Carbon-aware orchestration has emerged as a key enabler for reducing the environmental impact of Information and Communication Technology (ICT) infrastructures. While existing orchestration frameworks primarily optimize for performance-related metrics, the integration of sustainability-aware decisionmaking into production-grade MANO platforms remains limited. This paper presents the design, integration, and quantitative evaluation of two carbon-aware orchestration mechanisms, i.e., service throttling and spatial shifting, which are representative of being embedded into the ETSI-compliant Open-Source MANO (OSM). The mechanisms are validated in a maritime port video analysis use case, focusing on an Automatic License Plate Recognition (ALPR) pipeline deployed across edge and cloud environments. Experimental results show that the integrated carbon-aware orchestration mechanisms can reduce daily operational carbon emissions by up to 47.3%, without violating service requirements. These findings demonstrate that integrating carbon-awareness directly into MANO workflows is feasible and effective, providing a practical pathway for network operators and vertical industries to meet decarbonization targets.

Future networks must meet stringent requirements while operating within tight energy and carbon constraints. Current autoscaling mechanisms remain workload-centric and infrastructure-siloed, and are largely unaware of their environmental impact. We present NeuroScaler, an AI-native, energy-efficient, and carbon-aware orchestrator for green cloud and edge networks. NeuroScaler aggregates multi-tier telemetry, from Power Distribution Units (PDUs) through bare-metal servers to virtualized infrastructure with containers managed by Kubernetes, using distinct energy and computing metrics at each tier. It supports several machine learning pipelines that link load, performance, and power. Within this unified observability layer, a model-predictive control policy optimizes energy use while meeting service-level objectives. In a real testbed with production-grade servers supporting real services, NeuroScaler reduces energy consumption by 34.68

Reliable localization is critical for cooperative vehicular systems, yet most methods optimize accuracy without quantifying positional trust. This work proposes a hybrid 5G–Global Navigation Satellite Systems (GNSS) framework that attaches a calibrated confidence score to each position estimate, enabling uncertainty-aware operation in Vehicle-to-Everything (V2X) environments. Fusing 5G radio observables with GNSS data through adaptive gain filtering and probabilistic mapmatching reduces median error from 27 m to below 10 m in a live 5G testbed, with a confidence score empirically calibrated against absolute positioning error $(r=-0.59)$, providing a monotonic estimate of positional reliability. This interpretable confidence layer enables vehicles and infrastructure to adapt their behavior based on positional trust, improving both safety decisions and communication efficiency in cooperative scenarios. By exposing confidence as a signal, the framework bridges localization, communication, and cooperative decision-making in future 6G V2X systems, validated against synchronized GNSS ground truth over approximately 45,000 samples.

Modern telecommunications networks are no longer required to perform only traffic management and connection establishment functions, but are increasingly evolving into general-purpose processing engines. This is facilitated by InNetwork Computing (INC), which has emerged as a key enabling paradigm in this direction. This shift in vision, however, requires a complete redesign of network infrastructures, with a strong emphasis on deep integration between networking and artificial intelligence capabilities. In the wake of these considerations, in this work we introduce an INC framework for the 3GPP 6G architecture to offload processing from the user device to the processing instances of the 6G mobile system, based on service requirements and constraints of individual user sessions, and delve into 3GPP Control Plane (CP) and User Plane (UP) aspects. Furthermore, we provide an experimental evaluation of the advantages of our INC framework in saving processing resources and energy in the user device while meeting the performance requirements of an AI-powered application.

Recent years have brought a consensus in the telecommunications industry that a strong reduction of overall energy consumption and CO(2)e emission of running networks is a critical goal that must be achieved. For this to be possible, a mindset change is necessary in the industry, in particular in the relevant standards setting organizations, such as 3GPP. A whole new infrastructure for measuring energy consumption at various levels of granularity (e.g., per service, per user), measurement data collection and dissemination to relevant parties (e.g., service users, other domains), as well as optimized usage of available energy must be in place in the networks to enable this transition towards sustainable networks. This paper presents an approach on how this infrastructure can be standardized in 3GPP and references efforts in related standardization bodies. We analyze the current status of sustainability related efforts in 3GPP, identify the main gaps that need to be bridged, and provide concrete steps in terms of enhancing existing technical specifications or creation of new ones, to make the said infrastructure operational. We also report on the concrete efforts made within the EU funded project EXIGENCE to realize this vision.

