Globally, air pollution has become a major problem due to the growth of the human population and rapid urbanization, posing a serious threat to the environment and human health. Currently, most monitoring systems are based on fixed monitoring stations and are inefficient due to slow response time. An accurate and real-time air quality monitoring and prediction (AQMP) system is required to prevent health problems caused by air pollution. With the swift advancement of Internet of Things (IoT) technologies such as low-cost sensors and advanced wireless communication systems capable of sensing their environment, connecting with other systems, and interacting with users. To overcome the problems of existing systems, this paper aims to develop an unmanned aerial vehicle (UAV)-based real-time AQMP system by integrating artificial intelligence and data science technologies. The proposed UAV is equipped with lightweight air quality sensors including particulate matter (i.e., PM2.5 and PM10), carbon monoxide (CO), carbon dioxide (CO2), and ozone (O3), and meteorological sensors. The sensors are mounted on the UAV via the microcontroller, and the sensors reading data is transmitted to the web server via a message queueing telemetry transport (MQTT) protocol using a 5G mobile network module. Practical experiments were conducted in Hsinchu City and the industrial area of Yunlin County to collect real-time air pollutant data. Then, we employed a hybrid of attention mechanism, convolutional neural network (CNN) and gated recurrent unit (GRU) algorithms to predict future air quality levels. Experimental results show that the proposed system improves prediction performance compared with baseline time series algorithms.

The integration of unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RIS) in sixth-generation (6G) networks offers significant potential for enhancing integrated sensing and communication (ISAC) systems. Motivated by the need for real-time adaptability, spectrum efficiency, and intelligent wireless infrastructure, this paper investigates a UAV-RIS-enabled ISAC framework capable of meeting the dual demands of sensing and communication in complex environments. A key challenge in such systems lies in dynamically balancing resource allocation, UAV trajectory planning, and interference management. To address this, we propose a joint optimization framework in which a UAV simultaneously conducts sensing operations and serves multiple ground users in obstructed environments. The formulated problem involves a non-convex trade-off between maximizing sensing signal-to-noise ratio (SNR) and communication throughput. To solve it, we develop a multi-agent deep reinforcement learning (MADRL) solution. Three collaborative agents independently optimize UAV trajectories using Deep Deterministic Policy Gradient (DDPG), RIS phase shifts using Twin Delayed DDPG (TD3), and beamforming matrices using Proximal Policy Optimization (PPO). These agents learn optimal policies under dynamic channel conditions and mobility constraints. Simulation results show that the proposed approach improves sensing SNR by 22% and communication rates by 20% compared to baseline methods. With equal weight factors for sensing and communication, the system achieves 95% of the maximum theoretical sensing SNR while ensuring a minimum communication rate of 1 bps/Hz, validating its effectiveness in real-time ISAC environments.

The marine IoT (MIoT) has experienced widespread adoption in the maritime industry; however, its communication infrastructure faces significant challenges due to a lack of terrestrial networks and the dynamic nature of oceanic environments. In response, low-Earth orbit (LEO) satellites have been suggested as an alternative solution. However, LEO satellites also face limitations, including coverage gaps due to the power constraints of MIoT devices (MIoTDs) and the curvature of the Earth, which impede continuous connectivity. To address these challenges, integrating uncrewed aerial vehicles (UAVs) as aerial base stations between LEO satellites and MIoTDs has gained attention as an effective solution to enhance communication. Managing UAV mobility and ensuring reliable communication requires autonomous 3-D trajectory optimization and dynamic resource allocation. This article presents an integrated LEO and multi-UAV-enabled MIoT network designed to maximize communication coverage, system throughput, and fairness. To achieve this, we formulate a joint UAV trajectories and resource allocation (UTCRA) optimization problem. To address the UTCRA problem, we propose a two-stage learning approach: 1) Gaussian mixture models (GMMs)-based clustering is initially applied to dynamically cluster MIoTDs under different UAVs, ensuring a balanced distribution of user loads among UAVs. 2) A federated multiagent deep deterministic policy gradient (FL-MADDPG) algorithm is employed to continuously optimize UTCRA, thereby enhancing communication efficiency. Simulation results demonstrate that FL-MADDPG with GMM significantly enhances communication coverage, average throughput, and fairness. It outperforms benchmark algorithms across all evaluated metrics, highlighting its strong potential as a method for next-generation MIoT networks.

