scholarly journals Elemental: An Open-Source Wireless Hardware and Software Platform for Building Energy and Indoor Environmental Monitoring and Control

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 4017 ◽  
Author(s):  
Akram Syed Ali ◽  
Christopher Coté ◽  
Mohammad Heidarinejad ◽  
Brent Stephens
Keyword(s):  
Environmental Monitoring ◽  
Open Source ◽  
Environmental Quality ◽  
Printed Circuit Boards ◽  
Operating Conditions ◽  
Sensor Data ◽  
Raspberry Pi ◽  
Software Platform ◽  
Energy Usage ◽  
Efficient Operation

This work demonstrates an open-source hardware and software platform for monitoring the performance of buildings, called Elemental, that is designed to provide data on indoor environmental quality, energy usage, HVAC operation, and other factors to its users. It combines: (i) custom printed circuit boards (PCBs) with RFM69 frequency shift keying (FSK) radio frequency (RF) transceivers for wireless sensors, control nodes, and USB gateway, (ii) a Raspberry Pi 3B with custom firmware acting as either a centralized or distributed backhaul, and (iii) a custom dockerized application for the backend called Brood that serves as the director software managing message brokering via Message Queuing Telemetry Transport (MQTT) protocol using VerneMQ, database storage using InfluxDB, and data visualization using Grafana. The platform is built around the idea of a private, secure, and open technology for the built environment. Among its many applications, the platform allows occupants to investigate anomalies in energy usage, environmental quality, and thermal performance via a comprehensive dashboard with rich querying capabilities. It also includes multiple frontends to view and analyze building activity data, which can be used directly in building controls or to provide recommendations on how to increase operational efficiency or improve operating conditions. Here, we demonstrate three distinct applications of the Elemental platform, including: (1) deployment in a research lab for long-term data collection and automated analysis, (2) use as a full-home energy and environmental monitoring solution, and (3) fault and anomaly detection and diagnostics of individual building systems at the zone-level. Through these applications we demonstrate that the platform allows easy and virtually unlimited datalogging, monitoring, and analysis of real-time sensor data with low setup costs. Low-power sensor nodes placed in abundance in a building can also provide precise and immediate fault-detection, allowing for tuning equipment for more efficient operation and faster maintenance during the lifetime of the building.

scholarly journals Configurable IoT Open-Source Hardware and Software I-V Curve Tracer for Photovoltaic Generators

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7650
Author(s):  
Isaías González ◽  
José María Portalo ◽  
Antonio José Calderón
Keyword(s):  
Data Acquisition ◽  
Open Source ◽  
Smart Grids ◽  
Operating Conditions ◽  
Raspberry Pi ◽  
Modern Trend ◽  
Curve Tracer ◽  
Open Source Hardware ◽  
Curve Tracing ◽  
Photovoltaic Generators

Photovoltaic (PV) energy is a renewable energy resource which is being widely integrated in intelligent power grids, smart grids, and microgrids. To characterize and monitor the behavior of PV modules, current-voltage (I-V) curves are essential. In this regard, Internet of Things (IoT) technologies provide versatile and powerful tools, constituting a modern trend in the design of sensing and data acquisition systems for I-V curve tracing. This paper presents a novel I-V curve tracer based on IoT open-source hardware and software. Namely, a Raspberry Pi microcomputer composes the hardware level, whilst the applied software comprises mariaDB, Python, and Grafana. All the tasks required for curve tracing are automated: load sweep, data acquisition, data storage, communications, and real-time visualization. Modern and legacy communication protocols are handled for seamless data exchange with a programmable logic controller and a programmable load. The development of the system is expounded, and experimental results are reported to prove the suitability and validity of the proposal. In particular, I-V curve tracing of a monocrystalline PV generator under real operating conditions is successfully conducted.

