Nandor Verba - Profile on Academia.edu (original) (raw)
Papers by Nandor Verba
Modelling industry 4.0 based fog computing environments for application analysis and deployment
EPSRC-funded Humanitarian Engineering and Energy for Displacement (HEED) datasets
This repository contains raw datasets gathered under the EPSRC-funded Humanitarian Engineering an... more This repository contains raw datasets gathered under the EPSRC-funded Humanitarian Engineering and Energy for Displacement (HEED) research project (EP/P029531/1). The project aimed to understand energy needs of displaced communities by creating an evidence base on the usage of seven different energy interventions, and provide recommendations for improved design of future energy interventions to better meet the needs of people. Below is a brief description of the interventions. Stove-use monitoring systems (SUMs) - SUMs were deployed on clay stoves in Kigeme camp, Rwanda in July 2019. The aim of the study was to evaluate stove usage patterns by measuring temperature profiles within stove enclosure and on the surface of stoves. The SUMs consisted of 2 sensors - a thermocouple to measure temperature within the stove and a Si7021 sensor to measure temperature outside the stove, connected to an Arduino MKR GSM 1400 board. The data measured by the sensors was stored only if the change in ...
A community energy management system for smart microgrids
Electric Power Systems Research
Coventry University, 2020
The concepts of Industry 4.0 provide a new means of integrating concepts from ubiquitous computin... more The concepts of Industry 4.0 provide a new means of integrating concepts from ubiquitous computing with manufacturing technologies through cybernetics. This advances the automation of the manufacturing systems and helps improve product quality, production efficiency, condition monitoring and decision making (J. Lee, Bagheri, and Kao 2015; DIN 2016). Within this concept, machines become connected with humans through computer systems to work in a coordinated way to automate data acquisition, sharing and exchange among the physical and virtual worlds. The wide spread availability and affordability of sensors, wireless networks and the accessibility of high-speed Internet make real-time multiple parameters monitoring and control of manufacturing process possible in a way that was not feasible before (Y. Lu 2017). This leads to a great number of sensors being deployed to physical machines which in turn generates a large volume of data that requires computationally intensive analysis and interpretation for decision-making purposes. The resulting decisions, whether made by humans or software, often need to be transformed into control signals for actuators to operate the machine in the physical world. This then creates a loop-back to the sensor system as new sets of data are collected and sent back for further analysis, reflecting changing machine states over time. This type of system based on Cyber-Physical System (CPS) is a facilitator for realising the concepts of Industry 4.0. It enables computational algorithms and physical components to interact with each other through real-time monitoring and control to improve productivity (Trappey et al. 2016; L. Wang, Törngren, and Onori 2015). Yet, as stated in (Wiesner, Marilungo, and Thoben 2017) traditional servers with limited capacities may not be able to cope with the new challenges in terms of scalability and complexity of such systems. In turn, 1.3 Research Aims and Objectives The research aim can be defined as the broad challenge of this work and is stated below. Formulate, implement and evaluate an IoT and Fog based Application Deployment Framework for Industry 4.0 systems The objectives can be described as steps that need to be taken to fully answer both the main research question and the more precise questions that make it up. Due to the nature of the work the objectives can be split up into three categories, Platform, Model and Method. Each of these looks at distinct components of the big framework. 1.4.2 Fog of Things Platform The first component was designed to answer the requirements of Industry 4.0 and also to explore some of the novel concepts of IoT and Fog Computing. To decide which components are to be used on the platform a similar methodology was used as in (Cruz et al. 2018) where
Brazil STAR Project Surveys
Distributed system for the monitoring and control of processes
2017 CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON), 2017
A distributed system is presented for the monitoring and control of the primary variables: pressu... more A distributed system is presented for the monitoring and control of the primary variables: pressure, level and flow; for this, a wireless system is implemented at the level of sensor-actuators and at the level of controllers an Ethernet / IP network, the mentioned industrial networks are implemented based on the OSI model and TCP / IP respectively, for the visualization an HMI is realized in the software LabVIEW, finally performance tests are performed and the results presented allow to validate the operation of the distributed system.