The exponential growth of Information and Communications Technologies (ICT) is projected to contribute significantly to global carbon emissions, necessitating energy-efficient solutions for sustainable network management. Network Function Virtualization (NFV) has emerged as a key enabler to reduce infrastructural cost, which, meanwhile, gains high potential to support energy-aware ICT with resource utilization and energy consumption optimization. However, standardization efforts lack comprehensive frameworks for carbon-aware orchestration, and energy measurement/coordination in multi-domain networks. This paper addresses this gap by first giving a comprehensive overview of current standardization efforts related to energy-aware ICT management from the perspective of 3GPP, ETSI, and ITU-T. Based on the gap analysis of current standardization, we present an architectural extension with new strata and functions, supporting decarbonization in NFV-based multi-domain ICT management. A case study applying this framework in a setting involving the orchestration of video surveillance services demonstrates the necessity and effectiveness of the functionalities in reducing energy consumption while maintaining service quality. Together with highlighting key challenges in the standardization of the proposed functionalities, this paper provides crucial insights into future standardization efforts, paving the way for a carbon-neutral ICT landscape.

This paper proposes a digital twin (DT)-enabled edge computing framework for healthcare task offloading in smart medical environments. The DT maintains a continuously synchronized virtual representation of the physical edge system, capturing queue states, resource utilization, and energy dynamics. Based on this virtual state, a deterministic optimization model is formulated to support real-time offloading and scheduling decisions under latency, energy, and fairness constraints. Unlike prediction-only approaches, the proposed DT operates in a closed-loop manner, where state estimation and synchronization directly influence scheduling feasibility and system performance. The offloading problem is formulated as a mixed-integer linear programming (MILP) model to jointly optimize task allocation, delay minimization, and energy efficiency. The simulated workload includes 10,000 heterogeneous healthcare applications. At each time slot, one to five tasks are generated and uniformly selected from ECG, video-processing, or medical-imaging workloads, with average task sizes of 90 KB, 280 KB, and 150 KB, respectively. This setup aims to emulate diverse real-world healthcare edge workloads with varying communication and computation demands. The proposed approach significantly reduces average latency by up to 57%, eliminates task drops in all evaluated scenarios, and improves load balancing compared with hospital-only and round-robin baselines. Although total energy consumption increases moderately, energy efficiency per completed task improves due to more effective scheduling by stability, reducing deadline violations and enhancing resource utilization. We further analyze the impact of DT freshness and updated frequency and show that outdated or misaligned DT updates can degrade performance by increasing delay and leading to suboptimal decisions, while overly frequent updates introduce additional coordination overhead. These results highlight the importance of jointly designing DT synchronization mechanisms and optimization-based scheduling strategies for reliable and cost-efficient healthcare edge systems.

The transition to Industry 4.0 is transforming manufacturing through smart, autonomous, and highly interconnected systems that demand ultra-low latency, high reliability, and deterministic communication. Emerging technologies such as Wi-Fi 7 (IEEE 802.11be) and Time-Sensitive Networking (TSN) offer promising capabilities to meet these requirements. Wi-Fi 7 enhances wireless performance with features like Multi-Link Operation (MLO), 320 MHz bandwidth, and improved Quality of Service (QoS), while TSN enables deterministic Ethernet communication through precise time synchronization and traffic scheduling. This paper proposes a scalable IT-OT converged architecture that integrates Wi-Fi 7 and TSN to support high-throughput, low-latency, and time-sensitive communication, enabling reliable mobility and seamless interoperability across industrial systems. Real-world evaluations in a factory environment demonstrate the higher performance and robustness of the proposed solution, providing practical guidance for deploying TSN-integrated Wi-Fi 7 networks in smart manufacturing.

The expansion of the autonomous mobility market has brought unprecedented challenges to Multi-access Edge Computing (MEC) servers, including the deterioration of latency and energy consumption performance, which falls into multi-objective and integer programming optimization problems for service instantiation among MECs. Additionally, feasible solutions to support service instantiation among MECs in Cooperative Driving Automation (CDA) overlook the interoperability issues by exposing MNOs’ (in general, Mobile Network Operators’ (MNOs’)) privacy sensitive data. Therefore, this paper proposes a novel fully-decentralized MEC network architecture for CDA, targeting overall latency and energy consumption minimization, simultaneously, while enhancing multi-MNO interoperability. This is achieved by introducing a novel network orchestration algorithm, which decentralizes the network orchestration decision-making to individual MECs, while avoiding the exposure of the MNOs’ privacy sensitive data. Simulation results show that the proposed algorithm achieves an overall average latency and energy consumption of 33.2ms and 8.1W for 5 MECs, outperforming the selected benchmarks by up to 86.7% and 88.1%, respectively. A prototype using carrier-grade and open-source 5G networks is further developed and evaluated with a moving developer-mode vehicle used for Vehicle-to-Everything (V2X) Research. Experimental results show an almost three-fold reduction in both latency and power consumption while proving the applicability of the proposed architecture for the considered case.

