The increasing complexity and scale of modern network environments, particularly with the advent of the Internet of Things (IoT), demand flexible and adaptable routing protocols. Our study delves into the first known ns-3 implementation of Ad Hoc On-Demand Distance Vector version 2 (AODVv2), offering researchers a valuable platform to explore its capabilities. This implementation facilitates rigorous testing and comparative analysis under diverse network conditions, leading to new insights on the protocol’s performance and reliability. Key findings from our preliminary evaluations indicate that AODVv2’s overhead is higher than that of legacy AODV, but its route maintenance is more robust in dynamic topologies. These results accelerate innovation in mobile ad hoc networking and IoT domains, encouraging the development of more adaptive and resilient networks. Furthermore, our work offers empirical results to inform future standardization efforts for AODVv2.

With the growing integration of renewable energy sources into distributed grids, accurate household - level load forecasting becomes essential for robust energy management and optimization. This paper proposes a lightweight stochastic profile generation method grounded in conditional probability approximation. First, empirical conditional distributions are mined from historical load data via hourly histogram binning and correlation analysis. Second, a Monte Carlo - inspired “flock” of plausible future load trajectories is generated iteratively, each endowed with an occurrence probability. Validation on the Ausgrid dataset (127 prosumer profiles over one year) shows that the probabilistic mining from an historical dataset of 30-180 days requires only 0.5-0.6 s, while generating 200 potential future scenarios takes 8.1 ms, with a total memory footprint of approximately 200 KB. These computational and storage efficiencies made the approach suitable for online deployment on edge devices, enabling robust optimization under uncertainty in renewable energy management.

High-Altitude Platform Stations (HAPS) are gaining recognition as effective communication platforms due to their rapid deployment capabilities and cost efficiency. Unlike terrestrial infrastructures, HAPS provide enhanced flexibility and scalability, making them well-suited for the development of Non-Terrestrial Quantum Wide Area Networks (NTQWANs). These networks overcome the inherent limitations of Optical Fibers (OFs), such as significant photon losses over long distances, which hinder efficient quantum communication. HAPS offer a distinct advantage over terrestrial networks by enabling wide-area coverage without the need for extensive ground infrastructure. Despite their benefits, the performance of Free Space Optic (FSO) links can be affected by atmospheric disturbances and positional variations in HAPS. To address these challenges, this paper explores an architecture for Wide Area Quantum HAPS Networks (WAQHNs) that supports applications such as Distributed Quantum Computing (DQC) and Quantum Key Distribution (QKD). Performance evaluation focuses on specific key metrics that are optical attenuation, fidelity, and entanglement rate, demonstrating that HAPS-based networks can outperform traditional OF-based systems.

The Quantum Internet (QI) necessitates a complete revision of the classical protocol stack and the technologies used, whereas its operating principles depend on the physical laws governing quantum mechanics. Recent experiments demonstrate that Optical Fibers (OFs) allow connections only in urban areas. Therefore, a novel Quantum Satellite Backbone (QSB) composed of a considerable number of Quantum Satellite Repeaters (QSRs) deployed in Low Earth Orbit (LEO) would allow for the overcoming of typical OFs’ attenuation problems. Nevertheless, the dynamic nature of the scenario represents a challenge for novel satellite networks, making their design and management complicated. Therefore, we have designed an ad hoc QSB considering the interaction between Digital Twin (DT) and Software-Defined Networking (SDN). In addition to defining the system architecture, we present a DT monitoring protocol that allows efficient status recovery for the creation of multiple End-to-End (E2E) entanglement states. Moreover, we have evaluated the system performance by assessing the path monitoring and configuration time, the time required to establish the E2E entanglement, and the fidelity between a couple of Ground Stations (GSs) interconnected through the QSB, also conducting a deep analysis of the created temporal paths.