Off the boil? The challenges of monitoring cooking behaviour in refugee settlements
Energy Research & Social Science
The concepts of Industry 4.0 provide a new means of integrating concepts from ubiquitous computin... more The concepts of Industry 4.0 provide a new means of integrating concepts from ubiquitous computing with manufacturing technologies through cybernetics. This advances the automation of the manufacturing systems and helps improve product quality, production efficiency, condition monitoring and decision making (J. Lee, Bagheri, and Kao 2015; DIN 2016). Within this concept, machines become connected with humans through computer systems to work in a coordinated way to automate data acquisition, sharing and exchange among the physical and virtual worlds. The wide spread availability and affordability of sensors, wireless networks and the accessibility of high-speed Internet make real-time multiple parameters monitoring and control of manufacturing process possible in a way that was not feasible before (Y. Lu 2017). This leads to a great number of sensors being deployed to physical machines which in turn generates a large volume of data that requires computationally intensive analysis and interpretation for decision-making purposes. The resulting decisions, whether made by humans or software, often need to be transformed into control signals for actuators to operate the machine in the physical world. This then creates a loop-back to the sensor system as new sets of data are collected and sent back for further analysis, reflecting changing machine states over time. This type of system based on Cyber-Physical System (CPS) is a facilitator for realising the concepts of Industry 4.0. It enables computational algorithms and physical components to interact with each other through real-time monitoring and control to improve productivity (Trappey et al. 2016; L. Wang, Törngren, and Onori 2015). Yet, as stated in (Wiesner, Marilungo, and Thoben 2017) traditional servers with limited capacities may not be able to cope with the new challenges in terms of scalability and complexity of such systems. In turn, 1.3 Research Aims and Objectives The research aim can be defined as the broad challenge of this work and is stated below. Formulate, implement and evaluate an IoT and Fog based Application Deployment Framework for Industry 4.0 systems The objectives can be described as steps that need to be taken to fully answer both the main research question and the more precise questions that make it up. Due to the nature of the work the objectives can be split up into three categories, Platform, Model and Method. Each of these looks at distinct components of the big framework. 1.4.2 Fog of Things Platform The first component was designed to answer the requirements of Industry 4.0 and also to explore some of the novel concepts of IoT and Fog Computing. To decide which components are to be used on the platform a similar methodology was used as in (Cruz et al. 2018) where
ICSD 2021
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
ArXiv, 2022
Adding renewable energy sources and storage units to an electric grid has led to a change in the ... more Adding renewable energy sources and storage units to an electric grid has led to a change in the way energy is generated and billed. This shift cannot be managed without a unified view of energy systems and their components. This unified view is captured within the idea of a Smart Local Energy System (SLES). Currently, various isolated control and market elements are proposed to resolve network constraints, demand side response and utility optimisation. They rely on topology estimations, forecasting and fault detection methods to complete their tasks. This disjointed design has led to most systems being capable of fulfilling only a single role or being resistant to change and extensions in functionality. By allocating roles, functional responsibilities and technical requirements to bounded systems a more unified view of energy systems can be achieved. This is made possible by representing an energy system as a distributed peer-to-peer (P2P) environment where each individual demand e...
EPSRC HEED Data Repository: Lantern Monitoring System
The dataset deposited here was prepared under the EPSRC-funded Humanitarian Engineering and Energ... more The dataset deposited here was prepared under the EPSRC-funded Humanitarian Engineering and Energy for Displacement research project (EP/P029531/1). The project aimed to understand energy needs of displaced communities, create an evidence base on the usage of different energy interventions and provide recommendations for improved design of future energy interventions to better meet the needs of people. As part of the project, we deployed Lantern Monitoring Systems in Nyabiheke camp, Rwanda. The aim was to (a) evaluate lantern usage pattern – static or mobile (b) evaluate lantern charge and discharge pattern to understand consumption behaviour. The mobile lantern monitors comprise of a D.light S30 solar lantern fitted with an Arduino-based monitoring device. The most integral part of the device is the Arduino MKR GSM 1400 board connected to an ADXL345 inertial motion unit sensor. The ADXL is used to generate activity and freefall interrupts based on acceleration readings when the lan...
