Efficient routing in entanglement distribution networks is a challenging task that requires the coordinated allocation of quantum memories, repeaters, and quantum operations such as entanglement swapping and distillation across dynamic network conditions. In this paper, we present a Deep Q-Learning (DQN) Two-Stage Routing strategy designed to improve both path and quantum operations selection. Our approach enables the agent to make decisions over a diverse action space, including request scheduling, multipath routing, and the strategic application of quantum operations to maximize entanglement success and resource efficiency. We further extend DQN to integrate Double DQN, Dueling DQN, and Prioritized Experience Replay (PER) architectures. Simulation results indicate that our approach significantly outperforms baseline methods regarding request success rate, especially in large-scale networks under strict fidelity requirements.

The advancement of mobile quantum networks unlocks new opportunities for secure communication in dynamic environments, such as drone-based networks equipped with quantum repeaters. This work introduces Q-CAST Mobility, an adaptation of the Q-CAST quantum routing protocol, specifically optimized for mobile quantum nodes. By integrating concepts from classical reactive and proactive routing protocols, inspired by the Zone Routing Protocol (ZRP), and leveraging Q-CAST’s quantum routing framework, Q-CAST Mobility combines multipath routing, dynamic route updates, and efficient qubit management to maintain robust quantum entanglements in highly mobile scenarios. To assess its performance, we conducted simulations across various mobility levels, comparing Q-CAST Mobility with state-of-the-art methods. The results demonstrate that Q-CAST Mobility significantly outperforms existing protocols regarding throughput, underscoring its efficiency and adaptability in mobile quantum communication networks.

The research agenda on programmable data planes has been primarily focused on high-end networking devices, driven by technical requirements derived from operations & management needs of large scale datacenters and cloud providers. In this paper, we argue in favor of a yet incipient but equally paramount and challenging topic in this agenda: research on programmable low-end devices, like Low-power wide-area network (LPWAN). One main motivation is “unlocking” LPWAN, enabling one to freely redefine how they parse and process packets by means of Domain-specific languages such as P4. In addition to reducing capital expenditure by allowing interoperability between devices from multiple vendors, programmability would open LPWAN to an entire novel class of use cases, like providing inclusive internet access to technologically marginalized populations (such as rural communities). To contribute to this emerging research agenda, we propose a conceptual architecture and demonstrate the technical feasibility of a Programmable LPWAN by means of a proof-of-concept prototype, built using off-the-shelf hardware. More importantly, we present and discuss valuable lessons towards the design of such devices, maintaining their popular characteristics (like low power, low cost, long rage) yet freely (re)programmable for a broader class of novel use cases.

We present a prototype that could open the doors to mitigate digital exclusion in technologically underserved pop-ulations. By converging software-defined networks (SDN) with programmable data planes (PDP), our approach enables one to customize low-cost hardware to fit the network behavior. We integrate low-power wide-area networks (LPWANs), known for their scalability and efficiency, with PDPs to propose a flexible solution for changing requirements, such as distance and terrain configurations, validated through real-world test scenarios.

The OpenRAN@Brasil project has been advancing the evolution of Open RAN technology by enabling private SG networks in various fields, such as education, government, and industry. With this, there has been progress in techniques and de-velopments to improve the adoption of the technology and spread the use of Open RAN. This demonstration addresses the chal-lenges of fragmentation and complexity inherent in transitioning to open and disaggregated networks, leveraging an intent-based strategy to simplify the deployment and management of network services. The results demonstrate the feasibility of the solution by successfully provisioning 5G core network functions (Open5GS) and edge functions (SRS- RAN) in different Kubernetes clusters, allowing UE registration and access to Internet services. The integration of technologies such as OpenSGS, SRS-RAN, and Nephio highlights the potential for automation, scalability, and operational efficiency in distributed environments.

Quantum Internet aims to enable quantum communication between any two points, offering applications such as quantum key distribution (QKD), distributed quantum computing, and entanglement networks. However, the current quantum technology presents challenges with low entanglement (EPR pairs) generation rates, limited quantum memory capacity, and decoherence rates that often lead to unusable EPR pairs due to low fidelity. This presents a significant challenge for tasks such as routing. In this paper, we tackle this challenge by introducing qRL, a quantum-aware routing protocol that utilizes reinforcement learning to optimize quantum routing decisions. We show that qRL consistently outperforms traditional methods by maintaining higher fidelity routes and request success rates in different network configuration scenarios.