Democratizing ocean technology: low-cost innovations in underwater robotics

2020 ◽  
Author(s):  
Allison Chua ◽  
Aaron MacNeill ◽  
Douglas Wallace
Keyword(s):  
Open Source ◽  
Supply And Demand ◽  
Academic Community ◽  
Circuit Board ◽  
Sensor Data ◽  
Raspberry Pi ◽  
Common Source ◽  
Underwater Robotics ◽  
Ocean Science ◽  
Wide Range

<p>In comparison to the ocean’s immense volume and diversity of research areas, the number of sensors required to make the majority of desired measurements is quite small. This inequality of supply and demand elevates prices, adding further barriers for developing nations or fledgling research programs with smaller budgets attempting ocean science. Our work aims to demonstrate the potential of combining commercially available, open-source products to create inexpensive, configurable, and user-friendly platforms that can be adapted for underwater navigation and integration with most commercial oceanographic sensors.</p><p>Specifically, we will highlight modifications made to a Blue Robotics BlueROV2, which we have configured for various missions including vertical profiling of a coastal fjord and three-dimensional mapping of crude oil spills. The BlueROV2 offers an easily modified platform for physical mounting of sensors and streaming of sensor data via its onboard computer, a Raspberry Pi. Our custom circuit board is “sensor-agnostic”, powering sensors from a common source (the ROV battery) and using an Arduino that accepts analog or digital sensor inputs, allowing us to choose from a wide range of sensors. Physical modifications make use of inexpensive, readily available materials, and range from simple plastic brackets for small sensors to a skid for a sensor with half the ROV’s original weight, which utilizes pop bottles for buoyancy.</p><p>While products such as Pixhawk, Raspberry Pi, Arduino, and BlueROV have inspired hobbyists and youth around the world, they paradoxically have not been as widely embraced in the academic community, who perhaps remain unaware of their research potential. Thus, while there has yet to be an analogous push to develop inexpensive, small, power-efficient, and open-source sensors, these platforms offer exciting opportunities to build a new generation of oceanographic tools with measurement abilities far exceeding those of their predecessors. We are at an ocean technology tipping point, and, as MacGyver says, “With a little bit of imagination, anything is possible.”</p>

scholarly journals Pengembangan Sistem Streaming Sensor Data Temperatur dan Kelembapan di Ruang Laboratorium BBTA3 Melalui Teknologi Open Source

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Arizal Akbar Zikri
Keyword(s):  
Open Source ◽  
Streaming Data ◽  
Sensor Data ◽  
Raspberry Pi ◽  
Data Streaming ◽  
Application Development ◽  
Web Browser ◽  
Streaming Application ◽  
To Receive ◽  
Data Reading

AbstractDevelopment of temperature and humidity data streaming Application from sensors in the server room is necessary to enable the operator to monitor the real-time air condition in the server room, without having to enter the room. This application development utilizes open source technology to make developers more independent and able to interact easily within the community without license hassle.Sensor data reading is done by Raspberry Pi, assigned as a producer in sending data to Kafka Cluster. Kafka is an open source technology used as tools for streaming data distributedly. One node in a cluster is set to receive sensor data, known as consumer, executes python service to handle requests from users through Server Sent Event (SSE) in form of REST API.This application is called TempHum and can be executed on Desktop (Windows, Linux, Mac OS), web browser, and smartphone (Android and iOS). Hence, the application can serve many clients in monitoring air condition in realtime.Keywords: open source, cluster, raspberry pi, kafka, python.AbstraksAplikasi streaming data sensor berupa temperatur dan kelembapan di ruang server perlu dikembangkan, sehingga memudahkan bagi operator untuk memantau kondisi udara terkini secara dinamis di ruang server tanpa harus masuk kedalam ruang tersebut. Pengembangan aplikasi dilakukan menggunakan teknologi open source agar memudahkan pengembang untuk mandiri dan berinteraksi dalam komunitas tanpa terikat dengan permasalahan lisensi.Pembacaan data sensor dilakukan oleh Raspberry Pi dan dijadikan sebagai producer untuk mengirimkan data tersebut ke Kafka Cluster. Kafka merupakan teknologi open source yang digunakan sebagai alat untuk streaming data terdistribusi. Satu node dalam cluster digunakan untuk menerima kiriman data atau dikenal sebagai consumer sekaligus menjalankan python servis untuk menangani permintaan dari pengguna aplikasi melalui Server Sent Event (SSE) dalam bentuk REST API.Aplikasi ini diberi nama TempHum dan dapat dijalankan di Desktop (Windows, Linux, Mac OS), web browser, dan smartphone (Android dan iOS), sehingga aplikasi ini dapat melayani banyak pengguna dalam memantau kondisi ruang server secara dinamis.Kata Kunci : open source, cluster, raspberry pi, kafka, python. 

