|Abstract:||The advent of services and applications that rely on Global Navigation Satellite System (GNSS) to procure reliable, accurate and ubiquitous positioning and timing information is forcing conventional GNSS sensors’ computational resources to its limits. In addition, such information is expected to be provided at low energy consumption and at a low cost. These features have led to the implementation of novel techniques and architectures such as the Cloud GNSS receiver. In this architecture, the migration of the computational tasks (e.g. GNSS signal processing) from the device to a cloud server where high-scalable and high-performance computing resources are available is carried out with the aim of reducing the energy required by the sensor or device in order to obtain a position fix. This paper analyzes the energy efficiency and the accuracy performance of the Cloud GNSS receiver. To prove its feasibility, the energy consumption of a conventional GNSS sensor is analyzed, confirming the GNSS module as one of the most consuming components together with the transmission Radio Frequency (RF) front-end. Then, the energy required by the alternative cloud GNSS sensor is addressed and compared with conventional GNSS sensors. The obtained results reveal the feasibility of cloud architectures for GNSS signal processing in energetic and positioning accuracy terms. Costs derived from data transmission and the use of cloud resources are also assessed within the framework of an Internet-of-Things (IoT) application.|
Proceedings of the 30th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2017)
September 25 - 29, 2017
Oregon Convention Center
|Pages:||3843 - 3852|
|Cite this article:||
Lucas-Sabola, V., Seco-Granados, G., López-Salcedo, J.A., García-Molina, J.A., Crisci, M., "Efficiency Analysis of Cloud GNSS Signal Processing for IoT Applications," Proceedings of the 30th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2017), Portland, Oregon, September 2017, pp. 3843-3852.
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