As wireless communication continues to advance, the move towards Sixth-Generation (6G) networks has heightened the need for faster data speeds and reliable connections, prompting new approaches to connectivity. In scenarios such as natural disasters, where Ground Base Stations (GBSs) may be compromised, the use of Unmanned Aerial Vehicles (UAVs) has become increasingly important. A promising approach is to deploy low-altitude UAVs equipped with compact Base Stations (BSs) to reestablish essential communication networks and offer temporary coverage. However, identifying the optimal locations for these UAV-BSs presents a complex challenge. This paper proposes an innovative solution using UAVs as base stations (UAV-BSs) and introduces a Mixed-Integer Non-Linear Programming (MINLP) optimization model to position UAV-BSs based on real-time demand and network conditions. Traditional methods struggle with the complexity of UAV-BS deployment, so a novel algorithm combining the JAYA optimization technique is used. Extensive experiments show this approach maximizes network coverage and connectivity while minimizing UAV-BS power consumption, outperforming other methods in placement accuracy, power usage, packet loss, and latency. The algorithm also adapts to varying network conditions, making it a valuable tool for optimizing UAV-BS locations in dynamic environments. © 2025 The Authors

Objective: This study aims to comprehensively review the use of graph neural networks (GNNs) for clinical risk prediction based on electronic health records (EHRs). The primary goal is to provide an overview of the state-of-the-art of this subject, highlighting ongoing research efforts and identifying existing challenges in developing effective GNNs for improved prediction of clinical risks. Methods: A search was conducted in the Scopus, PubMed, ACM Digital Library, and Embase databases to identify relevant English-language papers that used GNNs for clinical risk prediction based on EHR data. The study includes original research papers published between January 2009 and May 2023. Results: Following the initial screening process, 50 articles were included in the data collection. A significant increase in publications from 2020 was observed, with most selected papers focusing on diagnosis prediction (n = 36). The study revealed that the graph attention network (GAT) (n = 19) was the most prevalent architecture, and MIMIC-III (n = 23) was the most common data resource. Conclusion: GNNs are relevant tools for predicting clinical risk by accounting for the relational aspects among medical events and entities and managing large volumes of EHR data. Future studies in this area may address challenges such as EHR data heterogeneity, multimodality, and model interpretability, aiming to develop more holistic GNN models that can produce more accurate predictions, be effectively implemented in clinical settings, and ultimately improve patient care. © 2024 The Authors

While machine learning applications in IoT devices are getting more widespread, the computational and power limitations of these devices pose a great challenge. To handle this increasing computational burden, edge, and cloud solutions emerge as a means to offload computation to more powerful devices. However, the unstable nature of network connections constantly changes the communication costs, making the offload process (i.e., when and where to transfer data) a dynamic trade-off. In this work, we propose DECOS: a framework to automatically select at run-time the best offloading solution with minimum latency based on the computational capabilities of devices and network status at a given moment. We use heterogeneous devices for edge and Cloud nodes to evaluate the framework's performance using MobileNetV1 CNN and network traffic data from a real-world 4G bandwidth dataset. DECOS effectively selects the best processing node to maintain the minimum possible latency, reducing it up to 29% compared to Cloud-exclusive processing while reducing the energy consumption by 1.9times compared to IoT-exclusive execution. © 2023 IEEE.

This paper delves into the utilization of Vehicular Ad-hoc Networks (VANETs) in emergency vehicle warning systems in the era of 6G. The proposed system, named SafeSmart 6G, will leverage VANET-based vehicle-to-infrastructure Communication powered by 6G to exchange data between traffic lights and emergency vehicles, enhancing safety and reducing response times. SafeSmart 6G will predict the arrival time of emergency vehicles at intersections using historical data and AI-driven analytics, requesting signal preemption for the chosen route. The paper discusses the potential benefits and challenges that might arise from the use of 6G in emergency scenarios. © 2023 IEEE.

An important use case for Vehicular Ad-hoc Networks (VANETs) is its application in the warning systems of emergency vehicles (EV). VANET-based vehicle-to-infrastructure (V2I) communication can be used to exchange important data and information between traffic lights and EVs, by means of transceivers at both ends. This communication helps in reducing the risks of accidents and also saves valuable time through an optimized orchestration of the traffic lights. This paper outlines the system design of an EV warning system that makes use of V2I communication. The system has been extensively studied in state-of-the-art simulators, such as SUMO and OMNeT++, in a huge variety of scenarios, where metrics for both time and safety have been collected. The results show that SafeSmart is highly effective in reducing trip times as well as increasing the overall safety of EVs in emergency scenarios. © 2013 IEEE.

