Technical development and socioeconomic implications of the Raspberry Pi as a learning tool in developing countries

Ali, Murat; Vlaskamp, Jozef; Eddin, Nof Nasser; Falconer, Ben; Oram, Colin

doi:10.1109/ceec.2013.6659454

Cited by 34 publications

(15 citation statements)

References 3 publications

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“…In recent years low-cost and energy efficient (low energy) microcomputer devices such as Raspberry Pi (RPi) microcomputer became available which are suitable for education purposes [15,16,17,18] which we used in our project to establish infrastructure for APRS reporting. Together with RPi it is possible to use software (in our case open source software) called software defined radio (SDR) to avoid using expensive hardware components where functions of hardware components are performed by software components [19].…”

Section: B Current Technology Applications and Advances In Amateur Rmentioning

confidence: 99%

A low energy APRS-IS client-server infrastructure implementation using Raspberry Pi

Hajdarevic

Konjicija

Subaşı

2014

2014 22nd Telecommunications Forum Telfor (TELFOR)

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Radio communication has great history of innovations. A hobby which helped in pioneering many innovations in radio communications that we are using today is radio amateurism. Hams are radio amateur hobbyists, which use radio communication to communicate, research, and explore new radio technologies and applications, such as Automatic Packet Reporting System (APRS), which is digital communications information channel for Ham radio. Among other purposes APRS is used to report and map position of any stationary or mobile object via radio. In this paper we presented a low cost APRS client-server infrastructure using Raspberry Pi, APRSdroid, and software defined radio (SDR).

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Section: B Current Technology Applications and Advances In Amateur Rmentioning

confidence: 99%

A low energy APRS-IS client-server infrastructure implementation using Raspberry Pi

Hajdarevic

Konjicija

Subaşı

2014

2014 22nd Telecommunications Forum Telfor (TELFOR)

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“…science and engineering education. Educators have exploited Raspberry Pi either as a single device [10,18] or as the basis of more sophisticated learning systems [7,8,[19][20][21] for their IoT education. Bruce et al [10] and Jamieson et al [18] described valuable experiences and case studies of using Raspberry Pi in undergraduate study.…”

Section: An Overview Of Raspberry Pimentioning

confidence: 99%

“…It is of critical importance to provide the next generation of computer scientists and engineers an opportunity to not only understand the concepts and principles of IoT, but also to study the practical development of IoT solidly, so that students can learn how to apply theories to real applications. Many universities have started to introduce IoT courses into their undergraduate curriculum [7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Raspberry Pi: An Effective Vehicle in Teaching the Internet of Things in Computer Science and Engineering

Zhong

Liang

2016

Electronics

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Abstract:The Raspberry Pi is being increasingly adopted as a suitable platform in both research and applications of the Internet of Things (IoT). This study presents a novel project-based teaching and learning approach devised in an Internet of Things course for undergraduate students in the computer science major, where the Raspberry Pi platform is used as an effective vehicle to greatly enhance students' learning performance and experience. The devised course begins with learning simple hardware and moves to building a whole prototype system. This paper illustrates the outcome of the proposed approach by demonstrating the prototype IoT systems designed and developed by students at the end of one such IoT course. Furthermore, this study provides insights and lessons regarding how to facilitate the use of the Raspberry Pi platform to successfully achieve the goals of project-based teaching and learning in IoT.

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“…Among the variety of vendors providing these single-board computers (SBCs), maybe the most renowned ones are Raspberry Pi and Odroid. Although the initial aim of these devices was to promote the teaching of basic computer science in schools [3,4] and developing countries [5][6][7], recent appearance of single-board computers with multicore ARM CPU chips and several gigabytes of main memory also provides a desirable hardware platform for the project-based learning paradigm in computer science and engineering education [8][9][10][11] and have attracted interest of a multitude of projects trying to take advantage of their very low-cost performance ratio (i.e., for scientific computing [12][13][14]) in contrast with other energyefficient but which are alternatives of higher cost [15].…”

Section: Introductionmentioning

confidence: 99%

On the Parallelization of Stream Compaction on a Low-Cost SDC Cluster

Bernabé

Acacio

2018

Scientific Programming

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Many highly parallel algorithms usually generate large volumes of data containing both valid and invalid elements, and high-performance solutions to the stream compaction problem reveal extremely important in such scenarios. Although parallel stream compaction has been extensively studied in GPU-based platforms, and more recently, in the Intel Xeon Phi platform, no study has considered yet its parallelization using a low-cost computing cluster, even when general-purpose single-board computing devices are gaining popularity among the scientific community due to their high performance per $ and watt. In this work, we consider the case of an extremely low-cost cluster composed by four Odroid C2 single-board computers (SDCs), showing that stream compaction can also benefit—important speedups can be obtained—from this kind of platforms. To do so, we derive two parallel implementations for the stream compaction problem using MPI. Then, we evaluate them considering varying number of processes and/or SDCs, as well as different input sizes. In general, we see that unless the number of elements in the stream is too small, the best results are obtained when eight MPI processes are distributed among the four SDCs that conform the cluster. To add value to the obtained results, we also consider the execution of the two parallel implementations for the stream compaction problem on a very high-performance but power-hungry 18-core Intel Xeon E5-2695 v4 multicore processor, obtaining that the Odroid C2 SDC cluster constitutes a much more efficient alternative when both resulting execution time and required energy are taken into account. Finally, we also implement and evaluate a parallel version of the stream split problem to store also the invalid elements after the valid ones. Our implementation shows good scalability on the Odroid C2 SDC cluster and more compensated computation/communication ratio when compared to the stream compaction problem.

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Technical development and socioeconomic implications of the Raspberry Pi as a learning tool in developing countries

Cited by 34 publications

References 3 publications

A low energy APRS-IS client-server infrastructure implementation using Raspberry Pi

A low energy APRS-IS client-server infrastructure implementation using Raspberry Pi

Raspberry Pi: An Effective Vehicle in Teaching the Internet of Things in Computer Science and Engineering

On the Parallelization of Stream Compaction on a Low-Cost SDC Cluster

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