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Low-Power Wide-Area Networks (LPWANs) are an emerging Internet-of-Things (IoT) paradigm, which caters to large-scale and long-term sensory data collection demand. Among the commercialized LPWAN technologies, LoRa (Long Range) attracts much interest from academia and industry due to its open-source physical (PHY) layer and standardized networking stack. In the flourishing LoRa community, many observations and countermeasures have been proposed to understand and improve the performance of LoRa networking in practice. From the perspective of the LoRa networking stack; however, we lack a whole picture to comprehensively understand what has been done or not and reveal what the future trends are. This survey proposes a taxonomy of a two-dimensional (i.e., networking layers, performance metrics) to categorize and compare the cutting-edge LoRa networking techniques. One dimension is the layered structure of the LoRa networking stack. From down to the top, we have the PHY layer, Link layer, Media-access Control (MAC) layer, and Application (App) layer. In each layer, we focus on the three most representative layer-specific research issues for fine-grained categorizing. The other dimension is LoRa networking performance metrics, including range, throughput, energy, and security. We compare different techniques in terms of these metrics and further overview the open issues and challenges, followed by our observed future trends. According to our proposed taxonomy, we aim at clarifying several ways to achieve a more effective LoRa networking stack and find more LoRa applicable scenarios, leading to a brand-new step toward a large-scale and long-term IoT. 

To bridge the giant semantic gap between applications and modern storage systems, passing a piece of tiny and useful information, called I/O access hints, from upper layers to the storage layer may greatly improve application performance and ease data management in storage systems. This is especially true for heterogeneous storage systems that consist of multiple types of storage devices. Since ingesting external access hints will likely involve laborious modifications of legacy I/O stacks, it is very hard to evaluate the effect and take advantages of access hints. In this article, we design a generic and flexible framework, called HintStor, to quickly play with a set of I/O access hints and evaluate their impacts on heterogeneous storage systems. HintStor provides a new application/user-level interface, a file system plugin, and performs data management with a generic block storage data manager. We demonstrate the flexibility of HintStor by evaluating four types of access hints: file system data classification, stream ID, cloud prefetch, and I/O task scheduling on a Linux platform. The results show that HintStor can execute and evaluate various I/O access hints under different scenarios with minor modifications to the kernel and applications.