Over the last few years, drone base station (DBS) technology has been recognized as a promising solution to the problem of network design for wireless communication systems, due to its highly flexible deployment and dynamic mobility features. This article focuses on the 3-D mobility control of the DBS to boost transmission coverage and network connectivity. We propose a dynamic and scalable control strategy for drone mobility using deep reinforcement learning (DRL). The design goal is to maximize communication coverage and network connectivity for multiple real-time users over a time horizon. The proposed method functions according to the received signals of mobile users, without the information of user locations. It is divided into two hierarchical stages. First, a time-series convolutional neural network (CNN)-based link quality estimation model is used to determine the link quality at each timeslot. Second, a deep$Q$-learning algorithm is applied to control the movement of the DBS in hotspot areas to meet user requirements. Simulation results show that the proposed method achieves significant network performance in terms of both communication coverage and network throughput in a dynamic environment, compared with the$Q$-learning algorithm.

Many Internet-of-Things (IoT) services rely on location information. This article proposes a deep learning-based fingerprinting outdoor positioning scheme (DFOPS) for use in scalable environments. The proposed scheme is a hierarchical combination of the support vector machine (SVM) and long short-term memory (LSTM) algorithms. It was applied in a large-scale wireless environment with multiple wireless local area networks (WLANs) and cellular base stations. The results show that the positioning error of the proposed scheme is 42 cm, and the computation time is reduced by 63% compared with conventional methods. Thus, the proposed system can provide promising and reasonable support location-aware services for IoT devices in large-scale wireless environments.

Mobile users are interested in utilising high network capabilities without time and place constraints. However, with a high level of interest in the usage of mobile phones and internet facilities, the limited capacity of terrestrial base stations (BSs) is unbalanced. As a potential alternative to BSs, unmanned aerial vehicles (UAVs) are emerging as a means of transmitting wireless data to ground mobile users. As an air‐to‐ground communication network, the real UAVs deployed and collected communication data from ground mobile users. The main objective of this study is to analyse and evaluate user throughput, interference, and power transmission when the UAVs are at different heights. The parameters used include the locations of the UAVs and users, the altitudes and elevation angles from the users to UAVs, signal‐to‐noise‐ratio, throughput values, the categories of line‐of‐sight, and non‐line‐of‐sight links. Furthermore, K ‐means used as a clustering method for class identification, long short‐term memory (LSTM), and gated recurrent unit (GRU) to analyse and evaluate system performance. The system's performance was compared with a multi‐layer perceptron approach. The evaluation results show that the proposed LSTM–GRU provides reliable and encouraging performance with low computational complexity, which is appropriate for heterogeneous networks.

Due to the rapid growth of mobile technology, unmanned aerial vehicles(UAV) is emerging as a promising solution to distribute wireless data for ground users as a base station (BS). Our study focuses on the analysis of UAV-based BS that assist aerial wireless network. We practically used the real data measurement from the UAVs connected with ground mobile users as air-to- ground(A2G) communication service. The main aim of our work is to analyze and estimate the UAV-BS user throughput with different parameters such as height and distance. In order to achieve our objective, we have estimated the locations of UAV' and mobile-users', heights of UAV, elevation angle and nature of LoS/NLoS. The system performances are evaluated through long short term memory(LSTM) and comparison was made with multi- layer perceptron(MLP) algorithm. Finally, the evaluation result shows the system has accurate and motivated prediction performances of the user throughput.

Recently, Location Based Services (LBSs) becomes an important technology to enhance the applicability of Internet-of-Things (IoT) to provide better services in wireless environments. The Global Positioning System (GPS) is not always optimal for urban and suburban areas to provide positioning services. Because GPS is easily affected by signal fluctuations and shadowing effects in a complex and dynamic environments, consumes much power and it is hardware dependent. In this paper, we present an accurate Fingerprinting Outdoor Positioning Scheme (FOPS) that contains Wi-Fi and Orthogonal Frequency Division Multiplexing (OFDM) signal values as a dataset using Long Short-Term Memory (LSTM) network approach. To select the most representative features from Wi-Fi data, we apply Linear Discriminant Analysis (LDA) techniques as preprocessing stage to optimize positioning services. The experimental results revealed that, the proposed system achieves positioning results with error no more than 1.7 m. Moreover, the positioning time improved due to the integrations of LDA and LSTM algorithms. Therefore, the proposed system provides a promising positioning services of the IoT devices in wireless environments.