期刊
SENSORS
卷 22, 期 10, 页码 -出版社
MDPI
DOI: 10.3390/s22103615
关键词
ambient noise seismic interferometry; Apache Cassandra; datastax enterprise; edge computing; edge storage; Internet of Things (IoT); raspberry pi; sensor networks
资金
- U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy [FOA DE-FOA-0001445]
- U.S. Department of Energy (Energy Efficiency and Renewable Energy Agency, Geothermal Technologies Program) [DE-EE0007699]
Recent technological advances have made the development of sensor networks for remote environmental monitoring easier and cheaper. However, there are still challenges in acquiring, transmitting, storing, and processing remote environmental data. Edge computing offers a solution by placing substantial storage and computing resources at the edge of the network, reducing the burden on centralized systems.
Recent technological advances have reduced the complexity and cost of developing sensor networks for remote environmental monitoring. However, the challenges of acquiring, transmitting, storing, and processing remote environmental data remain significant. The transmission of large volumes of sensor data to a centralized location (i.e., the cloud) burdens network resources, introduces latency and jitter, and can ultimately impact user experience. Edge computing has emerged as a paradigm in which substantial storage and computing resources are located at the edge of the network. In this paper, we present an edge storage and computing framework leveraging commercially available components organized in a tiered architecture and arranged in a hub-and-spoke topology. The framework includes a popular distributed database to support the acquisition, transmission, storage, and processing of Internet-of-Things-based sensor network data in a field setting. We present details regarding the architecture, distributed database, embedded systems, and topology used to implement an edge-based solution. Lastly, a real-world case study (i.e., seismic) is presented that leverages the edge storage and computing framework to acquire, transmit, store, and process millions of samples of data per hour.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据