Effective resource management and network slicing are essential to meet the diverse service demands of vehicular networks, including Enhanced Mobile Broadband (eMBB) and Ultra-Reliable and Low-Latency Communications (URLLC). This paper introduces an Explainable Deep Reinforcement Learning (XRL) framework for dynamic network slicing and resource allocation in vehicular networks, built upon a near-real-time RAN intelligent controller. By integrating a feature-based approach that leverages Shapley values and an attention mechanism, we interpret and refine the decisions of our reinforcement learning agents, addressing key reliability challenges in vehicular communication systems. Simulation results demonstrate that our approach provides clear, real-time insights into the resource allocation process and achieves higher interpretability precision than a pure attention mechanism. Furthermore, the Quality of Service (QoS) satisfaction for URLLC services increased from 78.0% to 80.13%, while that for eMBB services improved from 71.44% to 73.21%.

We present a comparative study of the Gaussian mixture model (GMM) and the Deep Autoencoder Gaussian Mixture Model (DAGMM) for estimating satellite quantum channel capacity, considering hybrid quantum noise (HQN) and transmission constraints. While GMM is simple and interpretable, DAGMM better captures non-linear variations and noise distributions. Simulations show that DAGMM provides tighter capacity bounds and improved clustering. This introduces the Deep Cluster Gaussian Mixture Model (DCGMM) for high-dimensional quantum data analysis in quantum satellite communication.

We introduce a hybrid Quantum Neural Networks (QNN) architecture for the efficient user scheduling in 5G/Beyond 5G (B5G) massive Multiple Input Multiple Output (MIMO) systems, addressing the scalability issues of traditional methods. By leveraging statistical Channel State Information (CSI), our model reduces computational overhead and enhances spectral efficiency. It integrates classical neural networks with a variational quantum circuit kernel, outperforming classical Convolutional Neural Networks (CNN) and maintaining robust performance in noisy channels. This demonstrates the potential of quantum-enhanced Machine Learning (ML) for wireless scheduling.

Quantum entanglement enables quantum networks to provide end-to-end sharing of entangled particles, establishing multi-hop path-to-path connections between remote parties. Implementing entanglement distribution plays a vital role in increasing the network scale, and practical entanglement algorithms are required to provide end-to-end multi-hop quantum entanglement. We consider the real-time entanglement distribution (R-TED) and pre-established entanglement distribution (P-EED) to meet this requirement. Based on these two types of entanglement distribution, we propose two algorithms, i.e., R-TED-based routing and entangled pairs allocation (REA) algorithm as well as P-EED-based REA algorithm for end-to-end entanglement establishment, where the practical physical factors (e.g., finite storage capacity and limited storage time) are considered. The R-TED-based REA algorithm can orchestrate the nodes in a route and perform entanglement swapping by adopting real-time entanglement. For the P-EED-based REA algorithm, remote entangled particle sharing can be achieved via pre-shared entanglement distribution and hop-by-hop entanglement swapping. This way, the entanglement routing selection satisfies the storage time constraint and allows two far-apart nodes to share long-distance entangled particles with limited memory cells. We evaluate the performance of the proposed algorithms under different network topologies and sizes, based on which we demonstrate that the network size can significantly affect the efficiency advantage achieved by the P-EED-based approach over the R-TED-based approach.

With the continual growth in user communication needs, classical optical communications are facing developmental bottlenecks. On one hand, the communication capacity of available optical fiber resources is approaching its upper limit. On the other hand, emerging quantum computing technology poses security threats. Quantum key distribution (QKD), as a representative quantum cryptography technology, is being introduced into existing optical infrastructure to mitigate security threats. It also has pioneering application value for next-generation quantum information networks. However, limited optical fiber resources struggle to support the introduction of quantum communication over classical optical networks. There are also incompatible noise factors between the two communication paradigms. This paper proposes transitioning from unilateral adaptive routing and wavelength assignment (ARW A) to bilateral synergistic RW A (SRW A) to facilitate the coexistence of two heterogeneous communication paradigms in optical networks. Simulations have proven SRW A can increase end-to-end key supply rates from bit/s to kbit/s levels. It has an enabling effect on QKD over classical optical networks.

