IoT Sensor Data Acquisition and Storage System Using Raspberry Pi and Apache Cassandra

Ferencz, Katalin; Domokos, József

doi:10.1109/cando-epe.2018.8601139

Cited by 12 publications

(7 citation statements)

References 2 publications

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“…In time, not only have they allowed the possibility of collecting data from processes with the aim of local automatic control, but they have also allowed humans to informally/virtually be present in places that are not safe for life or physical integrity. With the aid of various communication protocols, architectures like WSN (Wireless Sensor Network) and IoT (Internet of Things) have emerged and evolved, and now they are strongly related and omnipresent when it comes to fields like remote real-time data acquisition [ 15 , 16 , 17 ], storage [ 16 ], control [ 18 ], big data [ 19 , 20 ], data logging [ 20 , 21 ], data mining/machine learning [ 20 , 21 ], cloud/edge/fog computing [ 21 , 22 , 23 , 24 ].…”

Section: Related Workmentioning

confidence: 99%

Easy-to-Use MOX-Based VOC Sensors for Efficient Indoor Air Quality Monitoring

Pietraru,

Nicolae,

Mocanu

et al. 2024

Sensors

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This research paper presents a case study on the application of Metal Oxide Semiconductor (MOX)-based VOC/TVOC sensors for indoor air quality (IAQ) monitoring. This study focuses on the ease of use and the practical benefits of these sensors, drawing insights from measurements conducted in a university laboratory setting. The investigation showcases the straightforward integration of MOX-based sensors into existing IAQ monitoring systems, highlighting their user-friendly features and the ability to provide precise and real-time information on volatile organic compound concentrations. Emphasizing ease of installation, minimal maintenance, and immediate data accessibility, this paper demonstrates the practicality of incorporating MOX-based sensors for efficient IAQ management. The findings contribute to the broader understanding of MOX sensor capabilities, providing valuable insights for those seeking straightforward and effective solutions for indoor air quality monitoring. This case study outlines the feasibility and benefits of utilizing MOX-based sensors in various environments, offering a promising avenue for the widespread adoption of user-friendly technologies in IAQ management.

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Section: Related Workmentioning

confidence: 99%

Easy-to-Use MOX-Based VOC Sensors for Efficient Indoor Air Quality Monitoring

Pietraru,

Nicolae,

Mocanu

et al. 2024

Sensors

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“…Cassandra and DSE were established cloud and on-premises NoSQL solution in 2016 However, to the best of our knowledge no one had attempted to deploy Cassandra or DSE, on an embedded system, as an edge-based storage and computing solution supporting remote environmental monitoring. Nonetheless, there have been several publications since 2016 exploring the idea of utilizing the Raspberry Pi and Cassandra for IoT applications [ 74 , 75 , 76 ]. In 2017, Richardson [ 74 ] explored the feasibility of using the Raspberry Pi to host Cassandra in support of IoT applications.…”

Section: Framework Overviewmentioning

confidence: 99%

“…In 2018, Ferencz and Domokos [ 75 ] introduced a data acquisition and storage system using Cassandra and the Raspberry Pi as an alternative to existing IoT data acquisition and storage solutions. Although their system architecture represented a practical and flexible approach to IoT acquisition and storage, Ferencz and Domokos [ 75 ] did not run Cassandra on the Raspberry Pi. Likewise, Ooi et al [ 77 ] utilized the Raspberry Pi and Cassandra to effectively acquire and store sensor network data (i.e., seismic).…”

Section: Framework Overviewmentioning

confidence: 99%

The Edge of Exploration: An Edge Storage and Computing Framework for Ambient Noise Seismic Interferometry Using Internet of Things Based Sensor Networks

Sepulveda

Thangraj

Pulliam

2022

Sensors

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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.

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“…Citation information: DOI 10.1109/ACCESS.2022.3197151 vices. There have been projects deployed on ARM [186]- [188] with containers, and there have been some vulnerabilities like meltdown and side channels attack for ARM boards/microprocessors [189], [190], but this is the first work that includes security issues of containers deployed on ARM devices.…”

Section: Securitymentioning

confidence: 99%

Container Technologies for ARM Architecture: A Comprehensive Survey of the State-of-the-Art

et al. 2022

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Container technology is becoming increasingly popular as an alternative to traditional virtual machines because it provides a faster, lighter, and more portable runtime environment for the applications. A container bundles the application and its binary code, libraries, and configuration files together while sharing the host operating system image. Accordingly, containers efficiently share resources and operate small micro-services, software programs, and even more extensive applications with less overhead than virtual machines. There are many container technologies available with Docker being the most popular and many technologies support multiple architectures, including the ARM architecture. Due to its energy efficiency and high-performance, which are crucial parameters in containerization, ARM architecture is becoming prevalent in container technologies. In this paper, we explore various container technologies that support ARM architecture and investigate the pros and cons of each technology. Moreover, we provide a comparative analysis of both container orchestrators and container runtimes that are most prominent competitors of Docker. We also consider security of container technologies with particular focus on the image scanning tools that supports ARM architecture. Our survey reveals that ARM technology is gaining popularity in containerization and almost all recent technologies support ARM architecture.

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IoT Sensor Data Acquisition and Storage System Using Raspberry Pi and Apache Cassandra

Cited by 12 publications

References 2 publications

Easy-to-Use MOX-Based VOC Sensors for Efficient Indoor Air Quality Monitoring

Easy-to-Use MOX-Based VOC Sensors for Efficient Indoor Air Quality Monitoring

The Edge of Exploration: An Edge Storage and Computing Framework for Ambient Noise Seismic Interferometry Using Internet of Things Based Sensor Networks

Container Technologies for ARM Architecture: A Comprehensive Survey of the State-of-the-Art

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