This demonstration addresses a key open challenge in Open Radio Access Network (O-RAN) deployments: how to intelligently allocate Transport Network (TN) resources to ensure low-latency for mission-critical applications. The demo emulates a Smart Factory scenario where the time-sensitive control traffic of robotic arms competes with industrial camera broadband video streams. We propose an intelligent transport controller that combines network slicing, Adaptive Neuro-Fuzzy Inference System (ANFIS), and Federated Learning (FL) to dynamically prioritize traffic per slice. The architecture uses $\mathbf{P 4}$ switches for local queue monitoring and real-time resource scheduling. The integration with the O-RAN disaggregated stack is based on Open Air Interface (OAI). Experimental results demonstrate valuable load balancing and buffer occupation reduction in the O-RAN midhaul.

The growing popularity of eXtended Reality (XR) is being driven by technological advancements and the demand for advanced immersive digital experiences, including the vision around the metaverse. Within the XR realm, 360-degree immersive video streaming is essential for Virtual Reality (VR) adventures and experiences. The use of E2E encryption for content delivery in 360-VR streaming poses challenges for network operators, making it difficult to manage their networks and assess potential Quality of Experience (QoE) impairments, specifically in 5G and beyond networks. Therefore, we propose a Machine Learning (ML) approach for inferring 360-VR video QoE metrics from network-level encrypted traffic. Our solution uses packet-level information for feature engineering, which serves as input for the ML model to predict target QoE estimators. We evaluate our solution using real 4G and 5G drive test traces with encrypted VR traffic using HTTPS and QUIC protocols. The experimental results show that the trained ML model yields reasonable accuracy with minimal residual error in predicting target VR QoE for both HTTPS and QUIC. Network operators can use such a model to passively monitor the real-time QoE of encrypted VR video sessions and optimize network performance.

The exponential increase in Dynamic Adaptive Streaming over HTTP (DASH) based video traffic with endto-end encryption poses many challenges for Mobile Network Operators (MNOs). To improve user-perceived video quality, MNOs must be aware of the end user’s Quality of Experience (QoE) by exploring network level Quality of Service (QoS). On the contrary, the network edge facility provides proximity for performing an intelligent operation to maintain smooth QoE from the network level QoS measurement. Therefore, in this work, we propose EFFECTOR, a framework to showcase lightweight in-band QoS features measurement technique at edge nodes from encrypted DASH video traffic. EFFECTOR uses an emulated environment with real 4G and 5G drive test traces to generate video traffic. Moreover, this work provides a massive dataset for analyzing the impact of 4G and 5G technology on video quality in the form of Interactive Jupyter Notebooks. The proposed framework is ideal for investigating QoS extracted from the network’s edge and finding its relations with QoE to ensure better video quality for end-users.

The research community has recently proposed several solutions based on modern programmable switches to detect entirely in the data plane the flows exceeding pre-determined threshold in a time window, i.e., Heavy Hitters (HH). This is commonly achieved by dividing the network stream into fixed time slots and identifying each separately without considering the traffic trends from previous intervals. In this work, we show that using specified time windows can lead to high inaccuracies. We make a case for rethinking how switches analyze the incoming packets and propose to leverage per-flow Inter Packet Gap (IPG) analytics instead of using flow counters for HH detection. We propose an algorithm and present a P4 pipeline design using this new metric in mind. We implement our solution on P4 hardware and experimentally evaluate it against real traffic traces. We show that our results are more accurate than related work by up to 20% while reducing the control channel overhead by up to two orders of magnitude. Finally, we showcase a QoS-oriented application of the proposed dataplane-only IPG-based HH detection in a mobile network scenario.

Well-written code (which meets standards, conventions, and quality specifications) is often associated with the programmer's experience, which is why companies have been looking for increasingly qualified professionals. Codes at odds with these standards tend to be complex and poorly written and are generally difficult to understand. Consequently, the activities inherent to it become costly. Although well defined and consolidated in the industry, the concepts of code quality taught in the academy are insufficient to enable students to meet market expectations. Research indicates that graduates do not feel prepared to enter the labor market, facing difficulties when competing for the best job opportunities. This difficulty highlights a gap between industry and academia. Researchers have identified the gap and have proposed improvements to the teaching-learning process based on using the concepts and tools widely used in the software industry in an academic environment. When analyzing educational institutions' programming disciplines, it is possible to notice a mismatch between programming disciplines and code quality disciplines. In this scenario, this project aims to propose improvements on how knowledge of programming and quality is evolved, proposing an approach that uses software quality as a subsidy for teaching programming, providing the teacher with guidelines for teaching programming focusing on internal quality source code. We idealized an innovative practice that brought programming, usually focused on the execution of algorithms, within the teaching of code quality, focused on software engineering principles. The use of code inspection tools allowed direct teaching according to the class's needs, introducing a guided content-based internal quality teaching in programming disciplines without generating extra work for the teacher. In addition to the improvements for the teacher, we are convinced that from the student's perspective, we were able to motivate them to learn to program not only concerned with the execution results but also how the solution is developed.