The emergence of quantum computing and related technologies presents opportunities for enhancing network security. The transition towards quantum computational power paves the way for creating strategies to mitigate the constantly advancing threats to network integrity. In response to this technological advancement, our research presents QML-IDS, a novel Intrusion Detection System (IDS) that combines quantum and classical computing techniques. QML-IDS employs Quantum Machine Learning (QML) methodologies to analyze network patterns and detect attack activities. Through extensive experimental tests on publicly available datasets, we show that QML-IDS is effective at attack detection and performs well in binary and multiclass classification tasks. Our findings reveal that QML-IDS outperforms classical Machine Learning methods, demonstrating the promise of quantum-enhanced cybersecurity solutions for the age of quantum utility.

Information Technology (IT) changes are a critical part of the day-to-day operations of most modern organizations, and poor change delivery can pose severe risks to business continuity. In this context, frameworks like COBIT seek to provide guidance for best practices and procedures for proper IT change management, and shareholders often resort to auditing to ensure change delivery following defined procedures. To this end, third-party audit companies perform periodic inspections of the target IT system, log of changes deployed, etc. However, the sheer volume of changes, ever-increasing change complexity, and automation make it challenging to deliver change auditing between inspection events. To tackle this issue, we propose in this paper a blockchain-based approach for continued IT change auditing. In summary, we instrumented a change orchestration framework with a solution for certifying each change deployed in the target system through blockchain. The chain of IT changes in between inspection events is then used to ensure that only certified changes were deployed in the infrastructure.

Network slicing (NS) is becoming an essential element of service management and orchestration in communication networks, starting from mobile cellular networks and extending to a global initiative. NS can reshape the deployment and operation of traditional services, support the introduction of new ones, vastly advance how resource allocation performs in networks, and notably change the user experience. Most of these promises still need to reach the real world, but they have already demonstrated their capabilities in many experimental infrastructures. However, complexity, scale, and dynamism are pressuring for a Machine Learning (ML)-enabled NS approach in which autonomy and efficiency are critical features. This trend is relatively new but growing fast and attracting much attention. This article surveys Artificial Intelligence-enabled NS and its potential use in current and future infrastructures. We have covered state-of-the-art ML-enabled NS for all network segments and organized the literature according to the phases of the NS life cycle. We also discuss challenges and opportunities in research on this topic.

Caching plays a central role in networked systems, reducing the load on servers and the delay experienced by users. Despite their relevance, networked caching systems still pose a number of challenges pertaining their long term behavior. In this paper, we formally show and experimentally evidence conditions under which networked caches tend to synchronize over time. Such synchronization, in turn, leads to performance degradation and aging, motivating the monitoring of caching systems for eventual rejuvenation, as well as the deployment of diverse cache replacement policies across caches to promote diversity and preclude synchronization and its aging effects. Based on trace-driven simulations with real workloads, we show how hit probability is sensitive to varying channel reliability, cache sizes, and cache separation, indicating that the mix of simple policies, such as Least Recently Used (LRU) and Least Frequently Used (LFU), provide competitive performance against state-of-art policies. Indeed, our results suggest that diversity in cache replacement policies, rejuvenation and intentional dropping of requests are strategies that build diversity across caches, preventing or mitigating performance degradation due to caching aging.

Network traffic engineering aims at the network quality, optimizing routes and detecting network attacks. In this context, traffic prediction is an essential tool to capture the underlying behavior of a network. Therefore, this work proposes a modularization architecture for volumetric prediction models, allowing switching between models and setups at runtime in controllers of Software Defined Networks (SDN), dealing with short time series and delivering the data already processed for the prediction. The proposed architecture compares the results from four traditional predictors based on short-range time dependency.

Cloud computing allows for on demand storage and sharing of records with high degree of availability. However, storing a health record in a cloud provider requires trusting it for the record security. By mitigating it, current approaches focus on confidentiality and access control while not properly handling data integrity. This paper presents a protocol that employs attribute-based cryptography and decentralized networks for secure storage and sharing of health records. The solution addresses confidentiality, access control and integrity of records. A proof of concept of the protocol was implemented and load tests were executed in order to demonstrate its practical feasibility.

