LoRaWAN has become a leading Low Power Wide Area Network (LPWAN) technology for Industrial Internet of Things (IIoT) applications, offering long range communication with low energy consumption. A fundamental challenge lies in selecting the appropriate Spreading Factor (SF) for each device, since this directly influences coverage, packet success ratio (PSR), and airtime. The default Adaptive Data Rate (ADR) mechanism is static and fails to adapt under dynamic network conditions such as mobility. This paper proposes a lightweight linear upper confidence bandit (LinUCB–SF) based reinforcement learning approach for adaptive SF selection. Each end device uses locally observable features to autonomously select its SF, balancing exploration and exploitation. The method is implemented and validated in ns-3 simulations across a range of deployment densities. Results show that our proposed LinUCB-SF algorithm reduces energy consumption by 19.3% in mobile scenarios and 37.8% in static scenarios, while improving PSR by 9.1% and 9.0%, respectively, compared to the EXP3 baseline.

The exponential growth of encrypted network traffic in IoT ecosystems has created a critical challenge: maintaining privacy while enabling effective malware detection. This paper presents a machine learning (ML) and deep learning (DL) framework for detecting sophisticated malware (e.g., ransomware, trojans, and spyware) in encrypted Internet of Things (IoT) traffic, combining feature engineering with model fusion techniques. We evaluate Random Forest, LSTM, and RNN models on the CIC MalMem 2022 dataset, achieving an 87.1% accuracy with Random Forest - significantly outperforming sequential models (74.6%). Our proposed methodology includes: (1) a novel feature selection pipeline using mutual information for encrypted IoT traffic analysis, and (2) comprehensive benchmarking of traditional ML versus DL approaches. The results demonstrate this framework can potentially be deployed in smart cities and healthcare IoT systems where encrypted traffic analysis must balance detection accuracy with computational efficiency.

LoRaWAN is a low-power wide-area network technology that has become the de-facto for the Internet of Things (IoT) due to its low cost, ultra-low energy consumption, longrange, and support for the massive end devices (EDs). Adaptive data rate (ADR) is the most widely adopted approach for resource assignment with spreading factor (SF) and transmission power (TP) to massive EDs in the LoRaWAN network, recommended for static IoT applications such as metering. However, in a mobile IoT environment, ADR fails to adjust the resources due to dramatic changes in the signal strength owing to the underlying dynamic environment, resulting in massive packet loss and retransmissions. To assign suitable SF and TP parameters to mobile IoT EDs, we propose mobility adaptive data rate (M-ADR) using Kalman Filter. The proposed M-ADR determines the ED status (i.e., either static or mobile) by finding the distance between the previous and current positions of the ED at the NS. When the ED status is determined as mobile, we propose utilizing Kalman Filter to estimate the signal-to-noise ratio (SNR) to accurately determine SF, TP, or both, as these parameters are primarily dependent on SNR. When the Kalman Filter decides the current estimate of the system, the proposed M-ADR further finds the best possible configuration of the SF and TP. Simulation results show that the proposed M-ADR enhanced the packet success ratio by 16.88% compared with the state-of-the-art ADR of LoRaWAN.

It is known that an ultrawideband (UWB)-based indoor positioning system (IPS) has superior positioning performance and can meet the requirements of location-based services (LBSs) as the Internet of Things (IoT) applications. However, there is a limitation of UWB positioning when it is conducted at the nonline-of-sight (NLOS) channels degrading the UWB ranging accuracy at indoor environments. In this article, we propose an artificial intelligence (AI) applied UWB positioning system that can enhance the positioning performance by classifying channel conditions with channel impulse response (CIR) of the received UWB signal. The proposed system mitigates the positioning degradation caused by the NLOS situations by performing extended Kalman filter (EKF) localization and long short-term memory (LSTM) training of the observed channel status. The main feature of the proposed UWB positioning method is that it can be used even at unknown locations not trained with the LSTM model learning the channel status, because of our training strategy of not the position coordinates, but the UWB ranging error between UWB devices corresponding to CIR of the received UWB signal. This article provides the experimental setup and performance evaluation results of the proposed system. The evaluation results showed that the proposed AI-applied UWB positioning method significantly improved its accuracy performance compared with the existing positioning methods.

Adaptive data rate (ADR) is a widely adopted resource assignment approach in long-range wide-area networks (LoRaWANs) for static Internet of Things (IoT) applications such as smart grids and metering. Blind ADR (BADR) has been recommended for mobile IoT applications such as pet and industrial asset tracking. However, ADR and BADR cannot provide appropriate measures to alleviate the massive packet loss problem caused by the unsuitable spreading factors (SFs) assigned to end devices when they are mobile. This article proposes a novel proactive approach—“artificial intelligence-empowered resource allocation” (AI-ERA)—to address the resource assignment issue in static and mobile IoT applications. The AI-ERA approach consists of two modes, namely offline and online modes. First, a deep neural network (DNN) model is trained with a dataset generated at ns-3 in the offline mode. Second, the proposed AI-ERA approach utilizes the pretrained DNN model in the online mode to proactively assign an efficient SF for the end device before each uplink packet transmission. The proactive behavior of the AI-ERA improved the packet success ratio by an average of 32% and 28% in static and mobility scenarios compared with the typical LoRaWAN ADR, respectively.

LoRaWAN exhibits an essential feature, namely, the adaptive data rate (ADR), which has been recommended for the management of resources (e.g., the spreading factor and transmit power) of static end devices (EDs) based on channel conditions. Blind ADR (BADR) has been introduced for LoRaWAN mobile applications that experience frequent channel attenuation when the ED moves (e.g., pet-tracking). This channel condition leads to massive packet loss and retransmission, which significantly increases energy consumption. In this study, ADR and BADR are investigated in mobility environments, their limitations are highlighted, and a novel ADR “retransmission-assisted resource management (R-ARM)” system is proposed. The proposed R-ARM system operates concurrently on the ED and network server sides. This improves the network performance of the LoRaWAN. When compared to those of typical ADR approaches, the simulation results, in this case, show that R-ARM significantly enhances the packet success ratio and convergence period, and it lowers the energy consumption and packet loss ratio.