skip to main content


Search for: All records

Award ID contains: 1909177

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. 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. 
    more » « less
    Free, publicly-accessible full text available April 30, 2024
  2. null (Ed.)
    With the development of the Internet of Things (IoT), many kinds of wireless signals (e.g., Wi-Fi, LoRa, RFID) are filling our living and working spaces nowadays. Beyond communication, wireless signals can sense the status of surrounding objects, known as wireless sensing , with their reflection, scattering, and refraction while propagating in space. In the last decade, many sophisticated wireless sensing techniques and systems were widely studied for various applications (e.g., gesture recognition, localization, and object imaging). Recently, deep Artificial Intelligence (AI), also known as Deep Learning (DL), has shown great success in computer vision. And some works have initially proved that deep AI can benefit wireless sensing as well, leading to a brand-new step toward ubiquitous sensing. In this survey, we focus on the evolution of wireless sensing enhanced by deep AI techniques. We first present a general workflow of Wireless Sensing Systems (WSSs) which consists of signal pre-processing, high-level feature, and sensing model formulation. For each module, existing deep AI-based techniques are summarized, further compared with traditional approaches. Then, we provide a view of issues and challenges induced by combining deep AI and wireless sensing together. Finally, we discuss the future trends of deep AI to enable ubiquitous wireless sensing. 
    more » « less