"Recent Patents on the Triboelectrostatic Separation of Fly Ash"

2019 ◽  
Vol 13 ◽  
Author(s):  
Haisheng Li ◽  
Wenping Wang ◽  
Yinghua Chen ◽  
Xinxi Zhang ◽  
Chaoyong Li
Keyword(s):  
Fly Ash ◽  
Power Plants ◽  
High Efficiency ◽  
Separation Efficiency ◽  
High Reliability ◽  
Operating Conditions ◽  
Security Requirements ◽  
Unburned Carbon ◽  
Efficient Operation ◽  
Triboelectrostatic Separation

Background: The fly ash produced by coal-fired power plants is an industrial waste. The environmental pollution problems caused by fly ash have been widely of public environmental concern. As a waste of recoverable resources, it can be used in the field of building materials, agricultural fertilizers, environmental materials, new materials, etc. Unburned carbon content in fly ash has an influence on the performance of resource reuse products. Therefore, it is the key to remove unburned carbon from fly ash. As a physical method, triboelectrostatic separation technology has been widely used because of obvious advantages, such as high-efficiency, simple process, high reliability, without water resources consumption and secondary pollution. Objective: The related patents of fly ash triboelectrostatic separation had been reviewed. The structural characteristics and working principle of these patents are analyzed in detail. The results can provide some meaningful references for the improvement of separation efficiency and optimal design. Methods: Based on the comparative analysis for the latest patents related to fly ash triboelectrostatic separation, the future development is presented. Results: The patents focused on the charging efficiency and separation efficiency. Studies show that remarkable improvements have been achieved for the fly ash triboelectrostatic separation. Some patents have been used in industrial production. Conclusion: According to the current technology status, the researches related to process optimization and anti-interference ability will be beneficial to overcome the influence of operating conditions and complex environment, and meet system security requirements. The intelligent control can not only ensure the process continuity and stability, but also realize the efficient operation and management automatically. Meanwhile, the researchers should pay more attention to the resource utilization of fly ash processed by triboelectrostatic separation.

scholarly journals Integrated multi-modality image-guided navigation for neurosurgery: open-source software platform using state-of-the-art clinical hardware

2021 ◽  
Author(s):  
Jonathan Shapey ◽  
Thomas Dowrick ◽  
Rémi Delaunay ◽  
Eleanor C. Mackle ◽  
Stephen Thompson ◽  
...  
Keyword(s):  
Skull Base ◽  
Open Source ◽  
Open Source Software ◽  
Skull Base Surgery ◽  
Patient Specific ◽  
Software Platform ◽  
Image Guided ◽  
Phantom Model ◽  
Technical Requirements ◽  
Navigated Ultrasound

Abstract Purpose Image-guided surgery (IGS) is an integral part of modern neuro-oncology surgery. Navigated ultrasound provides the surgeon with reconstructed views of ultrasound data, but no commercial system presently permits its integration with other essential non-imaging-based intraoperative monitoring modalities such as intraoperative neuromonitoring. Such a system would be particularly useful in skull base neurosurgery. Methods We established functional and technical requirements of an integrated multi-modality IGS system tailored for skull base surgery with the ability to incorporate: (1) preoperative MRI data and associated 3D volume reconstructions, (2) real-time intraoperative neurophysiological data and (3) live reconstructed 3D ultrasound. We created an open-source software platform to integrate with readily available commercial hardware. We tested the accuracy of the system’s ultrasound navigation and reconstruction using a polyvinyl alcohol phantom model and simulated the use of the complete navigation system in a clinical operating room using a patient-specific phantom model. Results Experimental validation of the system’s navigated ultrasound component demonstrated accuracy of $$<4.5\,\hbox {mm}$$ < 4.5 mm and a frame rate of 25 frames per second. Clinical simulation confirmed that system assembly was straightforward, could be achieved in a clinically acceptable time of $$<15\,\hbox {min}$$ < 15 min and performed with a clinically acceptable level of accuracy. Conclusion We present an integrated open-source research platform for multi-modality IGS. The present prototype system was tailored for neurosurgery and met all minimum design requirements focused on skull base surgery. Future work aims to optimise the system further by addressing the remaining target requirements.

