Today’s wireless communication networks are very reliable. However, in case of a disaster, these networks can be overwhelmed by a tremendous amount of requests which they can not cope with. We propose a deployment tool for UAV (unmanned aerial vehicle)-aided emergency networks for such disaster scenarios. By using UAVs, femtocell base stations will be brought to and hovered at their assigned location. We applied this deployment tool on a realistic disaster scenario in the city center of Ghent, Belgium. The results are very promising although a large amount of drones (> 1000 type 1 or > 370 type 2 drones) is required to provide full coverage for 1 h. Halving the user coverage results in 1.8 to 2 times less drones. More effectively is to increase the drone’s fly height. A 10-m higher fly height can result in a reduction up to 13%. However, above 100 m, the influence is not significant any more. Decreasing the user’s service level has no significant influence on the number of required drones for the considered scenario. Furthermore, a prediction model for the number of required drones based on the intervention duration and the user coverage is proposed.

The rapid development of the number of wireless broadband devices requires that the induced uplink exposure be addressed during the design of the future wireless networks, in addition to the downlink exposure due to the transmission of the base stations. In this paper, the positions and power levels of massive MIMO-LTE (Multiple Input Multiple Output-Long Term Evolution) base stations are optimized towards low power consumption, low downlink and uplink electromagnetic exposure and maximal user coverage. A suburban area in Ghent, Belgium has been considered. The results show that the higher the number of BS antenna elements, the fewer number of BSs the massive MIMO network requires. This leads to a decrease of the downlink exposure (−12% for the electric field and −32% for the downlink dose) and an increase of the uplink exposure (+70% for the uplink dose), whereas both downlink and uplink exposure increase with the number of simultaneous served users (+174% for the electric field and +22% for the uplink SAR). The optimal massive MIMO network presenting the better trade-off between the power consumption, the total dose and the user coverage has been obtained with 37 64-antenna BSs. Moreover, the level of the downlink electromagnetic exposure (electric field) of the massive MIMO network is 5 times lower than the 4G reference scenario.

This study consists of a systematic literature review that aims to explore the potential of integrating wearable sensor data and machine learning (ML) techniques for predicting stress episodes. It aims to identify prevalent sensors, key physiological features, and the effectiveness of ML methods in real-world stress monitoring and prediction. This systematic review follows the PRISMA methodology, analyzing literature from January 2010 to June 2025. Data sources included IEEE Xplore, Elsevier, Springer, Multidisciplinary Digital Publishing Institute (MDPI), and paper repositories such as PubMed Central and the Association of Computing Machinery (ACM). The inclusion criteria encompassed studies that employed wearable devices for ML stress prediction, focusing on physiological data such as heart rate variability (HRV), skin conductance, and sleep patterns. Articles were screened for originality, clinical relevance, and methodological rigor. Key findings highlighted the use of diverse wearable sensors, including electrodermal activity (EDA), photoplethysmography (PPG), and accelerometers. Commonly extracted features included HRV metrics, skin conductance levels, and respiratory patterns. ML models, such as Random Forest (RF), Support Vector Machines (SVM), and deep neural networks (DNN), have demonstrated high predictive accuracy (e.g., up to 99%). Despite promising results, challenges such as small sample sizes, variability in data quality, and the need for standardized protocols were noted. Wearable sensors combined with ML algorithms provide scalable, real-time stress monitoring solutions, emphasizing proactive healthcare management. However, advancing this field requires addressing limitations through interdisciplinary collaboration and focusing on the accessibility and usability of technologies. This study highlights the transformative role of wearable technologies in predicting stress, with implications for personalized health interventions, mental health support, and enhanced healthcare efficiency.

Today’s wireless networks provide us reliable connectivity. However, if a disaster occurs, the whole network could be out of service and people cannot communicate. Using a fast deployable temporally network by mounting small cell base stations on unmanned aerial vehicles (UAVs) could solve the problem. Yet, this raises several challenges. We propose a capacity-deployment tool to design the backhaul network for UAV-aided networks and to evaluate the performance of the backhaul network in a realistic scenario in the city center of Ghent, Belgium. This tool assigns simultaneously resources to the ground users—access network—and to the backhaul network, taking into consideration backhaul capacity and power restrictions. We compare three types of backhaul scenarios using a 3.5 GHz link, 3.5 GHz with carrier aggregation (CA) and the 60 GHz band, considering three different types of drones. The results showed that an optimal UAV flight height (80 m) could satisfy both access and backhaul networks; however, full coverage was difficult to achieve. Finally, we discuss the influence of the flight height and the number of requesting users concerning the network performance and propose an optimal configuration and new mechanisms to improve the network capacity, based on realistic restrictions.

The radio spectrum is a limited resource. Demand for wireless communication services is increasing exponentially, stressing the availability of radio spectrum to accommodate new services. TV white space (TVWS) technologies allow a dynamic usage of the spectrum. These technologies provide wireless connectivity, in the channels of the very high frequency and ultra high frequency television broadcasting bands. In this paper, we investigate and compare the coverage range, network capacity, and network energy efficiency for TVWS technologies and LTE. We consider Ghent, Belgium, and Boyeros, Havana, Cuba, to evaluate a realistic outdoor suburban and rural area, respectively. The comparison shows that TVWS networks have an energy efficiency 9-12 times higher than LTE networks.