EPSRC HEED Data Repository: Nepal Household Appliance Survey
The dataset deposited here was prepared under the EPSRC-funded Humanitarian Engineering and Energ... more The dataset deposited here was prepared under the EPSRC-funded Humanitarian Engineering and Energy for Displacement research project (EP/P029531/1). The project aimed to understand the energy needs of displaced communities, create an evidence base on the usage of different energy interventions and provide recommendations for improved design of future energy interventions to better meet the needs of people. As part of the project, three Appliance surveys were conducted in the Uttargaya settlement in Nepal. Appliance surveys are designed to assess the energy needs of a community based on the devices they use. The surveys span three instances across 18 months, starting in October 2018 and ending in April 2020. The survey splits the participants into four categories, organised into sheets, based on the type of metering participants have: 'bulk meter'; 'sub meter'; 'do not possess meter' and 'do not have electrical connection'. The survey anonymises the na...
cogent-computing/Heed-Microcontroller: Zenodo Release
Release with updates for Zenodo
Brazil STAR Project Microgrid Monitoring Data
Contains monitoring data from microgrids deployed in rural areas of the State of Amazonas, Brazil... more Contains monitoring data from microgrids deployed in rural areas of the State of Amazonas, Brazil. The data collections started in January of 2018 and ended in March 2019. Contained in the folder CSVs.zip are data from four housing units (denoted as STAR-A, STAR-B, STAR-C and STAR-D) consisting of:<br> 1) Battery information,<br> 2) Solar photovoltaic (PV) generation information, and<br> 3) Measurements from individual appliance monitors (IAMs). Folders are named in the format YYYY-MM-DD-STAR-X_SYS, where<br> 1) YYYY is the year,<br> 2) MM is the month,<br> 3) DD is the day,<br> 4) STAR-X is indicator for the housing unit, i.e., STAR-A, STAR-B, STAR-C, or STAR-D.<br> 5) SYS denotes the "system" and refers to one of the following: Battery, PV, or IAM. The files YYYY-MM-DD-STAR-X_Battery contain the following information in their columns in the order presented:<br> 1) Timestamp<br> 2) Battery current (mA)<br> 3) Ampere-hours (mAh)<br> 4) State of Charge (SOC) (%)<br> 5) Number of charge cycles<br> 6) Number of full discharges<br> 7) Battery voltage (mV)<br> 8) Battery power (W)<br> 9) Amount of discharge energy (0.01 kWh)<br> 10) Amount of charge energy (0.01 kWh) The files YYYY-MM-DD-STAR-X_PV contain the following information in their columns in the order presented:<br> 1) Timestamp<br> 2) Panel voltage (mV)<br> 3) Panel power (W)<br> 4) Energy generated (0.01 kWh)<br> 5) Battery state of operation: 0(Off); 1(Low power); 2(Fault); 3(Bulk); 4(Absorption); 5(Float); 6(Inverting) The files YYYY-MM-DD-STAR-X_IAM contain the following information in their columns in the order presented:<br> 1) Timestamp<br> 2) Real power (W)<br> 3) Reactive power (W)<br> 4) Voltage (unknown unit)<br> 5) IAM ID
EPSRC HEED Data Repository: Stove Use Monitoring System
The dataset deposited here was prepared under the EPSRC-funded Humanitarian Engineering and Energ... more The dataset deposited here was prepared under the EPSRC-funded Humanitarian Engineering and Energy for Displacement research project (EP/P029531/1). The project aimed to understand energy needs of displaced communities, create an evidence base on the usage of different energy interventions and provide recommendations for improved design of future energy interventions to better meet the needs of people. As part of the project, we deployed Stove Use Monitoring Systems (SUM) on clay cook stoves in Kigeme camp, Rwanda. The aim was to (a) measure and evaluate temperature profiles within stove enclosure and on the surface of stoves (b) evaluate frequency and duration of stove use. The SUM consisted of 2 sensors - a thermocouple (to measure temperature within the stove) and a Si7021 sensor (to measure temperature and humidity outside the stove), connected to an Arduino MKR GSM 1400 board. The data measured by the sensors was stored only if the change in values exceeded a set threshold for ...