Using Machine Learning for Dependable Outlier Detection in Environmental Monitoring Systems

2021 ◽  
Vol 5 (3) ◽  
pp. 1-30
Author(s):  
Gonçalo Jesus ◽  
António Casimiro ◽  
Anabela Oliveira
Keyword(s):  
Machine Learning ◽  
Environmental Monitoring ◽  
Data Quality ◽  
Outlier Detection ◽  
Prediction Models ◽  
Sensor Data ◽  
Natural Phenomenon ◽  
Monitoring Systems ◽  
Data Errors ◽  
Redundant Data

Sensor platforms used in environmental monitoring applications are often subject to harsh environmental conditions while monitoring complex phenomena. Therefore, designing dependable monitoring systems is challenging given the external disturbances affecting sensor measurements. Even the apparently simple task of outlier detection in sensor data becomes a hard problem, amplified by the difficulty in distinguishing true data errors due to sensor faults from deviations due to natural phenomenon, which look like data errors. Existing solutions for runtime outlier detection typically assume that the physical processes can be accurately modeled, or that outliers consist in large deviations that are easily detected and filtered by appropriate thresholds. Other solutions assume that it is possible to deploy multiple sensors providing redundant data to support voting-based techniques. In this article, we propose a new methodology for dependable runtime detection of outliers in environmental monitoring systems, aiming to increase data quality by treating them. We propose the use of machine learning techniques to model each sensor behavior, exploiting the existence of correlated data provided by other related sensors. Using these models, along with knowledge of processed past measurements, it is possible to obtain accurate estimations of the observed environment parameters and build failure detectors that use these estimations. When a failure is detected, these estimations also allow one to correct the erroneous measurements and hence improve the overall data quality. Our methodology not only allows one to distinguish truly abnormal measurements from deviations due to complex natural phenomena, but also allows the quantification of each measurement quality, which is relevant from a dependability perspective. We apply the methodology to real datasets from a complex aquatic monitoring system, measuring temperature and salinity parameters, through which we illustrate the process for building the machine learning prediction models using a technique based on Artificial Neural Networks, denoted ANNODE ( ANN Outlier Detection ). From this application, we also observe the effectiveness of our ANNODE approach for accurate outlier detection in harsh environments. Then we validate these positive results by comparing ANNODE with state-of-the-art solutions for outlier detection. The results show that ANNODE improves existing solutions regarding accuracy of outlier detection.

scholarly journals Implementation of a Six-Layer Smart Factory Architecture with Special Focus on Transdisciplinary Engineering Education

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2944
Author(s):  
Benjamin James Ralph ◽  
Marcel Sorger ◽  
Benjamin Schödinger ◽  
Hans-Jörg Schmölzer ◽  
Karin Hartl ◽  
...  
Keyword(s):  
Open Source ◽  
User Interfaces ◽  
Industrial Revolution ◽  
Low Cost ◽  
Material Behavior ◽  
Sensor Data ◽  
Management Tool ◽  
Special Focus ◽  
Programming Environments ◽  
Metal Processing

Smart factories are an integral element of the manufacturing infrastructure in the context of the fourth industrial revolution. Nevertheless, there is frequently a deficiency of adequate training facilities for future engineering experts in the academic environment. For this reason, this paper describes the development and implementation of two different layer architectures for the metal processing environment. The first architecture is based on low-cost but resilient devices, allowing interested parties to work with mostly open-source interfaces and standard back-end programming environments. Additionally, one proprietary and two open-source graphical user interfaces (GUIs) were developed. Those interfaces can be adapted front-end as well as back-end, ensuring a holistic comprehension of their capabilities and limits. As a result, a six-layer architecture, from digitization to an interactive project management tool, was designed and implemented in the practical workflow at the academic institution. To take the complexity of thermo-mechanical processing in the metal processing field into account, an alternative layer, connected with the thermo-mechanical treatment simulator Gleeble 3800, was designed. This framework is capable of transferring sensor data with high frequency, enabling data collection for the numerical simulation of complex material behavior under high temperature processing. Finally, the possibility of connecting both systems by using open-source software packages is demonstrated.

scholarly journals Improvement Effect of Green Remodeling and Building Value Assessment Criteria for Aging Public Buildings

