Raspberry Pi as a Wireless Sensor node: Performances and constraints

Vujović, Vladimir; Maksimović, Mirjana

doi:10.1109/mipro.2014.6859717

Cited by 149 publications

(64 citation statements)

References 4 publications

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“…A key element in a Wireless Sensor Network (WSN) is a sensor node consisting of four basic elements: the sensor unit, the processing unit, the communication unit and the power supply [7]. In this study, the processing unit uses a Raspberry Pi computer, which is reasonably priced, flexible, adaptable and programmed quite easily.…”

Section: The Processing Unit and Sensorsmentioning

confidence: 99%

Broadcasting the Status of Plant Growth Chamber using Bluetooth Low Energy

et al. 2018

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Abstract. Mobile users are getting smarter in using their phones. Many tasks are usually completed or monitored via a computer screen, nowadays can be taken anywhere with Android phones or tablets. Likewise with features in the phone is increasingly sophisticated. Currently bluetooth version 4 is almost mandatory in all phones. Even for entry level phones are now equipped with bluetooth version 4. This paper discusses the use of Bluetooth Low Energy-which is part of bluetooth version 4, in providing information about the status of plant growth chamber conditions. By using this concept, all phones or tablets that have bluetooth version 4 and there are applications in it will be able to receive the latest chamber status. The results show that this way of broadcasting is very effective. The data required to be monitored by a laboratory technician can be continuously broadcasted so that anyone on duty will get instant information at his grasp.

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Section: The Processing Unit and Sensorsmentioning

confidence: 99%

Broadcasting the Status of Plant Growth Chamber using Bluetooth Low Energy

et al. 2018

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“…When Raspberry Pi is compared with Arduino and Udoo from the viewpoint of smart home, Raspberry Pi is superior in terms of computing power, memory, multitasking, OS porting, and high-level language, but it is heavier and consumes more electric power [4][5].…”

Section: Web Server Building With Raspberry Pimentioning

confidence: 99%

The Developement of Real-time Information Support Cart System based on IoT

Seo¹,

Kang²,

Jung³

2017

International journal of advanced smart convergence

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“…Since its launch in 2012, the Raspberry Pi has quickly become one of the best-selling computers and has stimulated various interesting projects across both industry and academia that fully exploit the low cost low power full feature computer [6][7][8][9][10][11]. As of 29 February 2016, the total number of units sold worldwide has passed 8 million [12].…”

Section: Related Workmentioning

confidence: 99%

Understanding the Performance of Low Power Raspberry Pi Cloud for Big Data

Hajji

Tso

2016

Electronics

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Nowadays, Internet-of-Things (IoT) devices generate data at high speed and large volume. Often the data require real-time processing to support high system responsiveness which can be supported by localised Cloud and/or Fog computing paradigms. However, there are considerably large deployments of IoT such as sensor networks in remote areas where Internet connectivity is sparse, challenging the localised Cloud and/or Fog computing paradigms. With the advent of the Raspberry Pi, a credit card-sized single board computer, there is a great opportunity to construct low-cost, low-power portable cloud to support real-time data processing next to IoT deployments. In this paper, we extend our previous work on constructing Raspberry Pi Cloud to study its feasibility for real-time big data analytics under realistic application-level workload in both native and virtualised environments. We have extensively tested the performance of a single node Raspberry Pi 2 Model B with httperf and a cluster of 12 nodes with Apache Spark and HDFS (Hadoop Distributed File System). Our results have demonstrated that our portable cloud is useful for supporting real-time big data analytics. On the other hand, our results have also unveiled that overhead for CPU-bound workload in virtualised environment is surprisingly high, at 67.2%. We have found that, for big data applications, the virtualisation overhead is fractional for small jobs but becomes more significant for large jobs, up to 28.6%.

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Raspberry Pi as a Wireless Sensor node: Performances and constraints

Cited by 149 publications

References 4 publications

Broadcasting the Status of Plant Growth Chamber using Bluetooth Low Energy

Broadcasting the Status of Plant Growth Chamber using Bluetooth Low Energy

The Developement of Real-time Information Support Cart System based on IoT

Understanding the Performance of Low Power Raspberry Pi Cloud for Big Data

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