nandor1992/fog_visdeploy: Full Release of features and Examples
Contains the full release of the java library responsible for the simulation; the python runtime ... more Contains the full release of the java library responsible for the simulation; the python runtime that shows how this can be utilised cross-platform and the visualisation platform designed in html and based on vis.js.
cogent-computing/STAR-Survey-Analysis: Code Release
Since 2003, Brazil is striving to provide energy access to all, in rural areas, in an effort to e... more Since 2003, Brazil is striving to provide energy access to all, in rural areas, in an effort to empower the communities economically. Unpacking fuel stacking behaviour can shed light onto the speed of transition toward the exclusive use of advanced fuel types. This analysis presents findings from surveys carried out with 14 non-electrified communities in a rural area of Rio Negro, Amazonas State, Brazil. We identify the fuel choice determinants in these communities using a multinomial logistic regression model and discuss more generally the validity and robustness of such models in the context of statistical validation and evaluation metrics. Specifically for the Amazonas communities considered in this study, the research showed that fuel choice determinants are the education of household head, the number of females in the household and the number of meals per day in a household. Moreover, considering the actual Brazilian policies related to energy or sustainability is not likely to...
cogent-computing/DOMUS-Data-Aquisition: Experiment Release
Release that contains the code that was used in the holistic comfort experiment.
Brazil STAR Project Surveys
To evaluate the determinants of household cooking fuel choice in Amazon riverside communities, a ... more To evaluate the determinants of household cooking fuel choice in Amazon riverside communities, a cross-sectional study was conducted in 14 riverside communities (593 households) located on the Rio Negro, Amazonas, Brazil. These GPS coordinates of the communities can be found in the surveys. The study was conducted over 9 weeks between April and June 2017. Two surveys were deployed in each of the 14 communities. One survey targeted individual households, whilst the second focused on the community as a whole. The households survey included open- and close-ended questions based on the World Bank guidelines for questionnaire design for household energy use from living standards measurement studies. One purpose of the household survey was to obtain data about their socio-demographic data such as income, education, house occupancy, house ownership, kitchen types, their choice of cooking fuels, their energy usage data, as well as their energy needs and aspirations.
Graph Analysis of Fog Computing Systems for Industry 4.0
2017 IEEE 14th International Conference on e-Business Engineering (ICEBE), 2017
Increased adoption of Fog Computing concepts into Cyber Physical Systems (CPS) is a driving force... more Increased adoption of Fog Computing concepts into Cyber Physical Systems (CPS) is a driving force for implementing Industry 4.0. The modern industrial environment focuses on providing a flexible factory floor that suits the needs of modern manufacturing through the reduction of downtimes, reconfiguration times, adoption of new technologies and the increase of its production capabilities and rates. Fog Computing through CPS aims to provide a flexible orchestration and management platform that can meet the needs of this emerging industry model. Proposals on Fog Computing platform and Software Defined Networks (SDN) for Industry allow for resource virtualization and access throughout the system enabling large composite application systems to be deployed on multiple nodes. The increase of reliability, redundancy and runtime parameters as well as the reduction of costs in such systems are of key interest to Industry and researchers as well. The development of optimization algorithms and methods is made difficult by the complexity of such systems and the lack of real-world data on fog systems resulting in algorithms that are not being designed for real world scenarios. We propose a set of use-case scenarios based on our Industrial partner that we analyze to determine the graph based parameters of the system that allows us to scale and generate a more realistic testing scenario for future optimization attempts as well as determine the nature of such systems in comparison to other networks types. To show the differences between these scenarios and our real-world use-case we have selected a set of key graph characteristics based on which we analyze and compare the resulting graphs from the systems.