Current literature has identified several issues with centralized mobility management in 5G-based Intelligent Transportation Systems (ITS), where 5G-enabled vehicles are expected to exhibit varying mobility patterns. So, distributed mobility management (DMM), where control plane mobility anchors work in a distributed fashion, is of keen interest to current researchers. Although existing DMM solutions, such as distributed mobile IPv6 (DMIPv6), distributed proxy MIPv6 (DPMIPv6), distributed mobility management software defined networking (SDN-DMM), distributed routing (D-Routing), and their derivatives appear to work fine in evolving 5G-based ITS, they cannot be applied directly. Further, to gauge their adaptability to different ITS scenarios, a thorough investigation of their performances is very much needed. Our key contributions in this paper include investigating DMM issues in 5G-based ITS scenarios, porting DMM schemes in 5G ITS, and developing a Markov model to evaluate handoff performance. The results show that DMIPv6 and DPMIPv6 outperform SDN-DMM and D-Routing in terms of end-to-end delay and signaling cost, with the latter increasing delay by 6-23% and signaling cost by 135% and 78%, respectively. However, SDN-DMM and D-Routing excel in packet loss ratio (15-20% lower), throughput (20-25% higher), and packet delivery cost (40% lower). In edge cloud flow mobility, SDN-DMM and D-Routing show better packet loss ratio as packet loss is caused by servers’ sensitive application delays rather than handoffs.

As the Internet of Things (IoT) rapidly expands, there is a growing need for advanced routing solutions that accommodate constrained devices and diverse network requirements. Traditional single-metric routing approaches typically focused on parameters such as delay, hop count, or Maximum Transmission Unit (MTU), lack the adaptability required by IoT environments where energy efficiency, bandwidth considerations, and security constraints must be balanced. A more flexible protocol could improve network performance and prolong device lifetimes. To this aim, building on the extensibility of AODVv2, we developed and integrated a multi-metric routing protocol into the ns-3 simulator. Our implementation allows for the prioritization of multiple factors, such as residual battery levels, node reliability, and hop count, through a customizable metric ordering. Using Type-Length-Value (TLV) encoding, the protocol seamlessly incorporates additional metrics without compromising existing functionalities. Experimental results demonstrate that our multi-metric AODVv2 outperforms the original AODVv2 in terms of network performance, reduces energy consumption, and enhances the overall network longevity, thus providing significant benefits for IoT network management. Our released code paves the way for the programmability of other routing metric systems, including battery discharge rates, and the development of dynamic trust adjustment mechanisms for robust and adaptive routing.

Next-generation computing platforms are increasingly expected to accommodate a wide range of immersive applications, integrating advanced technologies such as extended reality and ultra-realistic virtual reality (uVR). The design and optimization of these distributed systems present significant challenges, particularly when managing the coexistence of traffic flows generated by both human and machine sources. This work introduces an end-to-end delay analysis framework focused on human-driven traffic in environments characterized by mixedsource flows. The human-generated streams-central to the analysis-coexist with machine-type communications that differ in terms of latency requirements. The delay analysis is carried out by deriving per-flow stochastic bounds, expressed in terms of the probability of receiving timely service. This is achieved through the use of stochastic network calculus and martingalebased methods, with human perceptual constraints explicitly incorporated into the theoretical model. Simulation-based validation confirms that the proposed bound accurately reflects actual end-to-end behavior. Moreover, the results demonstrate that integrating human perception into the analysis leads to improved performance compared to conventional approaches that do not account for cognitive aspects.

In infrastructure-deficient environments-such as tunnels, remote military zones, and mountainous emergency sites-wireless connectivity is often disrupted by blocked Line-ofSight (LoS) and sparse coverage. This paper proposes a unified downlink framework, combining UAV-mounted base station with passive reconfigurable intelligent surfaces (RIS), and powerdomain NOMA, to restore communication in such scenarios. We formulate a mixed-integer problem to jointly optimize UAV placement, RIS phase shifts, analog beamforming, physical resource block (PRB) allocation, and power control, balancing throughput and energy efficiency. Our proposed solution algorithm comprises two phases: (1) joint configuration of beamforming, RIS tuning, PRB scheduling and UAV location using convex approximations; and (2) power tuning via geometric programming. Under realistic channel conditions, our method achieves up to 25% higher throughput and 18% lower power consumption over an orthogonal-access baseline, and around 6% higher throughput and 7% lower power over a zero-forcing benchmark with particle swarm optimization.

