Currently, smart farming is considered an effective solution to enhance the productivity of farms; thereby, it has recently received broad interest from service providers to offer a wide range of applications, from pest identification to asset monitoring. Although the emergence of digital technologies, such as the Internet of Things (IoT) and low-power wide-area networks (LPWANs), has led to significant advances in the smart farming industry, farming operations still need more efficient solutions. On the other hand, the utilization of unmanned aerial vehicles (UAVs), also known as drones, is growing rapidly across many civil application domains. This paper aims to develop a farm monitoring system that incorporates UAV, LPWAN, and IoT technologies to transform the current farm management approach and aid farmers in obtaining actionable data from their farm operations. In this regard, an IoT-based water quality monitoring system was developed because water is an essential aspect in livestock development. Then, based on the Long-Range Wide-Area Network (LoRaWAN®) technology, a multi-channel LoRaWAN® gateway was developed and integrated into a vertical takeoff and landing drone to convey collected data from the sensors to the cloud for further analysis. In addition, to develop LoRaWAN®-based aerial communication, a series of measurements and simulations were performed under different configurations and scenarios. Finally, to enhance the efficiency of aerial-based data collection, the UAV path planning was optimized. Measurement results showed that the maximum achievable LoRa coverage when operating on-air via the drone is about 10 km, and the Longley–Rice irregular terrain model provides the most suitable path loss model for the scenario of large-scale farms, and a multi-channel gateway with a spreading factor of 12 provides the most reliable communication link at a high drone speed (up to 95 km/h). Simulation results showed that the developed system can overcome the coverage limitation of LoRaWAN® and it can establish a reliable communication link over large-scale wireless sensor networks. In addition, it was shown that by optimizing flight paths, aerial data collection could be performed in a much shorter time than industrial mission planning (up to four times in our case).

The Internet of Things (IoT) has rapidly expanded for a wide range of applications towards a smart future world by connecting everything. As a result, new challenges emerge in meeting the requirements of IoT applications while retaining optimal performance. These challenges may include power consumption, quality of service, localization, security, and accurate modeling and characterization of wireless channel propagation. Among these challenges, the latter is critical to establishing point-to-point wireless communication between the sensors. Channel modeling also varies depending on the features of the surrounding area, which have a direct impact on the propagation of wireless signals. This presents a difficult task for network planners to efficiently design and deploy IoT applications without understanding the appropriate channel model to analyze coverage and predict optimal deployment configurations. As a result, this challenge has attracted considerable interest in academic and industrial communities in recent years. Therefore, this review presents an overview of current breakthroughs in wireless IoT technologies. The challenges in such applications are then briefly reviewed, focusing on wireless channel propagation modeling and characterization. Finally, the study gives a generalized form of commonly used channel models and a summary of recent channel modeling developments for wireless IoT technology. The outcome of this review is expected to provide a new understanding of the propagation behavior of present and future wireless IoT technologies, allowing network engineers to undertake correct planning and deployment in any environment. Additionally, the study may serve as a guideline for future channel modeling and characterization studies.

The need for wider coverage and high-performance quality of mobile networks is critical due to the maturity of Internet penetration in today's society. One of the primary drivers of this demand is the dramatic shift toward digitalization due to the Covid-19 pandemic impact. Meanwhile, the emergence of the 5G wireless standard and the increasingly complex actual operating environment of mobile networks make the traditional prediction model less reliable. With the recent advancements and promising capabilities of machine learning (ML), it is seen as an alternative to the traditional approaches for ground to ground (G2G) mobile communication coverage prediction. In this study, various ML models have been tested and evaluated to develop an ML-based received signal strength prediction model for mobile networks. However, the challenge is to identify a practical ML model that can fulfill the computing speed criteria while still meeting the prediction accuracy. A total of six categories of ML models, namely Linear Regression (LR), Artificial Neural Network (ANN), Support Vector Machine (SVM), Regression Trees (RT), Ensembles of Trees (ET), and Gaussian Process Regression (GPR) that consists of more than 20 types of established algorithms/kernels have been tested and evaluated in this paper to identify the best contender among them, in terms of speed and accuracy. Findings from the evaluation showed that the GPR model is the most accurate model for Reference Signal Received Power (RSRP) prediction in terms of RMSE and R 2 , followed by ET, RT, SVM, ANN and LR. Nevertheless, prediction speed and model training times are also important factors in determining the most practical model for RSRP prediction for several real-world mobile network planning applications. Finally, the ET model with Random Forest (RF) algorithm has been selected and highly recommended as the most practically employed ML model for developing rigorous RSRP predictions model in multi-frequency bands and multi-environment. The developed prediction model is capable of being utilized for the network analysis and optimization.

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