Modelling industry 4.0 based fog computing environments for application analysis and deployment
EPSRC-funded Humanitarian Engineering and Energy for Displacement (HEED) datasets
This repository contains raw datasets gathered under the EPSRC-funded Humanitarian Engineering an... more This repository contains raw datasets gathered under the EPSRC-funded Humanitarian Engineering and Energy for Displacement (HEED) research project (EP/P029531/1). The project aimed to understand energy needs of displaced communities by creating an evidence base on the usage of seven different energy interventions, and provide recommendations for improved design of future energy interventions to better meet the needs of people. Below is a brief description of the interventions. Stove-use monitoring systems (SUMs) - SUMs were deployed on clay stoves in Kigeme camp, Rwanda in July 2019. The aim of the study was to evaluate stove usage patterns by measuring temperature profiles within stove enclosure and on the surface of stoves. The SUMs consisted of 2 sensors - a thermocouple to measure temperature within the stove and a Si7021 sensor to measure temperature outside the stove, connected to an Arduino MKR GSM 1400 board. The data measured by the sensors was stored only if the change in ...
A community energy management system for smart microgrids
Electric Power Systems Research
Coventry University, 2020
The concepts of Industry 4.0 provide a new means of integrating concepts from ubiquitous computin... more The concepts of Industry 4.0 provide a new means of integrating concepts from ubiquitous computing with manufacturing technologies through cybernetics. This advances the automation of the manufacturing systems and helps improve product quality, production efficiency, condition monitoring and decision making (J. Lee, Bagheri, and Kao 2015; DIN 2016). Within this concept, machines become connected with humans through computer systems to work in a coordinated way to automate data acquisition, sharing and exchange among the physical and virtual worlds. The wide spread availability and affordability of sensors, wireless networks and the accessibility of high-speed Internet make real-time multiple parameters monitoring and control of manufacturing process possible in a way that was not feasible before (Y. Lu 2017). This leads to a great number of sensors being deployed to physical machines which in turn generates a large volume of data that requires computationally intensive analysis and interpretation for decision-making purposes. The resulting decisions, whether made by humans or software, often need to be transformed into control signals for actuators to operate the machine in the physical world. This then creates a loop-back to the sensor system as new sets of data are collected and sent back for further analysis, reflecting changing machine states over time. This type of system based on Cyber-Physical System (CPS) is a facilitator for realising the concepts of Industry 4.0. It enables computational algorithms and physical components to interact with each other through real-time monitoring and control to improve productivity (Trappey et al. 2016; L. Wang, Törngren, and Onori 2015). Yet, as stated in (Wiesner, Marilungo, and Thoben 2017) traditional servers with limited capacities may not be able to cope with the new challenges in terms of scalability and complexity of such systems. In turn, 1.3 Research Aims and Objectives The research aim can be defined as the broad challenge of this work and is stated below. Formulate, implement and evaluate an IoT and Fog based Application Deployment Framework for Industry 4.0 systems The objectives can be described as steps that need to be taken to fully answer both the main research question and the more precise questions that make it up. Due to the nature of the work the objectives can be split up into three categories, Platform, Model and Method. Each of these looks at distinct components of the big framework. 1.4.2 Fog of Things Platform The first component was designed to answer the requirements of Industry 4.0 and also to explore some of the novel concepts of IoT and Fog Computing. To decide which components are to be used on the platform a similar methodology was used as in (Cruz et al. 2018) where
Brazil STAR Project Surveys
Distributed system for the monitoring and control of processes
2017 CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON), 2017
A distributed system is presented for the monitoring and control of the primary variables: pressu... more A distributed system is presented for the monitoring and control of the primary variables: pressure, level and flow; for this, a wireless system is implemented at the level of sensor-actuators and at the level of controllers an Ethernet / IP network, the mentioned industrial networks are implemented based on the OSI model and TCP / IP respectively, for the visualization an HMI is realized in the software LabVIEW, finally performance tests are performed and the results presented allow to validate the operation of the distributed system.