The Internet of Things is emerging as a key concept, defining a network of interconnected devices capable of seamless data collection, exchange, and analysis. However, due to their emphasis on simplicity, these devices are often vulnerable to malware attacks. This study examines the potential of machine learning methods, specifically in the context of Federated Learning, to enhance privacy protection and to benefit from IoT’s decentralized nature, such as the low overhead traffic. The proposed approach is a federated machine learning algorithm based on a central aggregator and several clients. The study aims to conduct a comprehensive analysis using the IOT-23 dataset, which contains real and labeled instances of malware infections. The test outcomes demonstrate that the proposed approach outperforms centralized approaches regarding the global area under the precision-recall curve (AUPRC) and variance, with a significance level of 0.05.

As the complexity, distribution, and heterogeneity of networks continue to grow, how to architect and monitor of these networking environments is becoming an increasingly critical open issue. Digital twins, which can replicate the structure and behavior of a physical network, are seen as potential solution to address the problem. While reference architectures for digital twins exist in other fields, a comprehensive reference architecture for the networking context has yet to be developed. This paper discusses the need for such a reference architecture and outlines the key elements necessary for its design. We present the findings of a preliminary survey that explores the need for a network digital twin reference architecture, the crucial information it should include, and practical insights into its design. The survey results confirm that existing standards are inadequate for modeling network digital twins, outlining the necessity of a new reference architecture. We then articulate our position on the need for a reference architecture for network digital twins, focusing on three main aspects, namely: (i) digital twins of what, (ii) for what, and (iii) how to deploy them. We then proceed to delineate the fundamental obstacles that a reference architecture must confront, in tandem with the essential characteristics it needs to embody to successfully navigate these challenges. As conclusion, we present our vision for the reference architecture and outline the main research steps we plan to take to address this open problem. Our ultimate goal is to tightly collaborate both with the networking and digital twin software architecture communities to jointly establish a sound network digital twin architecture of the future.

Machine learning (ML) has emerged as a compelling approach to identify attacks in network traffic security. Existing malware detection strategies often concentrate on specific facets, such as efficient data collection, particular types of malware, or handling data scarcity. While valid, these strategies typically overlook the potential for minimizing sample size, focusing instead on data augmentation. This work introduces a novel method to determine the minimum sample size necessary to achieve a specified accuracy level, measured by the F1 score derived from the confusion matrix. We focus on TCP header traffic data transformed into images through slow-splitting techniques for multi-class traffic classification. In addition, we introduce a diffusion model to generate new synthetic traffic images and show that our method outperforms existing techniques in terms of stability and predictability. This study also compares the effectiveness of synthetic image augmentation using Generative Adversarial Networks (LANs) and Denoising Diffusion Probabilistic Models (DDPM) in improving image recognition and classification accuracy.

The Internet of Things (IoT) paradigm is assumed to be a major component in the present and future Internet, with forecasts claiming a humongous number of devices connected in the near future, and applications fields spanning from agriculture, to healthcare. Despite this, the standardization efforts have not yet resulted in widely adopted standards, and the market is fragmented into multiple solutions both at physical and communication protocol levels. Moreover, IoT systems exacerbate the usual test bed limitations, e.g., scalability (very large number of devices), hardware compatibility, space, and price. Due to the above problems, simulation tools become an extremely interesting tool for studying IoT systems both for academia (new algorithms), standardization (new protocols), and industry (what-if analysis). In this paper we will discuss what are the most relevant features and models that a simulation tool like ns-3 should prioritize to enable the above-mentioned needs from academia, standardization, and industry, and if they are achievable in the short, medium, or long term.

