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Raspberry Pi units sold (all versions), according to statistics published by the official Raspberry Pi blog.
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![Fig. 2. A selection of SBC clusters. (a) Iridis Pi [3] built using...](publication/326085229/figure/fig2/AS:864406321758213@1583101935653/A-selection-of-SBC-clusters-a-Iridis-Pi-3-built-using-Lego-b-Mythic-Beasts-21_Q320.jpg)
Current commodity Single Board Computers (SBCs) are sufficiently powerful to run mainstream operating systems and workloads. Many of these boards may be linked together, to create small, low-cost clusters that replicate some features of
large data center clusters. The Raspberry Pi Foundation produces a series of
SBCs with a price/performance ratio...
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... were other SBCs, such as the Gumstix [1] and the BeagleBone, that had similar technical specifications (although they were more expensive) [8]. Despite this, it is the Raspberry Pi that has come to lead the market. Selling over a million units in the first year, the Raspberry Pi Foundation became the fastest growing computing company to date [2]. Fig. 1 shows the sales figures for Raspberry Pi units, based on statistics published by the official Raspberry Pi blog, and in March 2017 the Raspberry Pi became the third best-selling general purpose computer of all time [12]. From the early days of the Linux capable SBC, when the list of available boards was extremely limited, there is now ...
Citations
... Technologies and methods that enable automated and cost-effective inventory tracking are desperately needed in response to these issues, as well as in keeping with ongoing technical advancements and digital transformation. Single-board computers (SBCs) are a powerful and reasonably priced computing solution that can completely transform SMEs' inventory management [6]. An SBC is a printed circuit board (PCB) computer that has a CPU, memory, input/output, and additional functionality [6]. ...
... Single-board computers (SBCs) are a powerful and reasonably priced computing solution that can completely transform SMEs' inventory management [6]. An SBC is a printed circuit board (PCB) computer that has a CPU, memory, input/output, and additional functionality [6]. Conventional warehouses may be able to leverage digital transformation in this way. ...
Industry 4.0's pervasive digital ideas have completely changed how businesses operate. A lot of big businesses have switched to digital systems. However, due to a lack of funding and digital infrastructure, SME sector digitalization issues still exist. Inventory management is one of the many manual methods that many SMEs still have to rely on, which makes it prone to error. To manage inventories more accurately and effectively, manual systems must be changed. The goal of this project is to improve inventory management efficiency by creating a dependable and affordable automated system. A cloud database is connected to the system to provide quick inventory monitoring on warehouse shelves. Prototyping and statistical testing are used in the study technique to evaluate the system's dependability. Using an Internet of Things-based single-board computer, the research creates an inventory monitoring system. With a daily implementation cost of IDR 642.92, the system is deemed cost-effective based on inventory monitoring results on warehouse shelves. Because the system's performance reached a Cronbach's alpha value greater than 0.8, the inventory management system used in the study is regarded as dependable for application in real-world systems to increase inventory management accuracy
... Recentemente, os computadores single board, definidos como computadores completos em uma única placa, têm se destacado devido ao seu baixo custo, ao pequeno consumo de energia e ao razoável poder computacional. As plataformas Raspberry Pi e Beaglebone Black [Johnston et al. 2018 Este trabalho escolheu a plataforma Orange Pi Pc Plus, como single board para a execução do software de decodificação ADS-B empregando o Linux. Além disso, realizou-se a análise do desempenho dessa plataforma durante a execução do monitoramento. ...
O tráfego aéreo tem crescido cada vez mais e a tecnologia ADS-B (Automatic Dependent Surveillance - Broadcast) se mostra uma importante ferramenta para auxiliar o funcionamento do setor. Entretanto, dispositivos proprietários de elevado custo são usados, limitando a adoção da tecnologia por usuários com menos recursos financeiros. Com o objetivo de fornecer uma solução de mais baixo custo, este trabalho desenvolveu e realizou a análise de desempenho de um sistema de código aberto para monitoramento aéreo baseado na tecnologia ADS-B, aplicado a hardware single board. Os resultados mostraram que o sistema single board apresentou bom desempenho quando comparado à plataforma de propósito geral usada para comparação.