Off the boil? The challenges of monitoring cooking behaviour in refugee settlements
Energy Research & Social Science
The concepts of Industry 4.0 provide a new means of integrating concepts from ubiquitous computin... more The concepts of Industry 4.0 provide a new means of integrating concepts from ubiquitous computing with manufacturing technologies through cybernetics. This advances the automation of the manufacturing systems and helps improve product quality, production efficiency, condition monitoring and decision making (J. Lee, Bagheri, and Kao 2015; DIN 2016). Within this concept, machines become connected with humans through computer systems to work in a coordinated way to automate data acquisition, sharing and exchange among the physical and virtual worlds. The wide spread availability and affordability of sensors, wireless networks and the accessibility of high-speed Internet make real-time multiple parameters monitoring and control of manufacturing process possible in a way that was not feasible before (Y. Lu 2017). This leads to a great number of sensors being deployed to physical machines which in turn generates a large volume of data that requires computationally intensive analysis and interpretation for decision-making purposes. The resulting decisions, whether made by humans or software, often need to be transformed into control signals for actuators to operate the machine in the physical world. This then creates a loop-back to the sensor system as new sets of data are collected and sent back for further analysis, reflecting changing machine states over time. This type of system based on Cyber-Physical System (CPS) is a facilitator for realising the concepts of Industry 4.0. It enables computational algorithms and physical components to interact with each other through real-time monitoring and control to improve productivity (Trappey et al. 2016; L. Wang, Törngren, and Onori 2015). Yet, as stated in (Wiesner, Marilungo, and Thoben 2017) traditional servers with limited capacities may not be able to cope with the new challenges in terms of scalability and complexity of such systems. In turn, 1.3 Research Aims and Objectives The research aim can be defined as the broad challenge of this work and is stated below. Formulate, implement and evaluate an IoT and Fog based Application Deployment Framework for Industry 4.0 systems The objectives can be described as steps that need to be taken to fully answer both the main research question and the more precise questions that make it up. Due to the nature of the work the objectives can be split up into three categories, Platform, Model and Method. Each of these looks at distinct components of the big framework. 1.4.2 Fog of Things Platform The first component was designed to answer the requirements of Industry 4.0 and also to explore some of the novel concepts of IoT and Fog Computing. To decide which components are to be used on the platform a similar methodology was used as in (Cruz et al. 2018) where
ICSD 2021
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
ArXiv, 2022
Adding renewable energy sources and storage units to an electric grid has led to a change in the ... more Adding renewable energy sources and storage units to an electric grid has led to a change in the way energy is generated and billed. This shift cannot be managed without a unified view of energy systems and their components. This unified view is captured within the idea of a Smart Local Energy System (SLES). Currently, various isolated control and market elements are proposed to resolve network constraints, demand side response and utility optimisation. They rely on topology estimations, forecasting and fault detection methods to complete their tasks. This disjointed design has led to most systems being capable of fulfilling only a single role or being resistant to change and extensions in functionality. By allocating roles, functional responsibilities and technical requirements to bounded systems a more unified view of energy systems can be achieved. This is made possible by representing an energy system as a distributed peer-to-peer (P2P) environment where each individual demand e...
