reflect real situations in which long distances can be reached in a secure way, guaranteeing an acceptable level of BER. So, after a huge campaign of simulations, we would like to let the scientific community know which are the theoretical limits that such kind of communications can reach. We take into account standard telescopes parameters (available today in the market), while configuring several real situations, in function of, for example, bit-rate, visibility, link distance, etc. A brief survey of the existing works is given, then a clearer performance evaluation of terrestrial FSO links is proposed.

Nowadays, mobile computing has become a key component of telecommunication systems, and the Open Systems Interconnection (OSI) layer operations are affected by the effects of node movements along the roads, from the physical to the routing/transport layers. In particular, routing approaches have been investigated from many years, trying to optimize the performance of the whole considered system, under different points of view. In this paper we are focusing the attention on the analysis of the mobility grade trend for a mobile ad-hoc network environment, as well as on the way it can be a-priori known, in order to have the possibility to study how the dynamics of mobile nodes can be described and in-advance known, with a predicted knowledge of nodes stability (in terms of mobility). Our simulations considered mobility in real geographical maps, and the obtained results confirmed the goodness of our proposed study.

Mobility is a key aspect of modern networking systems. To determine how to better manage the available resources, many architectures aim toa prioriknow the future positions of mobile nodes. This can be determined, for example, from mobile sensors in a smart city environment or wearable devices carried by pedestrians. If we consider infrastructure networks, frequently changing the coverage cell may lead to service disruptions if a predictive approach is not deployed in the system. All predictive systems are based on the storage of old mobility samples to adequately train the model. Our focus is based on the possibility to determine an approach for adaptively sampling mobility patterns based on the intrinsic features of the human/node behavior. Several works in the literature examine mobility prediction mobile networks, but all of them are dedicated to the study of time features in mobility traces: none took into account the spectral content of historical mobility patterns for predictive purposes. In contrast, we take into account this spectral content in mobility samples. Through a set of wavelet transforms, we adapted the sampling frequency dynamically and obtained a considerable set of advantages (space, energy, accuracy, etc.). In fact, this issue covers an important role in the IoT paradigm, where energy consumption is one of the main variables requiring optimization (frequent and unnecessary mobility samplings can disrupt battery life). We performed several simulations using real-world traces to confirm the merit of our proposal.

In the modern telecommunication systems, mobility is one of the key advantage of wireless communications, given that it is possible to transmit/receive data, without caring of having a static position into the network. Of course, mobility poses special issues such as degradations, channel quality fluctuations, fast topology changes, and so on. Modern researches focus their attention on predicting mobile future node positions, in order to a-priori know, for example, what the evolution of the network topology will be or which level of stability each node will reach. Each prediction scheme is based on the storage and analysis of several historical mobility trajectories, in order to train the proper prediction algorithm. In this paper, we focus our attention on the optimization of the space needed to store historical mobility samples, encoding their values and evaluating the conversion error, comparing different encoding functions. Several simulation campaigns have been carried out in order to evaluate the goodness and feasibility of our proposal.

With the proliferation of connected vehicles, new coverage technologies and colossal bandwidth availability, the quality of service and experience in mobile computing play an important role for user satisfaction (in terms of comfort, security and overall performance). Unfortunately, in mobile environments, signal degradations very often affect the perceived service quality, and predictive approaches become necessary or helpful, to handle, for example, future node locations, future network topology or future system performance. In this paper, our attention is focused on an in-depth stochastic micro-mobility analysis in terms of nodes coordinates. Many existing works focused on different approaches for realizing accurate mobility predictions. Still, none of them analyzed the way mobility should be collected and/or observed, how the granularity of mobility samples collection should be set and/or how to interpret the collected samples to derive some stochastic properties based on the mobility type (pedestrian, vehicular, etc.). The main work has been carried out by observing the characteristics of vehicular mobility, from real traces. At the same time, other environments have also been considered to compare the changes in the collected statistics. Several analyses and simulation campaigns have been carried out and proposed, verifying the effectiveness of the introduced concepts.

In recent years, noticeable progress has been made in the development of quantum equipment, reflected through the number of successful demonstrations of Quantum Key Distribution (QKD) technology. Although they showcase the great achievements of QKD, many practical difficulties still need to be resolved. Inspired by the significant similarity between mobile ad-hoc networks and QKD technology, we propose a novel quality of service (QoS) model including new metrics for determining the states of public and quantum channels as well as a comprehensive metric of the QKD link. We also propose a novel routing protocol to achieve high-level scalability and minimize consumption of cryptographic keys. Given the limited mobility of nodes in QKD networks, our routing protocol uses the geographical distance and calculated link states to determine the optimal route. It also benefits from a caching mechanism and detection of returning loops to provide effective forwarding while minimizing key consumption and achieving the desired utilization of network links. Simulation results are presented to demonstrate the validity and accuracy of the proposed solutions.