Beyond 5G and emerging 6G networks have the potential to revolutionize medical services by enabling ultra-reliable, low-latency communication, AI-driven edge intelligence, and dynamic network adaptation for real-time healthcare applications. The Drone Care Angel is an AI-driven context-aware mobile health monitoring system that utilizes Unmanned Aerial Vehicles to provide real-time surveillance and emergency detection. Equipped with high-resolution cameras, the UAV continuously monitors individuals, analyzing physiological and movement patterns to detect potential health anomalies. By integrating edge computing and AI-based analytics, DCA enables rapid anomaly detection, allowing for timely medical intervention and improved remote decision-making. To achieve its objectives, DCA leverages B5G capabilities, including Multi-access Edge Computing for low-latency AI inference and dynamic network slicing for adaptive low-latency communication to ensure seamless data transmission.

Autonomous mobility, particularly in regards to self-driving vehicular connectivity, has stringent service level agreements (SLA) that require a stable 5G connection with good quality-of-service (QoS), even when the user is on the move, alongside a significant cooperation with the environment. The dynamic nature of vehicular networks, in which the terminal device is constantly moving, leads to the occurrence of potentially problematic handovers, such as leaving a public 5G network and entering a private one. There is still a lack of integrated solutions that consider both latency requirements and the spatial context of the user. Handover decisions in current deployments are typically reactive and have undergone minimal evolutions since 2G, as they are still mostly based on radio signal strength indicators such as the Reference Signal Received Power (RSRP) or SignalNoise Ratio (SNR). While this scheme is easy to implement, these metrics are not appropriate for the SLAs of dynamic mobility scenarios. In these cases, radio signal quality does not directly reflect the end-to-end latency experienced by the application, and poorly timed handovers can result in significant latency spikes, which often violate application-level SLAs, especially in Ultra-Reliable Low-Latency Communication (URLLC) use cases. To address these challenges, we propose a demo of a statistical location-aware layer that refines 5G vehicular handover decisions by integrating a mechanism that optimizes network selection with minimal overhead, utilizing real-time geolocation, rule-based constraints, and a predictive Round-Trip Time (RTT) function.

Multi-Access Edge Computing (MEC) is a promising paradigm that brings computational capabilities closer to end-users, enabling low-latency and high-bandwidth applications. The federation of multiple MEC platforms has the potential to create a collaborative ecosystem, facilitating resource sharing and scalability. This paper addresses the benefits of exploring the synergies between Software-Defined Networking (SDN) and MEC federation deployments, aiming to enhance the network infrastructure’s overall performance, flexibility, and responsiveness. More concretely, this work explores the integration of MEC and SDN to address performance and resource utilization challenges in congested federated MEC environments, enabling seamless service migration between MEC nodes when resource constraints arise with results showing its ability to maintain service quality under congestion.

This article presents a standards-based architecture for V2X communication using CoAP and EdgeX Foundry. By mapping ETSI ITS messages to CoAP, the system enables efficient risk alerting at the edge. An inference module illustrates the architecture's extensibility, and the simulation confirms responsiveness and scalability under mixed traffic conditions.

Integration of blockchain and digital twin technologies has proven to offer distributed and efficient solutions for addressing edge computing task scheduling. However, limited research has comprehensively addressed the modeling, optimization, and analysis of latency and energy consumption within such integrated frameworks. To fill this gap, this paper presents a generic architecture that combines blockchain and digital twin layers to facilitate edge computing task scheduling. A system model is developed across all layers, from which a multi-objective optimization problem is formulated to minimize both overall latency and energy consumption. Furthermore, the feasibility of this approach is demonstrated through three representative task scheduling algorithms—Mixed Integer Linear Programming (MILP), First Come First Serve (FCFS), and Shortest Job First (SJF)—achieving latency reductions of up to 55.43 ms and energy consumption as low as 0.296 J.

The sixth-generation of mobile networks (6G), even at an early stage, is promising increased performance and the ability to accommodate new applications such as enhanced virtual and augmented reality in mobile environments. Due to the heavy computational load of such applications, these are split, moving the heavy processing to Multi-access Edge Computing (MEC) datacenters closer to the end-users. However, as datacenters consume a considerable amount of power, the deployment of computational tasks must take into account the energy expenditure of each MEC and its underlying hardware. This paper presents a framework that leverages the concepts of MEC, Federation and Software-defined Networking (SDN) to enable an energy-aware service migration for Augmented Reality (AR) services in tourism applications, taking into consideration the varying power efficiencies of hardware in a federated MEC environment. The framework was implemented and tested in a carrier graded 5G network with results showing a 55% reduction in energy consumption while maintaining an acceptable quality of experience for the user.