Wearable Internet of Things (IoT) devices are gaining ground for continuous physiological data acquisition and health monitoring. These physiological signals can be used for security applications to achieve continuous authentication and user convenience due to passive data acquisition. This paper investigates an electrocardiogram (ECG) based biometric user authentication system using features derived from the Convolutional Neural Network (CNN) and self-supervised contrastive learning. Contrastive learning enables us to use large unlabeled datasets to train the model and establish its generalizability. We propose approaches enabling the CNN encoder to extract appropriate features that distinguish the user from other subjects. When evaluated using the PTB ECG database with 290 subjects, the proposed technique achieved an authentication accuracy of 99.15%. To test its generalizability, we applied the model to two new datasets, the MIT-BIH Arrhythmia Database and the ECG-ID Database, achieving over 98.5% accuracy without any modifications. Furthermore, we show that repeating the authentication step three times can increase accuracy to nearly 100% for both PTBDB and ECGIDDB. This paper also presents model optimizations for embedded device deployment, which makes the system more relevant to real-world scenarios. To deploy our model in IoT edge sensors, we optimized the model complexity by applying quantization and pruning. The optimized model achieves 98.67% accuracy on PTBDB, with 0.48% accuracy loss and 62.6% CPU cycles compared to the unoptimized model. An accuracy-vs-time-complexity tradeoff analysis is performed, and results are presented for different optimization levels.

The Network Function Virtualization (NFV)-Resource Allocation (RA) problem is NP-Hard. Traditional deployment methods revealed the existence of a starvation problem, which the researchers failed to recognize. Basically, starvation here, means the longer waiting times and eventual rejection of low-priority services due to a ‘time out’. The contribution of this work is threefold: a) explain the existence of the starvation problem in the existing methods and their drawbacks, b) introduce ‘Adaptive Scheduling’ (AdSch) which is an ‘intelligent scheduling’ scheme using a three-factor approach (priority, threshold waiting time, and reliability), which proves to be more reasonable than traditional methods solely based on priority, and c) a ‘Dynamic Prioritization’ (DyPr), allocation method is also proposed for unseen services and the importance of macro- and micro-level priority. We presented a zero-touch solution using Deep Deterministic Policy Gradient (DDPG) for adaptive scheduling and an online-Ridge Regression (RR) model for dynamic prioritization. The DDPG successfully identified the ‘Beneficial and Starving’ services, efficiently deploying twice as many low-priority services as others, reducing the starvation problem. Our online-RR model learns the pattern in less than 100 transitions, and the prediction model has an accuracy rate of more than 80%.

Datacenter networks (DCNs) with cloud–edge collaboration are emerging to satisfy the communication, computation, and caching (3C) requirements of future services such as cloud-based IoT services. However, the enroute data over DCNs with cloud–edge collaboration is likely to suffer from cyberattacks such as eavesdropping. A large number of services require not only 3C resources, but also cryptographic resources for encryption to ensure high security. Quantum key distribution (QKD) is a practical approach to provide secret keys for remote users with information-theoretic security against attacks from quantum computing. A QKD-secured DCN (QKD-DCN) with cloud–edge collaboration can be deployed to satisfy the communication, computation, caching, and cryptographic (4C) requirements of services. This article innovatively solves the new 4C resource-allocation (4CRA) problem in the network to minimize the cryptographic resource consumption. It formulates an integer linear programming (ILP) model and proposes a heuristic cryptographic-dependent 4CRA algorithm to find optimal solutions. The proposed algorithm is compared with two baseline 4CRA algorithms which, respectively, consider the minimized service delivery latency and the first-fit resource availability. Analytical simulations show that the proposed algorithm minimizes the key-resource-consumption ratio and the average key-resource consumption under static and dynamic traffic scenarios in different network topologies.

An accurate prediction of rainfall is a very important task and has vital effects on human life. The usage of machine learning (ML) in the field of meteorology has provided solutions to improve the rainfall prediction accuracy. In the same direction, this study suggests an efficient methodology for the prediction of rainfall events with the aim of dimensionality reduction. Firstly, we identified most relevant features from the weather dataset which plays a major role in the prediction of a rainfall event using a wrapper-based feature selection (FS) technique. Secondly, principal components analysis (PCA) is integrated with the complete as well as with selected features dataset to reduce the data dimensionality. Finally, a thorough comparative analysis of different ML prediction models is presented with different nature of feature inputs. The performance of classification models improved significantly when using reduced features set. Specially PCA integrated with FS technique provided excellent prediction results.