AbstractThe detection and characterisation of electromagnetic signals within a specific frequency range, known as spectrum sensing, plays a crucial role in Cognitive Radio Networks (CRNs). The CRNs aim to adapt their communication parameters to the surrounding radio environment, thereby improving the efficiency and utilisation of the available radio spectrum. Spectrum sensing is particularly important in device‐to‐device (D2D) communication when operating independently of the cellular network infrastructure. The Medium Access Control (MAC) protocol coordinates device communication and ensures interference‐free operation of the CRN coexisting with the primary cellular network. A spectrum sensing strategy at the MAC layer for cognitive D2D communication. The strategy focuses on reducing the overall sensing period allocated at the MAC layer by having each Cognitive D2D User (cD2DU) sense a smaller subset of available channels while maintaining the same sensing time for cellular user detection at the physical layer. To achieve this, the concept of concurrent groups of D2D devices is introduced in proximity, which are formed by using unique IDs of cD2DUs during the device discovery stage. Each concurrent group senses a specific portion of the cellular user band in a shorter time, resulting in a reduced overall sensing period. In addition to mitigating traffic congestion through data diversion from the cellular network, the proposed strategy facilitates the concurrent sensing of multiple channels by cD2DUs within the underutilised cellular user band. This leads to extended data transmission periods, increased network throughput, and effective offloading of the cellular network. The effectiveness of the proposed work is evaluated by considering factors, such as network throughput and transmission time. Simulation results confirm the effectiveness of the approach in improving spectrum utilisation and communication efficiency in multi‐channel Cognitive D2D Networks (cD2DNs).

The 5G standard is aimed at supporting Quality of Service (QoS)-constrained traffic types, enabling new services to be reliably built into scenarios such as industrial automation and smart cities. The support comes via a strong emphasis on resource virtualization in the form of slices. Due to the strong QoS constraints of each slice, determining how to actually split the radio resources among different slices, while considering simultaneously the priority of slices, network efficiency, and each slice's target QoS, is very challenging. In this paper, we propose a radio resource scheduling scheme, designed on the basis of a strong theoretical analysis, to address the challenges. We formulate a Chance-constrained optimum resource allocation problem, which is then converted into a low complexity deterministic knapsack problem utilizing the concept of effective bandwidth. The performance analysis proves that our proposal is better in efficiency than the existing schemes, under different network conditions and QoS constraints. Results clearly show the effectiveness of our scheme in the considered 5G scenarios.

The adoption of Extended Reality (XR) technology has been hindered by the need for high-bandwidth and low-latency networks to provide immersive experiences. Head-mounted devices used in XR are still heavy and not portable, limiting the potential of XR applications in various contexts. In this paper, we propose EdgeVerse, a multi-user XR system that leverages edge computing and extended Berkeley Packet Filter (eBPF) to offload computation, thereby reducing the dependency on high-bandwidth networks in support of XR applications. Our approach enables lightweight clients, such as devices with an edge browser, making XR more accessible to users. The key design of EdgeVerse focuses on offloading the connection and network synchronization of multiple XR users at the edge. We used an XDP bidirectional router that processes XR traffic faster to enhance user interaction, responsiveness, and immersive experience. To establish the practicality of our approach, we evaluate our results on a prototype that indicates improved response times and reduced latency with respect to baseline solutions.