One important use case for Vehicular Ad-hoc Networks (VANETs) are applications related to emergency vehicles (EV). V2I (Vehicle-to-Infrastructure) communication can provide the infrastructure and protocol stack necessary to establish a communication channel between the transceivers in the EVs and the ones in the traffic lights, reducing accident risks and also help save valuable time. This paper outlines the system design of an EV warning system that makes use of V2I communication. A prototype of the system has been tested in a traffic simulation environment including EVs and traffic lights. To evaluate the system we performed a simulation and conducted a performance comparison between the travel times for EVs in normal traffic and when the system is in use. © 2020 IFIP.

Bluetooth Low Energy (BLE) is one of the most important technologies that feed the growing field of Internet of Things and Wireless Sensor Networks. Due to its flexibility and unique low power-consumption, an increasing number of industrial devices, household appliances and wearables are being designed using it. However, the real-time demands of these networks such as timing and Quality of Service are not fully covered by the protocol itself. To help improve and offer some control over these characteristics, this paper presents a time slot transmission scheme with packet prioritization. It is based on the division and allocation of the connection interval to two types of messages: real-time and ordinary. The goal is to offer the lowest packet loss and time guarantees for real-time messages, while providing acceptable throughput for ordinary ones. Since the probability of a BLE connection to close increases with the number of packets sent through it, the position where a real-time packet is being sent as well as the number of ordinary messages in a connection represent key factors. The use of the first and last slot for real-time packets with ordinary flow restricted to the space between them decreases the transmission delay uncertainty and allows probability tuning based on the number of ordinary messages. Simulations were performed using the proposed scheme and a reduction of more than 100 times in the delay variance was observed for real-time transmissions. Regarding reliability, around 5% of the packets were lost for a bit error rate of 10⁻³. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.

Situation awareness in surveillance systems benefits from high-quality video streaming service. This is even more important considering military systems, in which delays in image transmission may have a significant impact on the decision-making process. However, in order to deliver high-quality video streaming service, the required network infrastructure may be prohibitively complex, or even completely impossible to deploy, if mobile data providers are considered. Moreover, the demand for high network throughput poses extra requirements on the network. Considering this context, this paper addresses the problem of highly mobile networks composed of unmanned aerial vehicles (UAVs) as data providers of a military surveillance system. The proposed approach to tackle the problem is based on a Software Defined Networking (SDN) approach aiming at providing the best routes to deliver the data, enhancing the end-user quality of experience. An extensive experimental campaign was performed by means of simulations and the acquired results provide solid evidence of the usefulness of this proposal.© 2018 Iulisloi Zacarias et al.

Important signal processing techniques need that the response of the different elements of a sensor array has specific characteristics. For physical systems this often is not achievable as the array elements’ responses are affected by mutual coupling or other effects. In such cases, it is necessary to apply array interpolation to allow the application of ESPRIT, Forward Backward Averaging (FBA), and Spatial Smoothing (SPS). Array interpolation provides a model or transformation between the true and a desired array response. If the true response of the array becomes more distorted with respect to the desired one or the considered region of the field of view of the array increases, nonlinear approaches becomes necessary. This work presents two novel methods for sector discretization. An Unscented Transform (UT) based method and a principal component analysis (PCA) based method are discussed. Additionally, two novel nonlinear interpolation methods are developed based on the nonlinear regression schemes Multivariate Adaptive Regression Splines (MARS) and Generalized Regression Neural Networks (GRNNs). These schemes are extended and applied to the array interpolation problem. The performance of the proposed methods is examined using simulated and measured array responses of a physical system used for research on mutual coupling in antenna arrays. © 2017 The Author(s). Published by Elsevier B.V.

The envisaged usage of multiple Unmanned Aerial Vehicles (UAVs) to perform cooperative tasks is a promising concept for future autonomous military systems. An important aspect to make this usage a reality is the solution of the task allocation problem in these cooperative systems. This paper addresses the problem of tasks allocation among agents representing UAVs, considering that the tasks are created by a central entity, in which the decision of which task will be performed by each agent is not decided by this central entity, but by the agents themselves. The assumption that tasks are created by a central entity is a reasonable one, given the way strategic planning is carried up in military operations. To enable the UAVs to have the ability to decide which tasks to perform, concepts from swarm intelligence and multi-agent system approach are used. Heuristic methods are commonly used to solve this problem, but they present drawbacks. For example, many tasks end up not begin performed even if the UAVs have enough resources to execute them. To cope with this problem, this paper proposes three algorithm variants that complement each other to form a new method aiming to increase the amount of performed tasks, so that a better task allocation is achieved. Through experiments in a simulated environment, the proposed method was evaluated, yielding enhanced results for the addressed problem compared to existing methods reported in the literature. © 2018 Elsevier Ltd