In blockchain, full nodes (FNs) are peers that store and verify entire chains of transactions, and light clients (LCs) are those which outsource chain verification to FNs (as they lack computing resources required to do so). In general, LCs perform simpler verification protocols, e.g. Simple Payment Verification (SPV), by offloading the execution of blockchain operations to FNs. To cope with byzantine faults (like malicious behavior), a current approach for blockchain transaction verification is requiring that LCs outsource their requests to multiple FNs, and compare received results. This approach, however, requires that LCs establish secure connections to each FN, which leads to client-side complexity and slower verification. To tackle this issue, we propose Distributed Lightweight Client Protocol (DLCP), a protocol for secure verification in blockchain. In summary, DLCP requires LCs to encrypt a request once, allowing a pre-determined set of FNs to access and process it. Through DLCP, LCs become able to verify whether FNs have agreed on the operation outcome. From some preliminary evaluation, we observed that DLCP decreased computing and communication overhead in LCs, while providing lower latency.

Software-Defined Networking (SDN) has turned the Data Center Network (DCN) environment into a more flexible one by decoupling control plane from data plane, allowing an innovative and easily extensible network management solutions. Nowadays, OpenFlow is the most successful protocol for SDN. However, SDN based on OpenFlow protocol presents performance issues on forwarding table increasing and packet match cost. Our proposal named Tag-and-Forward (TF) is a data plane that reduces the number of flow table required in the Fat-Tree software-defined DCNs to optimize forwarding. The results noticebly outperformed RTT and packet transmission rate when compared to usual OpenFlow data plane.

Aim: Getting information to automatically collect object oriented metrics (OO metrics) in order to assist the comprehension and assessment of software products. Method: It was developed a study based on a systematic review and 37 primary studies were selected from 577 papers retrieved in 3 databases. Result: 177 metrics that can be automatically collected were cataloged. Besides, 27 from such total were the most referenced. The cataloged metrics were classified according to the quality characteristics which were related; 18 collection tools have been identified. This way, it was concluded that there is a set of common procedures for collecting OO metrics and the Java and C++ are the languages with the largest number of tools on which is possible to extract metrics.

Programmability is one of the key factors on software defined networks (SDN), once it allows to decouple between data plan and control plan. However, in such context, managing several applications' inputs and outputs is a little explored task on controllers field, which are not in a controlled and organized environment. This article proposes a method to make SDN applications to reach the administrator, allow him to manage his use in the network. This proposal is validated through performance tests in the repository, which stores the applications, along with developed modules to help its management and control, making the organization and coordination method safe, efficient and adjustable to any controller on software-defined network.

Wireless Mesh Networks (WMNs) are increasingly deployed to enable thousands of users to share, create, and access live video streaming with different characteristics and content, such as video surveillance and football matches. In this context, there is a need for new mechanisms for assessing the quality level of videos because operators are seeking to control their delivery process and optimize their network resources, while increasing the user’s satisfaction. However, the development of in-service and non-intrusive Quality of Experience assessment schemes for real-time Internet videos with different complexity and motion levels, Group of Picture lengths, and characteristics, remains a significant challenge. To address this issue, this article proposes a non-intrusive parametric real-time video quality estimator, called MultiQoE that correlates wireless networks’ impairments, videos’ characteristics, and users’ perception into a predicted Mean Opinion Score. An instance of MultiQoE was implemented in WMNs and performance evaluation results demonstrate the efficiency and accuracy of MultiQoE in predicting the user’s perception of live video streaming services when compared to subjective, objective, and well-known parametric solutions.

As Wireless Mesh Networks (WMNs) are being increasingly deployed, there is an increasing demand for new quality assessment mechanisms that allow service operators to evaluate and optimize the utilization of network resources, while ensuring a good quality level on multimedia applications as perceived by end-users. However, existing real-time assessment schemes for WMNs are not capable of capturing the actual quality of received multimedia content with regard to user perception. Therefore, it is not possible to assure the user experience of content services. To address this problem, this paper introduces the Hybrid Quality of Experience (HyQoE) Prediction, which is a quality estimator specially designed to assess real-time multimedia applications. HyQoE is designed based on the framework of the widely used Pseudo-Subjective Quality Assessment (PSQA) Tool which exploits Random Neural Network (RNN). Crucial extension work has been implemented to achieve our objectives. A performance evaluation verifies the effectiveness and advantages of HyQoE in predicting users' perception of multimedia content in WMNs over existing subjective and hybrid methods.