Currently, several wireless testbeds are available to test networking solutions including Fed4Fire testbeds such as w-ilab. t and CityLab in the EU, and POWDER and COSMOS in the US. In this demonstration, we use the w-ilab.t testbed to set up a wireless ad-hoc Software-Defined Network (SDN). OpenFlow is used as an SDN protocol and is deployed using a grid wireless ad-hoc topology in w-ilab.t. In this paper, we demonstrate: (1) the configuration of a wireless ad-hoc network based on w-ilab.t and (2) the automatic deployment of OpenFlow in an ad-hoc wireless network where some wireless nodes are not directly connected to the controller. The configuration of OpenFlow is shown using the Fed4Fire GUI (Graphical User Interface). The automatic configuration time of OpenFlow is calculated during the demonstration. Further, data traffic is transmitted from each wireless device to another device to show that an OpenFlow network is set up correctly on the w-ilab.t testbed.

Over the past several years, the electrocardiogram (ECG) has been investigated for its uniqueness and potential to discriminate between individuals. This paper discusses how this discriminatory information can help in continuous user authentication by a wearable chest strap which uses dry electrodes to obtain a single lead ECG signal. To the best of the authors' knowledge, this is the first such work which deals with continuous authentication using a genuine wearable device as most prior works have either used medical equipment employing gel electrodes to obtain an ECG signal or have obtained an ECG signal through electrode positions that would not be feasible using a wearable device. Prior works have also mainly dealt with using the ECG signal for identification rather than verification, or dealt with using the ECG signal for discrete authentication. This paper presents a novel algorithm which uses QRS detection, weighted averaging, Discrete Cosine Transform (DCT), and a Support Vector Machine (SVM) classifier to determine whether the wearer of the device should be positively verified or not. Zero intrusion attempts were successful when tested on a database consisting of 33 subjects.

Network Function Virtualization (NFV) refers to the process of running network functions in virtualized IT infrastructures as softwarized Virtual Network Functions (VNFs). Several telecom service providers are currently benefiting from this concept, as it enables a faster introduction of new network services, thereby meeting changing requirements. Following a trend initially adopted by cloud service providers, telecom service providers are also adopting de-aggregation of the VNFs into microservices (μservices). However, a μservice-based architecture that can manage a large set of diverse and sensitive network functions requires new Artificial Intelligence (AI)-based methodologies to cope with the complexity of the μservice-based NFV paradigm. This paper focuses on the use of explainable AI (XAI) for gradually migrating towards a μservices-based architecture in NFV. The paper first establishes the need for XAI to transform the NFV architecture to a μservice-based architecture and then describes some of our research objectives. Afterwards, our preliminary approach and long-term visions are provided.

Transmission power control in energy-constrained mobile wireless sensor nodes is a necessary means to extend the battery lifetime and reduce the overall operational expenditure. The body-wearable sensors are perhaps the best examples when they are used for tracking and monitoring health of individuals and to carry out data analysis for possible symptoms of any disease. In indoor environments, the challenge is increased manifold due to complex multi-path propagation and temporal variation of link quality. This paper has enhanced our previous work on the state based adaptive power control protocol (SAPC) to suit well in indoor dynamic radio environments. The proposed protocol adjusts the state-transition rate R (drop-off rate) from a higher to a lower state, depending on the radio link quality. This improvement is implemented in SAPC protocol and renamed as SAPC-R. This protocol is compared with an existing practical adaptive power control protocol (P-ATPC) that can track variations in the radio link quality, and select an appropriate transmission power level and the number of retransmissions. The aim is to keep the algorithm computationally simple as it will run on battery powered devices and therefore are energy-constrained. Simulation results show that SAPC-R can save at least 25% energy as compared to P-ATPC. Results from the experiments that were conducted inside a University building show that by using the SAPC-R algorithm, energy consumption per successful transmission can be reduced by at least 15% as compared to P-ATPC while the packet success rates are comparable. This is a significant improvement, given the small changes in current consumption corresponding to the large changes in transmission power in present-day low power wireless transmitters.

Ultradense Internet of Things (IoT) mesh networks and machine-to-machine communications herald an enormous opportunity for new computing paradigms and are serving as a catalyst for profound change in the evolution of the Internet. The collective computation capability of these IoT devices is typically neglected in favor of cloud or edge processing. We propose a framework to tap into this resource pool by coupling data communication and processing. Raw data captured by sensing devices can be aggregated and transformed into appropriate actions as it travels along the network toward actuating nodes. This paper presents an element of this vision, whereby we map the operations of an artificial neural network onto the communication of a multihop IoT network for simultaneous data transfer and processing. By exploiting the principle of locality inherent to many IoT applications, the proposed approach can reduce the latency in delivering processed information. Furthermore, it improves the distribution of energy consumption across the IoT network compared to a centralized processing scenario, thus mitigating the “energy hole” effect and extending the overall lifetime of the system.

