Europe prioritizes energy efficiency for sustainable development and climate action, with buildings consuming 40% of the EU’s energy and contributing over 30% of CO 2 emissions. To address this, the EU implements policies targeting energy efficiency across sectors like industry, transportation, and district heating. The Energy Efficiency Directive mandates reductions in energy consumption, promoting measures such as energy audits and performance standards. Initiatives like the European Green Deal and Renovation Wave Strategy underscore the importance of energy efficiency in achieving carbon neutrality. District heating systems play a vital role in this strategy, offering Efficient heating solutions for urban areas, and optimizing their operation is a key focus of research. Techniques include mathematical modeling, advanced control strategies, and integration of renewable energy sources. This work focuses on the application of a digital twin to model the district heating network combined with prediction tools to enhance system efficiency. A procedure to integrate physics (white-box) and data-driven (black-box) approaches to foster better-informed decisions in the optimal energy resources management is presented.

This paper introduces a novel dynamic multipath routing, modulation level, spatial and spectrum assignment algorithm for space division multiplexing (SDM) enabled elastic optical networks (EON) with the aim of minimizing the blocking probability and the energy consumed by bandwidth variable transponders (BVTs). The adopted multipath routing strategy allows the splitting of the demand into several sublightpaths using different fiber cores but ensuring that all of them utilize the same set of fibers in order to avoid differential delay. The method also imposes continuity constraints in both spectrum and core location in order to use cost-effective SDM Reconfigurable Optical Add and Drop Multiplexers (ROADMs) without lane change support. The complete usage of multi-core fibers (MCFs) in this kind of networks is restricted due to inter-core crosstalk (XT), which can reduce the quality of received signals. Therefore, the method besides using the most effective modulation format, also ensures that the XT of the lightpaths (or sublightpaths) does not exceed the threshold for each modulation format. A simulation study comparing our method with another similar proposal from the literature is presented for different types of topologies in terms of link distances. Simulation results demonstrate that the proposed multipath routing algorithm in networks including links close to or beyond 1000 kms significantly boost the dynamic performance in terms of blocking probability, energy consumption, and latency.

In recent years, the number of embedded computing devices connected to the Internet has exponentially increased. At the same time, new applications are becoming more complex and computationally demanding, which can be a problem for devices, especially when they are battery powered. In this context, the concepts of computation offloading and edge computing, which allow applications to be fully or partially offloaded and executed on servers close to the devices in the network, have arisen and received increasing attention. Then, the design of algorithms to make the decision of which applications or tasks should be offloaded, and where to execute them, is crucial. One of the options that has been gaining momentum lately is the use of Reinforcement Learning (RL) and, in particular, Deep Reinforcement Learning (DRL), which enables learning optimal or near-optimal offloading policies adapted to each particular scenario. Although the use of RL techniques to solve the computation offloading problem in edge systems has been covered by some surveys, it has been done in a limited way. For example, some surveys have analysed the use of RL to solve various networking problems, with computation offloading being one of them, but not the primary focus. Other surveys, on the other hand, have reviewed techniques to solve the computation offloading problem, being RL just one of the approaches considered. To the best of our knowledge, this is the first survey that specifically focuses on the use of RL and DRL techniques for computation offloading in edge computing system. We present a comprehensive and detailed survey, where we analyse and classify the research papers in terms of use cases, network and edge computing architectures, objectives, RL algorithms, decision-making approaches, and time-varying characteristics considered in the analysed scenarios. In particular, we include a series of tables to help researchers identify relevant papers based on specific features, and analyse which scenarios and techniques are most frequently considered in the literature. Finally, this survey identifies a number of research challenges, future directions and areas for further study.

Mobile technologies have undergone a great leap forward in a few years, and while 5G networks are already being deployed, there are not yet many proven applications that can fully utilize the advantages of this new technology. Connected and autonomous vehicles are a specific and demanding case, particularly in terms of delay and bandwidth requirements, which can leverage not only 5G but also edge computing technologies. Therefore, the development of testbeds to demonstrate future applications is crucial to enable the full deployment of 5G and edge computing possibilities. In this paper, we present a flexible and modular testbed, targeted towards the evaluation of Cooperative, Connected, and Automated Mobility (CCAM) applications, and we demonstrate a use case (using a 4G system) where an autonomous vehicle offloads processing tasks to an edge server which analyzes images, makes routing decisions, and sends guidance commands back to the vehicle, thus proving the possibilities of edge computing and wireless technologies.

A novel quality of transmission (QoT) estimator based on support vector machines (SVM) is proposed for classifying optical connections (lightpaths) into high or low quality categories in impairment-aware wavelength-routed optical networks (WRONs). The performance of the SVM-based estimator is evaluated in a long haul communications network and compared to previous semi-analytical and cognitive proposals. Results show that the SVM approach significantly reduces the necessary computing time to estimate the QoT of a given lightpath, critical aspect of design in these networks, and even slightly improves accuracy.

The promises of SDN and NFV technologies to boost innovation and to reduce the time-to-market of new services is changing the way in which residential networks will be deployed, managed and maintained in the near future. New user-centric management models for residential networks combining SDN-based residential gateways and cloud technologies have already been proposed, providing flexibility and ease of deployment. Extending the scope of SDN technologies to optical access networks and bringing cloud technologies to the edge of the network enable the creation of advanced residential networks in which complex service function chains can be established to provide traffic differentiation. In this context, this paper defines a novel network management model based on a user-centric approach that allows residential users to define and control access network resources and the dynamic provision of traffic differentiation to fulfill QoS requirements.

An emulation study has been carried to demonstrate the benefit of a proactive restoration technique in cognitive heterogeneous optical networks. Results show the advantages of that method in terms of recovery percentage and disruption time.

Communication networks are facing continuous variations of traffic patterns as well as occasional failures of network equipment. Wavelength-routed optical networks (WRONs) offer the possibility of dynamically adapting to traffic conditions by means of reconfiguring the logical topology, that is, the set of lightpaths embedded in it. However, reconfiguration has a cost, the number of packets lost during the reconfiguration process. Hence, it is necessary to use efficient reconfiguration policies and algorithms to design the logical topologies. In this paper, a new algorithm is proposed to design logical topologies that jointly minimizes the number of lightpaths changed (reconfiguration cost) and the network congestion (reconfiguration reward). This method is based on the combination of genetic algorithms with Pareto optimality techniques. Thus, the algorithm provides a set of optimal (or near-optimal) solutions in terms of both parameters, the Pareto optimal set. Moreover, a novel policy to minimize the packet loss ratio considering all the solutions provided by the algorithm is also proposed. A simulation study is presented to show how the combination of the new algorithm and policy can reduce in more than one order of magnitude the packet loss ratio in stationary state and respond to abrupt changes in less time when compared with previous work on logical topology reconfiguration.