... A network like this would be extremely expensive to set up, requiring physical hardware connections between all machines. However, software methods that exist that allow you to create virtual networks over current commodity Internet connections without the need for costly gear or dedicated support staff [33]. ...
... These physical characteristics of a SBC describe it as extensive. A user of an SBC can improve its usefulness by linking it with other devices through the given I/O ports, mainly: I2c, SPI, UART, USB RG-45, and GPIO pins, [1]. ...
... A set of single-board computers (SBCs), deemed proficient in AI and DL within this article, are presented without a specific order. The primary goal of this research article is to offer comprehensive insights into implementing embedded AI and DL solutions, avoiding confinement to particular SBCs that may not have achieved the necessary functional maturity, [1]. The rest of the paper is arranged as follows: Section 2 gives an overview of AI, ML, and DL. ...
This research introduces a comprehensive study of the most robust Single-Board Computers (SBCs) implemented recently, where most of them are built on the system-on-chip architecture. This study also presents the main characteristics of each of these SBCs, as well as their prices and applications. Additionally, the study reviews some machine learning (ML) and deep learning (DL) techniques, exploring their implementation on SBCs. Finally, it displays some software tools on how to implement DL and ML projects on SBCs.
... Over the past decade, Apache Hadoop has become a leading framework for big data processing [3]. The Hadoop framework has established itself as a key player, allowing distributed computing across a wide range of nodes. ...
... This limits the execution on the frugal RPi devices in the cluster to only one container, ensuring that the application does not crash. A similar observation is also made by the authors in [3], where the authors run into similar issues with regards to memory management. To alleviate this restriction, one approach is to increase the size of the swap partition on the host operating system to maximize the utilization of the virtual memory; however, this resulted in slower performance due to the significantly slow read/write speeds on the local storage media (SD cards). ...
Single-board computers (SBCs) are emerging as an efficient and economical solution for fog and edge computing, providing localized big data processing with lower energy consumption. Newer and faster SBCs deliver improved performance while still maintaining a compact form factor and cost-effectiveness. In recent times, researchers have addressed scheduling issues in Hadoop-based SBC clusters. Despite their potential, traditional Hadoop configurations struggle to optimize performance in heterogeneous SBC clusters due to disparities in computing resources. Consequently, we propose modifications to the scheduling mechanism to address these challenges. In this paper, we leverage the use of node labels introduced in Hadoop 3+ and define a Frugality Index that categorizes and labels SBC nodes based on their physical capabilities, such as CPU, memory, disk space, etc. Next, an adaptive configuration policy modifies the native fair scheduling policy by dynamically adjusting resource allocation in response to workload and cluster conditions. Furthermore, the proposed frugal configuration policy considers prioritizing the reduced tasks based on the Frugality Index to maximize parallelism. To evaluate our proposal, we construct a 13-node SBC cluster and conduct empirical evaluation using the Hadoop CPU and IO intensive microbenchmarks. The results demonstrate significant performance improvements compared to native Hadoop FIFO and capacity schedulers, with execution times 56% and 22% faster than the best_cap and best_fifo scenarios. Our findings underscore the effectiveness of our approach in managing the heterogeneous nature of SBC clusters and optimizing performance across various hardware configurations.
... Over the past decade, Apache Hadoop has become a leading framework for big data processing [9]. Hadoop, a robust framework designed for distributed storage and processing of vast datasets, serves as a cornerstone in fostering sustainability initiatives across diverse domains. ...
