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Asking questions is a fundamental aspect of human nature. Languages all around the world encode interrogative constructions. It is therefore incumbent upon semanticists to capture the meaning of questions. However, achieving this goal faces a challenge under a truth conditional approach to meaning, since questions cannot easily be assigned a truth value. Moreover, it is not sufficient to focus only on the questions themselves; one must also determine what counts as a felicitous and informative answer, and how this relates to a speaker's intention in posing a question in a discourse context. How then do semanticists approach an investigation of questions? In this article, we present the core issues inherent to question‐answer dynamics, review the main approaches to question‐answer meaning, highlight how questions are situated in a discourse context, and explore extensions of questions that highlight the connection between semantics, pragmatics, and human reasoning.
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A Data-Driven Model for LoRaWAN Connection Quality and Coverage
LoRaWAN is a popular Long-Range Low-Power wireless communications protocol that is enabling many IoT applications worldwide, with more networks growing both in size and number around the world. To effectively plan and operate these networks, it is necessary to have tools that reliably quantify, measure, and predict the connection quality provided by LoRaWAN receivers. Being able to reliably quantify connection quality would allow LoRaWAN adopters to answer questions such as, “What does ‘good coverage’ mean?”. Reliably measuring coverage would allow for questions like “What is the quality of network coverage in a given area?”, to be answered, while predicting connection quality would allow adopters to answer questions such as “What would the coverage quality be if we deployed an additional wireless receiver in this location?” This paper proposes a novel data-driven approach to connection quality modeling that is tailored for LoRaWAN with the following features. First, connection quality is quantified by the packet reception rate (PRR), as opposed to the traditional received signal strength typical of generic radio planning tools. The PRR more closely captures what network operators and users ultimately care about. Next, we leverage a large set of original data to fit a model for PRR. This dataset is unique in two ways. First, it includes transmissions that were transmitted but not received by any gateway, eliminating an otherwise persistent source of bias in empirical estimates of wireless connectivity. Second, it includes features derived from high-fidelity terrain topology extracted from LiDAR point clouds. Our model includes both feature extraction and estimation. We evaluate our model out-of-sample, including in regions entirely disjoint from the training data, and show that it is considerably more accurate than common benchmark wireless propagation models. Finally, we demonstrate how our model can be used to provide coverage maps in a real-world network.
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- Award ID(s):
- 1952063
- PAR ID:
- 10659999
- Publisher / Repository:
- IEEE
- Date Published:
- Page Range / eLocation ID:
- 400 to 406
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/WF-IoT62078.2024.10811435
