Pemanfaatan Single-Board Computer pada Sistem Pengukur Suhu Ruangan : Studi Kasus Ruang Server STMIK STIKOM Bali

Server seperti layaknya perangkat elektronik lainnya, apabila sebuah perangkat bekerja maka perangkat elektronik tersebut mengeluarkan panas sebagai efek samping dari sumber daya listrik yang digunakan. Monitoring menjadi hal yang penting sebagai salah satu faktor pendukung untuk memastikan sebuah sistem berjalan dengan baik. Sistem monitoring eksternal digunakan sebagai monitoring kepada administrator server untuk mengetahui suhu ruangan server. Single-board computer adalah salah satu terobosan dalam dunia manufaktur board computer yang dimana board dapat dibuat semakin kecil dengan kemampuan yang sama tetapi dengan konsumsi daya yang lebih rendah. Dengan jenis board computer seperti ini dapat digunakan untuk berbagai keperluan. Salah satunya adalah menggabungkannya modul pengukur suhu ruangan untuk mengukur suhu ruangan. Dengan memanfaatkan GPIO, dibuat sebuah sistem sederhana untuk mengukur suhu ruangan dan menampilkan data suhu ruangan menggunakan web service

Single-Board-Computer Clusters for Cloudlet Computing in Internet of Things

The number of connected sensors and devices is expected to increase to billions in the near future. However, centralised cloud-computing data centres present various challenges to meet the requirements inherent to Internet of Things (IoT) workloads, such as low latency, high throughput and bandwidth constraints. Edge computing is becoming the standard computing paradigm for latency-sensitive real-time IoT workloads, since it addresses the aforementioned limitations related to centralised cloud-computing models. Such a paradigm relies on bringing computation close to the source of data, which presents serious operational challenges for large-scale cloud-computing providers. In this work, we present an architecture composed of low-cost Single-Board-Computer clusters near to data sources, and centralised cloud-computing data centres. The proposed cost-efficient model may be employed as an alternative to fog computing to meet real-time IoT workload requirements while keeping scalability. We include an extensive empirical analysis to assess the suitability of single-board-computer clusters as cost-effective edge-computing micro data centres. Additionally, we compare the proposed architecture with traditional cloudlet and cloud architectures, and evaluate them through extensive simulation. We finally show that acquisition costs can be drastically reduced while keeping performance levels in data-intensive IoT use cases.Ministerio de Economía y Competitividad TIN2017-82113-C2-1-RMinisterio de Economía y Competitividad RTI2018-098062-A-I00European Union’s Horizon 2020 No. 754489Science Foundation Ireland grant 13/RC/209

Single Board Computer Radiation Test Results and Radiation Test Software

Single Board Computers (SBCs) are quickly evolving and gaining capability as their cost comes down. As their footprints, cost, and power requirements decrease, their processing power increases. This makes them very attractive for use on space missions and an enabling technology as spacecraft size decreases and computational demand increases. One of the major challenges electronics face in the space environment is radiation. In 2019, the NASA Johnson Space Center (JSC) tested a selection of SBCs to low Earth orbit (LEO) radiation levels and evaluated their susceptibility and survivability. For this test campaign, JSC developed a Python software suite to better characterize the SBCs performance and intends to share the software

Build-A-Board

The purpose of this project is to build a single board computer that will be able to run Linux and provide some expansion capabilities in the form of GPIO, USB, etc. This single board computer will have all the components that a fully functional computer has but with a much smaller form factor and overall capabilities

Spacecube V2.0 Micro Single Board Computer

A single board computer system radiation hardened for space flight includes a printed circuit board having a top side and bottom side; a reconfigurable field programmable gate array (FPGA) processor device disposed on the top side; a connector disposed on the top side; a plurality of peripheral components mounted on the bottom side; and wherein a size of the single board computer system is not greater than approximately 7 cm.times.7 cm

Performance analysis of single board computer clusters

The past few years have seen significant developments in Single Board Computer (SBC) hardware capabilities. These advances in SBCs translate directly into improvements in SBC clusters. In 2018 an individual SBC has more than four times the performance of a 64-node SBC cluster from 2013. This increase in performance has been accompanied by increases in energy efficiency (GFLOPS/W) and value for money (GFLOPS/$). We present systematic analysis of these metrics for three different SBC clusters composed of Raspberry Pi 3 Model B, Raspberry Pi 3 Model B+ and Odroid C2 nodes respectively. A 16-node SBC cluster can achieve up to 60GFLOPS, running at 80W. We believe that these improvements open new computational opportunities, whether this derives from a decrease in the physical volume required to provide a fixed amount of computation power for a portable cluster; or the amount of compute power that can be installed given a fixed budget in expendable compute scenarios. We also present a new SBC cluster construction form factor named Pi Stack; this has been designed to support edge compute applications rather than the educational use-cases favoured by previous methods. The improvements in SBC cluster performance and construction techniques mean that these SBC clusters are realising their potential as valuable developmental edge compute devices rather than just educational curiosities

Next generation single board clusters

Until recently, cluster computing was too expensive and too complex for commodity users. However the phenomenal popularity of single board computers like the Raspberry Pi has caused the emergence of the single board computer cluster. This demonstration will present a cheap, practical and portable Raspberry Pi cluster called Pi Stack. We will show pragmatic custom solutions to hardware issues, such as power distribution, and software issues, such as remote updating. We also sketch potential use cases for Pi Stack and other commodity single board computer cluster architectures

Commodity single board computer clusters and their applications

© 2018 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 that makes SBC clusters viable, perhaps even expendable. These clusters are an enabler for Edge/Fog Compute, where processing is pushed out towards data sources, reducing bandwidth requirements and decentralizing the architecture. In this paper we investigate use cases driving the growth of SBC clusters, we examine the trends in future hardware developments, and discuss the potential of SBC clusters as a disruptive technology. Compared to traditional clusters, SBC clusters have a reduced footprint, are low-cost, and have low power requirements. This enables different models of deployment—particularly outside traditional data center environments. We discuss the applicability of existing software and management infrastructure to support exotic deployment scenarios and anticipate the next generation of SBC. We conclude that the SBC cluster is a new and distinct computational deployment paradigm, which is applicable to a wider range of scenarios than current clusters. It facilitates Internet of Things and Smart City systems and is potentially a game changer in pushing application logic out towards the network edge

Applying convolutional neural networks for limited-memory application

Currently, convolutional neural networks (CNN) are considered as the most effective tool in image diagnosis and processing techniques. In this paper, we studied and applied the modified SSDLite_MobileNetV2 and proposed a solution to always maintain the boundary of the total memory capacity in the following robust bound and applied on the bridge navigational watch & alarm system (BNWAS). The hardware was designed based on raspberry Pi-3, an embedded single board computer with CPU smartphone level, limited RAM without CUDA GPU. Experimental results showed that the deep learning model on an embedded single board computer brings us high effectiveness in application