Ultra-dense Internet of Things (IoT) networks and machine type communications herald an enormous opportunity for new computing paradigms and are serving as a catalyst for profound change in the evolution of the Internet. We explore leveraging the communication within IoT to serve data processing by appropriately shaping the aggregate behavior of a network to parallel more traditional computation methods. This paper presents an element of this vision, whereby we map the operations of an artificial neural network onto the communication of an IoT network for simultaneous data processing and transfer. That is, we provide a framework to treat a network holistically as an artificial neural network, rather than placing neural networks within the network. The operation of components of a neural network, neurons and connections between neurons, are performed by the various elements of the IoT network, i.e., the devices and their connections. The proposed approach reduces the latency in delivering processed information and supports the locality of information inherent to IoT by removing the need for transfer to remote data processing sites.

This paper presents a novel Long Term Evolution (LTE) mobile wireless testbed for streaming multimedia Quality of Experience (QoE) experimentation. The software-radio based design of the testbed provides an extremely flexible architecture which offers detailed insights into all elements of the entire LTE network protocol stack from physical to application layer. With the testbed, we aim to bridge the gap between video content providers such as YouTube and commercial LTE operators by identifying and testing viable approaches to enhance end-user quality of experience (QoE). In this work, we focus on the design and implementation of the automated and instrumented testbed, and share results from our initial experiments.

Long-reach passive optical networks (LR-PONs) are able to effectively support the growing demand of traffic originating from residential and business customers. Failures of metro/core (M/C) nodes serving the traffic to/from the access networks covered by LR-PONs, may potentially affect hundreds or thousands of customers. One way of guaranteeing 100% survivability from single-node failures is to apply dual-homing, where each LR-PON is connected to two M/C nodes, and combine it with node-disjoint dedicated-path protection (DPP). In this paper, we present a new approach to provide network survivability against single M/C node failures. Instead of applying dedicated path protection (DPP) strategy, which can require huge amount of extra resources, we combine an unprotected network design with a dynamic multilayer restoration algorithm. Our aim is to determine a suitable amount of resource overbuild (in terms of extra transponders) needed to provide average connection availability close to that guaranteed by DPP. Preliminary results show that dimensioning for the worst-case scenario among a set of predefined M/C node failures, i.e., the one disrupting the highest number of connections, yields to a cost-effective strategy requiring up to 35% less transponders than DPP, while offering the same average connection availability.

As new Internet applications are emerging, traffic in optical backbone networks is increasing with heterogeneity in bandwidth and QoS requirements. Higher bit-rate wavelength channels of 40 Gbps, 100 Gbps, and beyond are being deployed in WDM backbone networks to meet these growing traffic needs. Mixed-line-rate (MLR) networks that support various line rates on the same fiber are becoming popular to address the heterogeneous traffic growth. We study the important problem of long-term cost-effective dimensioning of WDM backbone networks with MLR. Our study should enable operators to dimension their networks with deployment of MLR transponders and optical cross-connect (OXC) ports over multiple time periods. We study two multi-period dimensioning approaches: all-periods planning and incremental planning. Our approaches predict the network equipment to be deployed at various nodes in the network during each time period while exploiting the cost reduction of network equipment over time to support traffic growth over multiple time periods. We propose mixed-inter-linear-program (MILP) solutions and a computationally-efficient heuristic. Our illustrative examples show that significant cost savings can be achieved by our long-term planning approaches.

We propose an orthogonal frequency division multiplexing (OFDM) based backbone optical network design. We focus on minimizing the total energy consumption of the network i.e., to make the network green. OFDM is a promising technology for next-generation optical networks with per-wavelength capacities higher than or equal to 100 Gbps. In addition, it can support heterogeneity in network traffic by having flexible bandwidth allocation per wavelength. The flexibility comes through the multiple subcarriers in an OFDM signal which can be modulated with the client data signals. Another paradigm for supporting traffic heterogeneity and high bandwidth demands is mixed-line-rate (MLR) networks where wavelengths can have discrete capacities of 10/40/100 Gbps which are single carrier based. In this study, we compare the energy efficiency of an OFDM-based network versus a MLR network. Our results show that OFDM outperforms MLR in terms of energy efficiency.