This paper investigates the development of a solution for Edge Internet Traffic Steering (EITS), focusing on optimizing the performance of services at the network edge using Computing-Aware Traffic Steering (CATS) principles. Motivated by the increasing demand for low-latency connectivity, high bandwidth, resource availability, link redundancy, and efficient memory and CPU usage optimization, this research addresses the limitations of existing traffic steering approaches in meeting the static or dynamic requirements of edge services. The proposed solution leverages network and compute metrics to direct traffic to the most suitable service instance, enhancing overall performance through the use of OpenAirinterface SG Core Network. The practical implementation of a video streaming use case demonstrated the efficacy of the solution.

Multi-access Edge Computing (MEC) enhances 5G services by enabling low-latency edge-based processing. However, isolated deployments limit its scalability and resource efficiency. To address this, we propose a federation-aware MEC architecture that supports cross-domain service migration through the introduction of a MEC Federator and a Metrics Forwarder with fine-grained, policy-driven orchestration mechanisms. We implemented and validated this solution by extending an existing open-source MEC-NFV platform with the proposed architecture, enabling the management of heterogeneous administrative domains. Evaluation shows that, despite added orchestration overhead, the system maintains service continuity across federated domains.

The advancements in wireless networking technologies empower new opportunities and applications. However, these advanced networks are increasingly consuming more energy to provide higher performance. In line with the United Nations sustainable development goals and the need to reduce networking energy consumption, this paper presents an efficient dynamic slicing and antenna control architecture for 5G networks. The architecture was then applied to a smart port scenario, where a pre-gate is used to control the entrance of trucks into the port with the aid of a camera, with the video stream being analyzed in a datacenter to detect the presence of a truck and access its details. Experimental results showed that the architecture was able to potentially reduce the energy expenditure of the considered scenario in 2.26 MWh in a year, considering a real statistical number of trucks entering the Portuguese Sines Port in 2017. Moreover, it reduced the energy consumption of the User Equipment, computing infrastructure, and wireless network in 9%, 23% and 14% respectively.

Vehicle-to-Everything (V2X) communications are constrained by both 3GPP technical specifications, as well as by country-specific spectrum regulations. The world's largest economies, such as the USA, EU and China have self-imposed regulations regarding the specific bandwidths and central spectrum frequencies where both safety and non-safety related V2X communication services are allowed to occur (always aligned with the aforementioned 3GPP technical specifications). Although the channels used for safety, non-safety, and control packets differ, what all of these countries have in common is that V2X shall occur mostly on New Radio Unlicensed (NR-U) spectrum, i.e., by means of private networks. A specific bandwidth in the public spectrum is also available, but since public spectrum is purchased through auctions, it is quite common the case that one particular operator will own the entirety of this spectrum, leading to a monopoly in V2X operations. Besides, this public spectrum is quite limited in bandwidth. This of course includes all of the Intelligent Transportation Systems (ITS) services, even location-based services, such as the ones that require the usage of positioning technologies, like autonomous vehicles, that require said services in order to support complex maneuvers and cooperative driving. Global Navigation Satellite Systems (GNSS) such as GPS or Galileo, currently already offer high-accuracy location to vehicles. However, this form of stand-alone position estimation of the vehicle has several drawbacks, as the information is constrained to the individual vehicle and not shared with others in a secure manner. This exchange of position information between other entities (not only vehicles, but also other infrastructure nodes) is vital for actions such as cooperative maneuvers and to counter loss of satellite sight (e.g., when entering a tunnel). Taking these facts into consideration, it is therefore expected that in the mid to long-term, municipalities and highways will possess dedicated private 5G networks for V2X operations with the aim of offering a plethora of vehicular services, including positioning ones. Since the existent scientific literature lacks an integrated analysis of precise positioning services for ITS in 5G private networks, we propose in this paper, to provide a comprehensive review connecting these diverse elements, examining the role of 5G private networks in transmitting positioning messages in V2X scenarios. Additionally, the paper shall explore hybrid positioning systems that combine 5G and GNSS technologies, illustrating their potential to enhance V2X communications. This study offers a roadmap for the evolution of ITS and V2X communications by showcasing current trends and identifying areas for further research.