As natural disasters become more frequent and severe, ensuring a resilient communications infrastructure is of paramount importance for effective disaster response and recovery. This disaster-resilient infrastructure should also respond to sustainability goals by providing an energy-efficient and economically feasible network that is accessible to everyone. To this end, this paper provides a comprehensive exploration of the technological solutions and strategies necessary to build and maintain resilient communications networks that can withstand and quickly recover fromdisaster scenarios. The paper starts with a survey of existing literature and related reviews to establish a solid foundation, followed by an overview of the global landscape of disaster communications and power supply management. We then introduce the key enablers of communications and energy resource technologies to support communications infrastructure, examining emerging trends that improve the resilience of these systems. Pre-disaster planning is emphasized as a critical phase where proactive communication and energy supply strategies can significantly mitigate the impact of disasters.We also explore the essential technologies for disaster response, focusing on real-time communications and energy solutions that support rapid deployment and coordination in times of crisis.The paper then presents post-disaster communication and energy management planning for effective rescue and evacuation operations. The main findings derived from the comprehensive survey are also summarized for each disaster phase. This is followed by an analysis of existing vendor products and services as well as standardization efforts and ongoing projects that contribute to the development of resilient infrastructures. A detailed case study of the Turkiye earthquakes is presented to illustrate the practical application of these technologies and strategies. Finally,we address the open issues and challenges in realizing sustainable and resilient communication infrastructures and provide insights into future research directions. By incorporating lessons learned from various disaster scenarios, this paper presents strategic recommendations that enhance the resilience and adaptability of communication systems in the context of disaster relief and management.

IEEE 802.11 (Wi-Fi) is one of the technologies that provides high performance with a high density of connected devices to support emerging demanding services, such as virtual and augmented reality. However, in highly dense deployments, Wi-Fi performance is severely affected by interference. This problem is even worse in new standards, such as 802.11n/ac, where new features such as Channel Bonding (CB) are introduced to increase network capacity but at the cost of using wider spectrum channels. Finding the best channel assignment in dense deployments under dynamic environments with CB is challenging, given its combinatorial nature. Therefore, the use of analytical or system models to predict Wi-Fi performance after potential changes (e.g., dynamic channel selection with CB, and the deployment of new devices) are not suitable, due to either low accuracy or high computational cost. This paper presents a novel, data-driven approach to speed up this process, using a Graph Neural Network (GNN) model that exploits the information carried in the deployment’s topology and the intricate wireless interactions to predict Wi-Fi performance with high accuracy. The evaluation results show that preserving the graph structure in the learning process obtains a 64% increase versus a naive approach, and around 55% compared to other Machine Learning (ML) approaches when using all training features.

Traffic Classification (TC) systems allow inferring the application that is generating the traffic being analyzed. State-of-the-art TC algorithms are based on Deep Learning (DL) and have outperformed traditional methods in complex and modern scenarios, even if traffic is encrypted. Most of the works on TC assume the traffic flows on a wired network under the same network management domain. This assumption limits the capabilities of TC systems in wireless networks since users’ traffic on one network domain can be negatively impacted by undetected users’ traffic from other network domains or detected ones but with no traffic context in a shared spectrum. To solve this problem, we introduce a novel framework to achieve TC at any layer on the radio network stack. We propose a spectrum-based procedure that uses a DL-based classifier to realize this framework. We design two DL-based classifiers, a novel Convolutional Neural Network (CNN) spectrum-based TC and a Recurrent Neural Networks (RNN) as baseline architecture, and benchmark their performance on three TC tasks at different radio stack layers. The datasets were generated by combining packet traces from real transmissions with an 802.11 standardcompliant waveform generator. Performance evaluations show that the best model can achieve an accuracy above 92% in the most demanding TC task, a drop of only 4.37% in accuracy compared to a byte-based DL approach, with micro-second perpacket prediction time, which is very promising for delivering real-time spectrum-based traffic analyzers.

Many Internet of Things (IoT) applications require a distributed architecture for decision making: either because of a lack of a centralized system, failure-prone connectivity to a centralized system, or because the imposed latency to contact such a system is too high for real-time applications. Often, these IoT applications fall in the domain of Reinforcement Learning (RL), e.g., autonomous robot navigation in Smart Factories and Traffic Signal Control in Smart Cities. However, RL-based applications require a long learning time. To overcome this limitation and scale with the number of agents, Parallel Reinforcement Learning (PRL) algorithms run multiple RL agents in parallel and on distributed environments. However, deploying PRL algorithms in such environments entails a communication overhead that increases the (actual) execution time. State-of-the-art PRL algorithms are designed for reducing the learning time while assuming no (or limited) communication overhead. In this work, we present a novel partitioning algorithm that minimizes the communication overhead in PRL running on IoT environments. To the best of our knowledge, this is the first work that focuses on solving the communication overhead of distributing PRL algorithms algorithm without requiring any a priori knowledge about the structure of the problem. The proposed algorithm intelligently combines a dynamic state partitioning strategy, which exploits the agent’s exploration capabilities to build partition knowledge while learning, with an efficient mapping of agents to partitions, which reduces the communication among agents. Performance evaluations show that the proposed algorithm can achieve almost no communication among PRL agents at the converged state.

In the last decade, several routing algorithms have been proposed in ad hoc wireless networks. However, most of them require either a high bandwidth, to maintain a full routing table, or suffer a high delay with packet flooding over the network, when the routes are discovered on-demand. As a solution, hybrid approaches, i.e. algorithms that combine on-demand route discovery with proactive updates of the available routes, have shown a good trade-off between low communication overhead and quality of the found routes. One of the approaches used in hybrid algorithms is Multi-Agent Reinforcement Learning (MARL), where the routing problem is addressed as a complex distributed control and learning problem. However, state-of-the-art MARL routing algorithms suffer from some limitations such as either lack of exploration or exploration at the cost of a high communication overhead, slow convergence under network dynamics, or no support for Quality of Service (QoS). In order to overcome such limitations, in this paper, we propose the Q ² -Routing algorithm, which merges existing techniques in wireless routing and enhances them by using techniques from the MARL domain. Simulation results showed that the proposed algorithm is able to outperform well-known ad-hoc routing algorithms in dynamic environments under QoS constraints.

This paper presents the architecture and the basic principles behind the design and implementation of the SCATTER system, a wireless end-to-end communication system that participated in the DARPA Second Spectrum Collaboration Challenge (SC2). The focus is mainly on presenting the architecture and the supported interactions between the different components of the system in order to deliver a true dynamic collaborative spectrum allocation and usage, while coexisting with numerous unknown heterogeneous wireless technologies.