... Its distributed computing model enhances energy efficiency by enabling parallel processing of data across multiple nodes within a cluster. Lately, researchers in [9][10][11][12][13][14] have directed their attention towards achieving energy-efficient remote data processing through the utilization of clusters comprised of single-board computers (SBCs) like Raspberry Pi, coupled with the Hadoop framework for handling large-scale data processing tasks in various context including agriculture, smart cities, smart homes, healthcare etc. Qureshi et. al. in [11] developed a heterogenous cluster of 20 SBCs including Raspberry Pis and Ordoid Xu-4 for data analytics using Hadoop. ...
... This approach ensures optimal resource utilization and enhances the overall efficiency of the system by intelligently allocating Map tasks based on the varying capacities of individual nodes within the heterogenous SBC based cluster. 9 ...
In the dynamic landscape of sustainable computing, use of edge devices is paramount for reducing the need for large-scale centralized data centers. By processing data locally, edge devices minimize the energy-intensive computing in data centers, improving the overall performance, cost-effectiveness whereas reducing the environmental impact. Edge devices may constitute edge clusters composed of resource frugal Single Board Computers (SBC) such as Raspberry Pi etc. The small form-factor and energy efficiency of these computers makes them ideal for processing large data on the edge. Despite their potential, traditional Hadoop configurations struggle to optimize performance in heterogeneous SBC clusters due to disparities in computing resources. Consequently, we propose modifications to the Yet Another Resource Negotiator (YARN) scheduling mechanism to address these challenges. Our proposed changes include the introduction of a Frugality Index and an adaptiveConfig policy. The Frugality Index categorizes SBC nodes based on their capabilities, enabling intelligent resource allocation. The adaptiveConfig policy dynamically adjusts resource allocation in response to workload and cluster conditions, enhancing system efficiency. Additionally, we introduce a fetch_threshold for reduce tasks to improve task prioritization based on locality and data processing efficiency. We evaluate our approach using a 13-node SBC cluster and conduct experiments with CPU-intensive and IO-intensive Hadoop benchmarks. The results demonstrate significant performance improvements compared to native YARN settings, with execution times 4.7 times faster than the worst_native and 1.9 times faster than the best_native scenarios. Furthermore, the proposed adaptiveConfig policy implementing the frugality index and a fetch_threshold outperforms the native YARN by 5.86 times and 1.79 times in Terasort and wordcount executions respectively. Our findings underscore the effectiveness of our approach in managing the heterogeneous nature of SBC clusters and optimizing performance across various hardware configurations. The adaptive policies prove well-suited to the frugal SBC-cluster context, yielding enhanced outcomes and paving the way for sustainable big data processing initiatives.
... Single-board architectures (SBAs) for computational (SBCs) and microcontroller (SBMs) purposes began popular and interesting solutions in the last decade, their due to low cost and low consumption, small size, and great flexibility, which makes them an alternative in many applications [1], [2]. Included onboard innumerable integrating sensors and state-of-art of communication technologies that are increasingly used for do-it-yourself (DIY) projects, internet of things (IoT) devices in the field of science, technology, engineering, educational and academic project [3]- [5]. ...
... Figure 4, parameters can be calculated to reflect of performance of the classification model i.e. The accuracy describes how accurate the model is to correctly classify the object, as in (2). The precision describes how precise the model predicts requested data, as in (3). ...
span lang="EN-US">Implementation of artificial intelligence tends to be portable, mobile and embeds in embedded computer system (EBD). EBD is a special-purpose computer with limited capacity in a small-form size. Deep learning (DL) had known as cutting edges for object recognition. With DL, object feature extraction analysis is omitted. DL requires large computing resources and capacity. Implement DL algorithm on EBD goal to achieves high detection accuracy and high-efficiency resources. Hence, be able to cope with intra-class variations, and image disturbances. By those challenges and limitations, this study reports the performance of EBD to recognize an object which has high variations in their class, through an optimal raw-input dataset. The raw-input dataset performed optimization process with a supervisor. Yield is the proper optimal input dataset in size. The performance results observed begin from training dataset until evaluation stage of DL. The comparison performs in efficiency resources, loss, validation-loss, timesteps, and detection accuracy by multiclass confusion matrix analysis. This study shows through this purpose method efficient resources are highly archived. Shorter timesteps ensure training stage is successful, and detection accuracy is perfectly archived. In addition, this study proves DL method archived great performances in classifying object that has identical structure.</span
... A SBC is a self-contained computing device that is constructed on a solitary circuit board, including essential components such as a microprocessor, memory, Input/Output (I/O) interfaces, and other requisite elements to enable its functionality as a fully operational computer [5], [6]. However, this definition must fully cover the critical differences between SBC and other platforms: The factors that need to be considered are the accessibility of generalpurpose input/output (I/O) ports, the level of power consumption, and the associated costs. ...