EPSRC HEED Data Repository: Lantern Monitoring System
The dataset deposited here was prepared under the EPSRC-funded Humanitarian Engineering and Energ... more The dataset deposited here was prepared under the EPSRC-funded Humanitarian Engineering and Energy for Displacement research project (EP/P029531/1). The project aimed to understand energy needs of displaced communities, create an evidence base on the usage of different energy interventions and provide recommendations for improved design of future energy interventions to better meet the needs of people. As part of the project, we deployed Lantern Monitoring Systems in Nyabiheke camp, Rwanda. The aim was to (a) evaluate lantern usage pattern – static or mobile (b) evaluate lantern charge and discharge pattern to understand consumption behaviour. The mobile lantern monitors comprise of a D.light S30 solar lantern fitted with an Arduino-based monitoring device. The most integral part of the device is the Arduino MKR GSM 1400 board connected to an ADXL345 inertial motion unit sensor. The ADXL is used to generate activity and freefall interrupts based on acceleration readings when the lan...
EPSRC HEED Data Repository: Nepal Household Appliance Survey
The dataset deposited here was prepared under the EPSRC-funded Humanitarian Engineering and Energ... more The dataset deposited here was prepared under the EPSRC-funded Humanitarian Engineering and Energy for Displacement research project (EP/P029531/1). The project aimed to understand the energy needs of displaced communities, create an evidence base on the usage of different energy interventions and provide recommendations for improved design of future energy interventions to better meet the needs of people. As part of the project, three Appliance surveys were conducted in the Uttargaya settlement in Nepal. Appliance surveys are designed to assess the energy needs of a community based on the devices they use. The surveys span three instances across 18 months, starting in October 2018 and ending in April 2020. The survey splits the participants into four categories, organised into sheets, based on the type of metering participants have: 'bulk meter'; 'sub meter'; 'do not possess meter' and 'do not have electrical connection'. The survey anonymises the na...
cogent-computing/Heed-Microcontroller: Zenodo Release
Release with updates for Zenodo
Brazil STAR Project Microgrid Monitoring Data
Contains monitoring data from microgrids deployed in rural areas of the State of Amazonas, Brazil... more Contains monitoring data from microgrids deployed in rural areas of the State of Amazonas, Brazil. The data collections started in January of 2018 and ended in March 2019. Contained in the folder CSVs.zip are data from four housing units (denoted as STAR-A, STAR-B, STAR-C and STAR-D) consisting of:<br> 1) Battery information,<br> 2) Solar photovoltaic (PV) generation information, and<br> 3) Measurements from individual appliance monitors (IAMs). Folders are named in the format YYYY-MM-DD-STAR-X_SYS, where<br> 1) YYYY is the year,<br> 2) MM is the month,<br> 3) DD is the day,<br> 4) STAR-X is indicator for the housing unit, i.e., STAR-A, STAR-B, STAR-C, or STAR-D.<br> 5) SYS denotes the "system" and refers to one of the following: Battery, PV, or IAM. The files YYYY-MM-DD-STAR-X_Battery contain the following information in their columns in the order presented:<br> 1) Timestamp<br> 2) Battery current (mA)<br> 3) Ampere-hours (mAh)<br> 4) State of Charge (SOC) (%)<br> 5) Number of charge cycles<br> 6) Number of full discharges<br> 7) Battery voltage (mV)<br> 8) Battery power (W)<br> 9) Amount of discharge energy (0.01 kWh)<br> 10) Amount of charge energy (0.01 kWh) The files YYYY-MM-DD-STAR-X_PV contain the following information in their columns in the order presented:<br> 1) Timestamp<br> 2) Panel voltage (mV)<br> 3) Panel power (W)<br> 4) Energy generated (0.01 kWh)<br> 5) Battery state of operation: 0(Off); 1(Low power); 2(Fault); 3(Bulk); 4(Absorption); 5(Float); 6(Inverting) The files YYYY-MM-DD-STAR-X_IAM contain the following information in their columns in the order presented:<br> 1) Timestamp<br> 2) Real power (W)<br> 3) Reactive power (W)<br> 4) Voltage (unknown unit)<br> 5) IAM ID
EPSRC HEED Data Repository: Stove Use Monitoring System
The dataset deposited here was prepared under the EPSRC-funded Humanitarian Engineering and Energ... more The dataset deposited here was prepared under the EPSRC-funded Humanitarian Engineering and Energy for Displacement research project (EP/P029531/1). The project aimed to understand energy needs of displaced communities, create an evidence base on the usage of different energy interventions and provide recommendations for improved design of future energy interventions to better meet the needs of people. As part of the project, we deployed Stove Use Monitoring Systems (SUM) on clay cook stoves in Kigeme camp, Rwanda. The aim was to (a) measure and evaluate temperature profiles within stove enclosure and on the surface of stoves (b) evaluate frequency and duration of stove use. The SUM consisted of 2 sensors - a thermocouple (to measure temperature within the stove) and a Si7021 sensor (to measure temperature and humidity outside the stove), connected to an Arduino MKR GSM 1400 board. The data measured by the sensors was stored only if the change in values exceeded a set threshold for ...