Low-Power and Lossy Networks (LLNs) are based on constrained devices. Energy conservation is one of the main constraints, and the traditional IPv6 Neighbor Discovery Protocol (IPv6-NDP) was neither designed nor suitable to cope with it. This inefficiency arises from non-transitive wireless links and heavy multicast transmission, sometimes rendering it impractical in LLNs. Substantial work has been done by the Internet Engineering Task Force (IETF) to optimize the IPv6-ND protocol, known as IPv6 over Low power Wireless Personal Area Network - Neighbor Discovery Protocol (6LoWPAN-NDP). Despite these improvements, full implementation is yet to be achieved in commercial, open-source, or proprietary sectors. In this article, we debate both Neighbor Discovery Protocols (NDPs), examining various aspects. We implemented 6LoWPAN-NDP in a well-known ns3 simulator. We discuss the complexity of 6LoWPAN-NDP and see why open-source, commercial, or proprietary sectors have not widely adopted it. We present how both protocols function optimally in meshunder and non-meshunder scenarios. We present results and analysis of both NDPs control messages’ behavior. At the same time, data traffic is turned on and off, and we demonstrate the operational behavior of Link-local Unicast Address (LUA) and Global Unicast Address (GUA) in meshunder and non-meshunder scenarios. The presented implementation can be helpful in enabling large-scale simulations and evaluating scenario-specific protocol parameters, along with protocol extensions.

Active magnetic bearings are complex mechatronic systems that consist of mechanical, electrical, and software parts, unlike classical rolling bearings. Given the complexity of this type of system, fault detection is a critical process. This paper presents a new and easy way to detect faults based on the use of a fault dictionary and machine learning. The dictionary was built starting from fault signatures consisting of images obtained from the signals available in the system. Subsequently, a convolutional neural network was trained to recognize such fault signature images. The objective of this study was to develop a fault dictionary and a classifier to recognize the most frequent soft electrical faults that affect position sensors and actuators. The proposed method permits, in a computationally convenient way that can be implemented in real time, the determination of which component has failed and what kind of failure has occurred. Therefore, this fault identification system allows determining which countermeasure to adopt in order to enhance the reliability of the system. The performance of this method was assessed by means of a case study concerning a real turbomachine supported by two active magnetic bearings for the oil and gas field. Seventeen fault classes were considered, and the neural network fault classifier reached an accuracy of 93% on the test dataset.

The European Commission published in 2015 “Energy Union Strategy” to make energy production safer, more sustainable, competitive, and economical. To allow citizens of a community to cooperate to consume and exchange renewable energy they produce, the use of blockchain technology seems natural. In this note, we will start from a concrete case study to see what the technological challenges (and not only) are to be overcome. Furthermore, in the last sections, it will be shown how the creation of the IT infrastructure necessary for the exchange of energy produced can also be used for other purposes. That is according to the smart city paradigm to create a community able to make the most of the touristic, environmental, and social resources of the territory in which it lives.

In accordance with the Internet of Everything (IoE) paradigm, millions of people and billions of devices are expected to be connected to each other, giving rise to an ever increasing demand for application services with a strict quality of service requirements. Therefore, service providers are dealing with the functional integration of the classical cloud computing architecture with edge computing networks. However, the intrinsic limited capacity of the edge computing nodes implies the need for proper virtual functions' allocations to improve user satisfaction and service fulfillment. In this sense, demand prediction is crucial in services management and exploitation. The main challenge here consists of the high variability of application requests that result in inaccurate forecasts. Federated learning has recently emerged as a solution to train mathematical learning models on the users' site. This paper investigates the application of federated learning to virtual functions demand prediction in IoE based edge cloud computing systems, to preserve the data security and maximise service provider revenue. Additionally, the paper proposes a virtual function placement based on the services demand prediction provided by the federated learning module. A matching based tasks allocation is proposed. Finally, numerical results validate the proposed approach, compared with a chaos theory prediction scheme.

This paper analyzes the end-to-end delay performance in an edge-computing scenario where a set of Internet of Things devices (IoTDs) access the computation facilities of an Edge Node by means of a 5G based network. In particular, the paper deals with a two power levels slotted Aloha non-orthogonal-multiple-access (NOMA) scheme and formulates a stochastic end-to-end delay bound, in terms of complementary cumulative probability distribution, by resorting to the application of the martingale theory. In order to validate the proposed analysis, the paper proposes comparisons between the achieved analytical predictions and actual values derived by resorting to extensive computer simulations. Furthermore, the well known Boole bound has been formulated and compared with the proposed Martingale approach to highlight the better behavior of the proposed solution.