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1200
Author(s):  
Yong-Joon Jun ◽  
Seung-ho Ahn ◽  
Kyung-Soon Park
Keyword(s):  
High Efficiency ◽  
Energy Performance ◽  
Operating Conditions ◽  
Energy Usage ◽  
Public Buildings ◽  
Value Judgment ◽  
Relationship Analysis ◽  
Improvement Effect ◽  
The Relationship ◽  
Usage Data

The Green Remodeling Project under South Korea’s Green New Deal policy is a government-led project intended to strengthen the performance sector directly correlated with energy performance among various elements of improvement applicable to building remodeling by replacing insulation materials, introducing new and renewable energy, introducing high-efficiency equipment, etc., with public buildings taking the lead in green remodeling in order to induce energy efficiency enhancement in private buildings. However, there is an ongoing policy that involves the application of a fragmentary value judgment criterion, i.e., whether to apply technical elements confined to the enhancement of the energy performance of target buildings and the prediction of improvement effects according thereto, thus resulting in the phenomenon of another important value criterion for green remodeling, i.e., the enhancement of the occupant (user) comfort performance of target buildings as one of its purposes, being neglected instead. In order to accurately grasp the current status of these problems and to promote ‘expansion of the value judgment criteria for green remodeling’ as an alternative, this study collected energy usage data of buildings actually used by public institutions and then conducted a total analysis. After that, the characteristics of energy usage were analyzed for each of the groups of buildings classified by year of completion, thereby carrying out an analysis of the correlation between the non-architectural elements affecting the actual energy usage and the actual energy usage data. The correlation between the improvement performance of each technical element and the actual improvement effect was also analyzed, thereby ascertaining the relationship between the direction of major policy strategies and the actual energy usage. As a result of the relationship analysis, it was confirmed that the actual energy usage is more affected by the operating conditions of the relevant building than the application of individual strategic elements such as the performance of the envelope insulation and the performance of the high-efficiency system. In addition, it was also confirmed that the usage of public buildings does not increase in proportion to their aging. The primary goal of reducing energy usage in target buildings can be achieved if public sector (government)-led green remodeling is pushed ahead with in accordance with biased value judgment criteria, just as in the case of a campaign to refrain from operating cooling facilities in aging public buildings. However, it was possible to grasp through the progress of this study that the remodeling may also result in the deterioration of environmental comfort and stability, such as the numerical value of the indoor thermal environment. The results of this study have the significance of providing basic data for pushing ahead with a green remodeling policy in which the value judgment criteria for aging existing public buildings are more expanded, and it is necessary to continue research in such a direction that the quantitative purpose of green remodeling, which is to reduce energy usage in aging public buildings, and its qualitative purpose, which is to enhance their environmental performance for occupants’ comfort, can be mutually balanced and secured at the same time.

scholarly journals Monitoring System for Tracking a PV Generator in an Experimental Smart Microgrid: An Open-Source Solution

2021 ◽  
Vol 13 (15) ◽  
pp. 8182
Author(s):  
José María Portalo ◽  
Isaías González ◽  
Antonio José Calderón
Keyword(s):  
Open Source ◽  
Monitoring System ◽  
Smart Grids ◽  
Raspberry Pi ◽  
Operator Interface ◽  
Open Source Hardware ◽  
Real Time Information ◽  
And Storage ◽  
Time Information ◽  
Pv Generator

Smart grids and smart microgrids (SMGs) require proper monitoring for their operation. To this end, measuring, data acquisition, and storage, as well as remote online visualization of real-time information, must be performed using suitable equipment. An experimental SMG is being deployed that combines photovoltaics and the energy carrier hydrogen through the interconnection of photovoltaic panels, electrolyser, fuel cell, and load around a voltage bus powered by a lithium battery. This paper presents a monitoring system based on open-source hardware and software for tracking the temperature of the photovoltaic generator in such an SMG. In fact, the increases in temperature in PV modules lead to a decrease in their efficiency, so this parameter needs to be measured in order to monitor and evaluate the operation. Specifically, the developed monitoring system consists of a network of digital temperature sensors connected to an Arduino microcontroller, which feeds the acquired data to a Raspberry Pi microcomputer. The latter is accessed by a cloud-enabled user/operator interface implemented in Grafana. The monitoring system is expounded and experimental results are reported to validate the proposal.

Sign in / Sign up

Export Citation Format

Share Document