This Research examines how open educational resources (OER) and open educational practices (OEP) provide fair access to high-quality education. Using qualitative research, the author evaluates university formal education OER and OEP deployment and outcomes. This research found that OER, CDIO (Conceive – Design – Implement – Operate), and single-boar computer (SBC) learning media increase curricular content and instruction. Open education encourages education community cooperation and interaction. However, copyright, quality assurance, and curricular integration persist. This Research also explores the CDIO framework to promote OER and OEP, suggesting that these four elements may alter education.
... The brain of the ICA platform is a single-board computer (SBC). An SBC consists of the central processing unit (CPU), random access memory (RAM), solid-state storage (SSS), and peripheral ports combined into a small form factor printed circuit board (PCB) [23][24][25][26][27]. The main two candidates for SBC were Nvidia Jetson Nano 4GB [28][29][30][31] and Raspberry Pi 4 Model B 8GB [32][33][34][35]. ...
Intelligent compaction (IC) is a technology that uses non-contact sensors to monitor and record the compaction level of geomaterials in real-time during road construction. However, current IC devices have several limitations: (i) they are unable to visualize or compare multiple intelligent compaction measurement values (ICMVs) in real-time during compaction; (ii) they are not retrofittable to different conventional rollers that exist in the field; (iii) they do not incorporate corrections for ICMVs reflecting variable field conditions; (iv) they are unable to integrate construction specifications as needed for performance-based compaction; and (v) they do not record all the key roller parameters for further compaction analysis. To address these issues, an innovative retrofittable platform with cutting-edge hardware and software was developed. This platform, called the intelligent compaction analyzer (ICA) platform, is effective at calculating conventional acceleration amplitude-based ICMVs and stiffness-based parameters and at displaying the spatial distributions of these parameters in a colour-coded map in real-time during compaction.
... Several studies have proposed the adoption of commodity Single-Board Computer (SBC) clusters as a promising alternative to conventional data centers. The authors in [2] sustain that the use of low consumption devices such as SBC's can be an option to minimize the infrastructure problems of data centers. A single board can encapsulate all the resources of a functional computer and with relatively good processing power that, when interconnected as a cluster, can replicate characteristics of large data centers. ...
The constant growth of social media, unconventional web technologies, mobile applications, and Internet of Things (IoT) devices create challenges for cloud data systems in order to support huge datasets and very high request rates. NoSQL databases, such as Cassandra and HBase, and relational SQL databases with replication, such as Citus/PostgreSQL, have been used to increase horizontal scalability and high availability of data store systems. In this paper, we evaluated three distributed databases on a low-power low-cost cluster of commodity Single-Board Computers (SBC): relational Citus/PostgreSQL and NoSQL databases Cassandra and HBase. The cluster has 15 Raspberry Pi 3 nodes with Docker Swarm orchestration tool for service deployment and ingress load balancing over SBCs. We believe that a low-cost SBC cluster can support cloud serving goals such as scale-out, elasticity, and high availability. Experimental results clearly demonstrated that there is a trade-off between performance and replication, which provides availability and partition tolerance. Besides, both properties are essential in the context of distributed systems with low-power boards. Cassandra attained better results with its consistency levels specified by the client. Both Citus and HBase enable consistency but it penalizes performance as the number of replicas increases.