Scenarios for distributed cloud with multiple edge clouds and centralized data centers are being investigated as the computing and networking underpinnings of next-generation network services such as augmented reality, self-driving vehicles, drones, and more. In such distributed environments, service providers will typically face tens, hundreds, or thousands of compute location candidates (edge, regional, and central) where network service components can be placed. To take optimized placement decisions of network services and execute the management workflows, orchestration systems require up-to-date and accurate resource availability representation, in the form of a network inventory that can be immense in distributed cloud scenarios. As a result, the service management and placement problems may become not tractable. In this work, we propose the Abstracted Network Inventory (ANI) component to generate service-optimized network views over the same network inventory. ANI implements a novel abstraction method where network service requirements are used as an input to generate an optimized abstract network inventory representation, called Logical Network Inventory (LNI). We also provide a formal definition of the network model and problem statement along with the development of three algorithms to efficiently build an LNI. Results show the potential benefits of using an LNI to streamline service management and placement: (i) the relationship between compute nodes and links (i.e., density) in an LNI is reduced between 1.8-2.7x compared to a full network inventory topology; and (ii) up to 50% of time can be saved for service placement after abstracting around 20% of the compute nodes.

Intent-Based Networking (IBN) proposals are based on autonomous closed-loop orchestration architectures that monitor and tune network performance. To this end, IBN defines high-level policies and actions implemented by a closed-loop system. This work demonstrates a Closed Control Loop (CCL) architecture for video service assurance using Machine Learning (ML) based Quality of Experience (QoE) estimation at edge nodes. As part of the solution, network-level Quality of Service (QoS) metrics patterns (e.g., RTT, Throughput) collected through flow-level monitoring are used to build a QoS-to-QoE correlation model tailored to specific target network regions, user groups, and services, in our case DASH video streaming. The demo will showcase the CCL workflow triggering the Orchestrator to take appropriate network-level actions to overcome network QoS degradations and restore the QoE target based on the intent associated with the video service.

This paper proposes a fault-tolerant indoor localization system that employs Recurrent Neural Networks (RNNs) for the localization task. A decision module is designed to detect failures and this is responsible for the allocation of RNNs that are suitable for each situation. As well as the fault-tolerant system, several architectures and models for RNNs are exploited in the system: Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM) and Simple RNN. The system uses as inputs a collection of Wi-Fi Received Signal Strength Indication (RSSI) signals, and the RNN classifies the position of an agent on the basis of this collection. A fault-tolerant mechanism has been designed to handle two types of failures: (i) momentary failure, and (ii) permanent failure. The results show that the RNNs are suitable for tackling the problem and that the whole system is reliable when employed for a series of failures.

Orchestration is crucial to address the growing complexity of IT, cloud and telecom services. Orchestrators enable service deployment, but continuous operation is given second-class status. Most orchestrators use static workflows, which may fail, misbehave or be insufficient, leaving services in an undesirable state. We present Reorchestration, a prototype showing orchestration without workflows, combining functional-inspired state management with an infrastructure-as-code approach. The prototype demonstrates the viability of our ideas in some scenarios, and we expect it will contribute to discussions on software architectures for closed-loop automation.

Orchestration is crucial to address the growing complexity of IT, cloud and telecom services. Orchestrators enable service deployment, but continuous operation is given second-class status. Most orchestrators use static workflows, which may fail, misbehave or be insufficient, leaving services in an undesirable state. We propose the Reorchestration architecture for orchestration without workflows, which marries functional-inspired state management with an infrastructure-as-code approach. It enables changes to be applied to existing services, while providing a high-level framework for expressing how these changes should happen. We also share our first prototype, in the hope that it can foster discussions on new software architectures for orchestrators and closed-loop automation.