As Wireless Mesh Networks (WMNs) have been increasingly deployed, where users can share, create and access videos with different characteristics, the need for new quality estimator mechanisms has become important because operators want to control the quality of video delivery and optimize their network resources, while increasing the user satisfaction. However, the development of in-service Quality of Experience (QoE) estimation schemes for Internet videos (e.g., real-time streaming and gaming) with different complexities, motions, Group of Picture (GoP) sizes and contents remains a significant challenge and is crucial for the success of wireless multimedia systems. To address this challenge, we propose a real-time quality estimator approach, HyQoE, for real-time multimedia applications. The performance evaluation in a WMN scenario demonstrates the high accuracy of HyQoE in estimating the Mean Opinion Score (MOS). Moreover, the results highlight the lack of performance of the well-known objective methods and the Pseudo-Subjective Quality Assessment (PSQA) approach.

The Future Internet will arise from the convergence of new network concepts and combine technologies, services, media and content. It will offer flexibility and diversity with scalable content and services that are accessible through a wide range of interfaces and devices. However, the biggest challenge now is how to enable and test the proposed approaches so that they can be validated without sacrificing the current production infrastructure. The OpenFlow protocol allows production networking environments such as campus networks, metropolitan networks or R&D networks, to be used as experimental infrastructure hosting, future Internet architectures, software and protocols, in parallel with the production traffic. During rollout, there is a practical problem that arises with Legacy networks that do not support OpenFlow and need to be replaced/upgraded or refined by means of costly network re-engineering. This paper proposes a new OpenFlow architecture with new components, capable of managing Legacy-non-OpenFlow elements by offering a new solution that facilitates the management of Legacy technologies and allows them to be employed in FI experimentation environment and increase the number of experiment with the Legacy Network Environment using OpenFlow control.

The proliferation of multimedia content and Wireless Mesh Networks (WMNs) are changing the Internet facilities and life style of fixed and mobile users. For the success of the next generation wireless networks, novel multimedia approaches with quality level assurance are required to allow the distribution of video-streaming, video conference, gaming, multimedia social networking, green multimedia content and other entrainment applications for thousand of users in ubiquitous wireless (mesh) systems. In this context, new routing schemes are needed to provide end-to-end Quality of Service (QoS) and Quality of Experience (QoE) support for delay/loss/jitter-sensitive multimedia applications in WMNs. The well-known OLSR (Optimized Link State Routing) protocol with ETX (Expected Transmission Count) metric bring many benefits for the path selection process, but present a drawback in the queue availability management and reduce the system performance. Therefore, multimedia-related packets will suffer with loss/delay/jitter and the system overall performance will decrease. This paper proposes the Queue-based OLSR ETX (QoETX) approach to overcome the limitations of OLSR-ETX regarding queue availability by using a cross-layer scheme and supporting QoS and QoE assurance. QoETX optimizes network and user-based parameters by coordinating queue availability, QoS and fuzzy issues in the routing decision process as a way to allocate the best paths for multimedia applications. In order to present the benefits of the proposed solution compared with existing routing schemes, namely OLSR-ETX, OLSR-FLC, OLSR-MD and HWMP (IEEE 802.11s standard), regarding QoS (block probability and throughput) and QoE (PSNR, SSIM, VQM and MOS) parameters, performance evaluations were carried by using the Network Simulator (NS-2.34).

We present a multiple-metric approach in order to improve routing in wireless mesh networks (WMNs). It is based on the proactive optimized link state routing (OLSR) protocol to deal with applications with high quality of service (QoS) demands. Since routing with multiple metrics is an NP-complete problem, we use the analytic hierarchy process (AHP) and pruning techniques to perform such routing. The proposal, dubbed here OLSR-MM (multiple-metric), offers the best available routes based on the considered metrics and is evaluated on Network Simulator 2.31.

The Transmission Control Protocol - TCP has been widely researched in many different networking technologies such as wireless, optical, satellite, sensor, and wired since it is the most used protocol on the Internet. In Wireless mesh networks, this protocol still has challenges due to its origin and implementations for wired networks. This paper proposes an adaptation on the congestion control mechanism using information from the network layer to improve TCP performance through the vertical calibration across layer technique. The result is the Cross Layer Mesh - Transmission Control Protocol (CLM-TCP). To show the efficiency of the proposal, simulations in the NS-2 were carried out.

One of the main problems faced by ad hoc networks is providing specific quality of service guarantees for multimedia applications, mainly due to factors such as radio signal fading and node mobility. Since mesh networks are a special type of ad hoc network, they inherit these networks' problems. This paper's main goal is to present OLSR-MD, an extension to OLSR (optimized link state routing), to provide quality of service based on link delay measurements. An evaluation of OLSR-MD in a mesh network to be deployed at the Federal University of Para, by means of ns2 (version 2.30) simulations, showed that this protocol performed better than other OLSR based alternatives studied in the simulations.