Within the paradigm of smart cities, smart devices can be considered as a tool to enhance safety. Edge sensing, Internet of Things (IoT), big data, social media analytics, edge computing, and artificial intelligence are key technologies that can be applied through smart devices to create emergency-aware systems. The use of these technologies could make emergency management tasks such as visualizing, analyzing, and predicting disasters easier to perform. The aim of this paper is to conduct a review of recent activities in literature about disaster and emergency management, and showing the role of different edge technologies used in this regard, and through the different stages of dealing with a disaster situation. Special importance is given to two main technologies: Social media analytics and artificial intelligence, due to their exceptional impact on emergency situations. Social media represents a rich source of data while artificial intelligence stands out as the mechanism to deal with the huge amount of data generated by smart devices, and thus needed to tackle all sources of data, in order to predict, detect, manage information, and for authorities to respond to emergency situations. This survey is a comprehensive review for the recent literature on the related topics, providing the reader with a clear overview of the current status and classifying the papers into groups with relations among them. The structuring of the recent literature into four phases makes it easier for the reader to realize the current state of the art. For completeness, this survey ends with a section on open issues and research trends in disaster and emergency management systems.

Major research challenges in the next generation (4G) of wireless networks include the provisioning of worldwide seamless mobility across heterogeneous wireless networks, the improvement of end-to-end Quality of Service (QoS) and enabling users to specify their personal preferences. Under this motivation, we design a novel cross-layer architecture that provides context-awareness, smart handoff and mobility control in heterogeneous wireless IP networks. We develop a Transport and Application Layer Architecture for vertical Mobility with Context-awareness (Tramcar). Tramcar is tailored for a variety of different network technologies with different characteristics and has the ability of adapting to changing environment conditions and unpredictable background traffic. Furthermore, Tramcar allows users to identify and prioritize their preferences. Simulation results demonstrate that Tramcar increases user satisfaction levels and network throughput under rough network conditions and reduces overall handoff latencies.

The open radio access network (O-RAN) architecture supports intelligent network control algorithms as one of its core capabilities. Data-driven applications incorporate such algorithms to optimize radio access network (RAN) functions via RAN intelligent controllers (RICs). Deep reinforcement learning (DRL) algorithms are among the main approaches adopted in the O-RAN literature to solve dynamic radio resource management problems. However, despite the benefits introduced by the O-RAN RICs, the practical adoption of DRL algorithms in real network deployments falls behind. This is primarily due to the slow convergence and unstable performance exhibited by DRL agents upon deployment and when encountering previously unseen network conditions. In this paper, we address these challenges by proposing transfer learning (TL) as a core component of the training and deployment workflows for the DRL-based closed-loop control of O-RAN functionalities. To this end, we propose and design a hybrid TL-aided approach that leverages the advantages of both policy reuse and distillation TL methods to provide safe and accelerated convergence in DRL-based O-RAN slicing. We conduct a thorough experiment that accommodates multiple services, including real VR gaming traffic to reflect practical scenarios of O-RAN slicing. We also propose and implement policy reuse and distillation-aided DRL and non-TL-aided DRL as three separate baselines. The proposed hybrid approach shows at least: 7.7% and 20.7% improvements in the average initial reward value and the percentage of converged scenarios, and a 64.6% decrease in reward variance while maintaining fast convergence and enhancing the generalizability compared with the baselines.

Social media traffic constitutes the highest percentage of Internet traffic. Such traffic is largely facilitated by mobile devices, which imposes a huge traffic load on backhaul links in 5G networks, and can in turn affect the quality of service. This traffic load can be alleviated by using vehicular networks as a traffic offloading platform. In particular, vehicles can act as a resourceful asset for edge caching, thus enabling data acquisition from nearby caching nodes rather than the remote backhaul servers. In this paper, we propose the Predictive Proactive Caching Framework (PPCF), which exploits the daily driving routine and predictable behavior of users to pre-cache the data at parked vehicles for the requesters to proactively acquire as they pass by. PPCF is composed of a cache placement module and a prediction module. The former aims at maximizing cache hits by assigning replicas to caching spots that yield maximum certainty in their spatiotemporal availability for requesters. To estimate such availability, the prediction module aims at making accurate travel time predictions by proposing the use of a Long Short-Term Memory (LSTM) network trained using particle swarm optimization (PSO-LSTM). The predicted average travel time is then used to estimate a personalized travel time for users by considering their different driving behaviors. Extensive simulations demonstrate the ability of the proposed framework to achieve significant improvements in its targeted objectives compared to other prominent caching and prediction schemes in vehicular networks.