Currently, there is an increasing number of patients that are treated in-home, mainly in countries such as Japan, USA and Europe. As well as this, the number of elderly people has increased significantly in the last 15 years and these people are often treated in-home and at times enter into a critical situation that may require help (e.g. when facing an accident, or becoming depressed). Advances in ubiquitous computing and the Internet of Things (IoT) have provided efficient and cheap equipments that include wireless communication and cameras, such as smartphones or embedded devices like Raspberry Pi. Embedded computing enables the deployment of Health Smart Homes (HSH) that can enhance in-home medical treatment. The use of camera and image processing on IoT is still an application that has not been fully explored in the literature, especially in the context of HSH. Although use of images has been widely exploited to address issues such as safety and surveillance in the house, they have been little employed to assist patients and/or elderly people as part of the home-care systems. In our view, these images can help nurses or caregivers to assist patients in need of timely help, and the implementation of this application can be extremely easy and cheap when aided by IoT technologies. This article discusses the use of patient images and emotional detection to assist patients and elderly people within an in-home healthcare context. We also discuss the existing literature and show that most of the studies in this area do not make use of images for the purpose of monitoring patients. In addition, there are few studies that take into account the patient's emotional state, which is crucial for them to be able to recover from a disease. Finally, we outline our prototype which runs on multiple computing platforms and show results that demonstrate the feasibility of our approach.

We present the design and implementation of Multi-Channel Collection (MCC) protocol , a high-rate multi-channel time-scheduled protocol for fair, real-time data collection in Wireless Sensor Networks (WSN). MCC incorporates sophisticated mechanisms for balanced routing tree formation, multiple frequency channel allocation and globally synchronized TDMA scheduling. Through systematic experiments with real WSN hardware (Tmote Sky), we identify the maximum possible throughput for many-to-one (convergecast) data collection as a function of key communication parameters such as packet size, use of acknowledgements, and network topology. Then, we demonstrate that the maximum achievable network throughput can in fact be attained in practice using a carefully designed mix of routing, frequency allocation and time scheduling. Compared to state of the art collection protocols for WSN, we show that MCC offers 33-155% improvement in throughput. We also show how to exploit the time-scheduled nature of this approach for reducing the number of required frequency channels. MCC presents an algorithmic approach for time-frequency scheduling and routing that could be adapted and used in conjunction with relevant emerging standards such as WirelessHART, ISA 100.11a and IEEE 802.15.4e TSCH.

This paper proposes an architecture for mobile wireless networks based on Radio-over-Fiber (RoF) technology. The architecture organizes cells in a multi-tier fashion, with different tiers covering areas with different radii lengths. Proposed optimization algorithm splits cells to improve network capacity in congested areas and merges cells when the demand is low. The evaluation of the effectiveness of the proposed model considered three distinct objectives: minimization of the number of base stations used, maximization of the number of users served, and minimization of network energy consumption. The combination of the first two objectives was also evaluated. Three algorithms based on linear relaxation techniques are introduced for rapid computation of the near-optimum solutions. The proposed architecture is shown to lead to network infrastructures that save costs and energy and yet provide service to a large number of users.

This article presents a radio resource optimization model for Radio-over-Fiber (RoF) access networks. The proposed model arranges cells in a multi-tier fashion with increasing coverage radius. Considering the available structure of antennas the optimizer performs dynamic cell merging and cell splitting according to mobile users' demands for efficiently utilizing radio resources. It is proposed an integer programming model with a bi-criteria objective function that tries to minimize the use of network resources as well as to maximize network revenue. The computational demand for obtaining integer solutions increases proportionally to the number of mobile users and also to the number of tiers of antennas. In addition to the integer solutions, an algorithm based on linear relaxation technique is presented, which implies on significant computational time reduction when solving large instances of the problem. Moreover, results are very close to those given by the integer programming formulation.

In this paper, we introduce a radio resource optimization model for Radio-over-Fiber (RoF) access networks. We propose a mixed integer programming model to minimize the network cost while providing service to all mobile users. The proposed architecture arranges cells in a multi-layer fashion, with cells in each layer providing different coverage radius. The optimization algorithm performs dynamic cell splitting to improve network capacity in congested areas and cell merging in areas with low demand to save resources. The computational demand of the proposed model increases proportionally to the number of layers of RAUs in the infrastructure. Results indicate that two layers of RAUs give the best trade-off between network cost reduction and computational demand.

In this paper, we introduce a novel queue management technique for the buffer space at the base station of infrastructure 802.11 networks that considers mobile user's receiving characteristics. The maximum amount of the base station buffer that can be used by a given flow is updated proportionally to RTT, measured at the mobile nodes and sent to the base station my the mean of link layer acknowledgements. In this way, the proposed scheme remains transparent to high-level protocols. The proposed approach makes possible the implementation of algorithms able to provide RTT-fairness in wired-cum-wireless networks. Results show the advantage of the proposed queue management scheme when compared to that of traditional drop-tail queue management.