Future Internet

Among indoor positioning technologies, Wi-Fi fingerprinting using the Received Signal Strength Indicator (RSSI) is the most convenient and cost-effective method for indoor positioning. Instability and interference in wireless signal transmission cause significant variations in the RSSI, especially in a dynamic environment (DE). These factors hamper the accuracy of fingerprint-based indoor positioning system (IPSs), as these systems may struggle to reliably match observed signal patterns with stored fingerprints. Thus, ensuring positioning accuracy is critically important when designing and implementing Wi-Fi IPSs. Currently, there is a lack of surveys that provide a detailed and systematic analysis of the impact of DEs on the accuracy and reliability of Wi-Fi indoor positioning. This systematic literature review (SLR) was conducted to examine three aspects of Wi-Fi indoor positioning based on the RSSI: the impact of a DE on indoor positioning accuracy, the importance of constructing radio maps for indoor localization, and the role of machine learning (ML)/deep learning (DL) models in predicting indoor position with minimal error despite the DE. This review was conducted according to a structured and well-defined methodology to search for and filter relevant studies on Wi-Fi indoor positioning using the RSSI. Through this systematic process, 128 papers (2018–2024) were identified as relevant and then extracted and thoroughly analyzed to effectively answer the specified research questions. Additionally, this review highlights gaps in existing research, suggests directions for future studies, and provides practical recommendations for enhancing Wi-Fi-based indoor positioning in DEs.

To enable remote and automatic monitoring of the farmland soil information, this paper has developed a soil monitoring system based on the Internet of Things (IoT), which mainly involves the development of a gateway server node, wireless sensor nodes, a remote monitoring platform, and photovoltaic (PV) modules. The Raspberry Pi 5-based gateway server periodically sends data acquisition commands to wireless sensor nodes via LoRa, receives soil data returned by sensor nodes, and stores them in a MySQL database. Using a remote monitoring platform, Internet users can monitor real-time and historical soil data stored in the database. The STM32F103C8T6-based wireless sensor node receives data acquisition commands from the gateway server, uses soil temperature and humidity sensors as well as a pH sensor to collect soil status, and then sends sensor data back to the gateway server via LoRa. The system is powered by both PV energy and batteries, which enhances the endurance capability. Experimental results show that the designed system works well in remotely monitoring soil information. Using the proposed query attempt dynamic adjustment (QADA) method, the wireless sensor node dynamically adjusts the number of query attempts, which reduces the data acquisition failure rate from 21–25% to no more than 0.33%. Using the obtained qualitative relationship that the data acquisition delay varies inversely with the LoRa transfer rate, the data acquisition delay can be reduced to less than 67 ms.

Nowadays, driven by the exponential growth of parameters and training data of AI applications and Large Language Models, a single GPU is no longer sufficient in terms of computing power and storage capacity. Building high-performance multi-GPU systems or a GPU cluster via vertical scaling (scale-up) has thus become an effective approach to break the bottleneck and has further emerged as a key research focus. Given that traditional inter-GPU communication technologies fail to meet the requirement of GPU interconnection in vertical scaling, a variety of high-performance inter-GPU communication protocols tailored for the scale-up domain have been proposed recently. Notably, due to the emerging nature of these demands and technologies, academic research in this field remains scarce, with limited deep participation from the academic community. Inspired by this trend, this article identifies the challenges and requirements of a scale-up network, analyzes the bottlenecks of traditional technologies like PCIe in a scale-up network, and surveys the emerging scale-up targeted technologies, including NVLink, OISA, UALink, SUE, and other X-Links. Then, an in-depth comparison and discussion is conducted, and we express our insights in protocol design and related technologies. We also highlight that existing emerging protocols and technologies still face limitations, with certain technical mechanisms requiring further exploration. Finally, this article presents future research directions and opportunities. As the first review article fully focusing on intra-node GPU interconnection in a scale-up network, this article aims to provide valuable insights and guidance for future research in this emerging field, and we hope to establish a foundation that will inspire and direct subsequent studies.