nandor1992/fog_visdeploy: Full Release of features and Examples
Contains the full release of the java library responsible for the simulation; the python runtime ... more Contains the full release of the java library responsible for the simulation; the python runtime that shows how this can be utilised cross-platform and the visualisation platform designed in html and based on vis.js.
cogent-computing/STAR-Survey-Analysis: Code Release
Since 2003, Brazil is striving to provide energy access to all, in rural areas, in an effort to e... more Since 2003, Brazil is striving to provide energy access to all, in rural areas, in an effort to empower the communities economically. Unpacking fuel stacking behaviour can shed light onto the speed of transition toward the exclusive use of advanced fuel types. This analysis presents findings from surveys carried out with 14 non-electrified communities in a rural area of Rio Negro, Amazonas State, Brazil. We identify the fuel choice determinants in these communities using a multinomial logistic regression model and discuss more generally the validity and robustness of such models in the context of statistical validation and evaluation metrics. Specifically for the Amazonas communities considered in this study, the research showed that fuel choice determinants are the education of household head, the number of females in the household and the number of meals per day in a household. Moreover, considering the actual Brazilian policies related to energy or sustainability is not likely to...
cogent-computing/DOMUS-Data-Aquisition: Experiment Release
Release that contains the code that was used in the holistic comfort experiment.
Brazil STAR Project Surveys
To evaluate the determinants of household cooking fuel choice in Amazon riverside communities, a ... more To evaluate the determinants of household cooking fuel choice in Amazon riverside communities, a cross-sectional study was conducted in 14 riverside communities (593 households) located on the Rio Negro, Amazonas, Brazil. These GPS coordinates of the communities can be found in the surveys. The study was conducted over 9 weeks between April and June 2017. Two surveys were deployed in each of the 14 communities. One survey targeted individual households, whilst the second focused on the community as a whole. The households survey included open- and close-ended questions based on the World Bank guidelines for questionnaire design for household energy use from living standards measurement studies. One purpose of the household survey was to obtain data about their socio-demographic data such as income, education, house occupancy, house ownership, kitchen types, their choice of cooking fuels, their energy usage data, as well as their energy needs and aspirations.
Graph Analysis of Fog Computing Systems for Industry 4.0
2017 IEEE 14th International Conference on e-Business Engineering (ICEBE), 2017
Increased adoption of Fog Computing concepts into Cyber Physical Systems (CPS) is a driving force... more Increased adoption of Fog Computing concepts into Cyber Physical Systems (CPS) is a driving force for implementing Industry 4.0. The modern industrial environment focuses on providing a flexible factory floor that suits the needs of modern manufacturing through the reduction of downtimes, reconfiguration times, adoption of new technologies and the increase of its production capabilities and rates. Fog Computing through CPS aims to provide a flexible orchestration and management platform that can meet the needs of this emerging industry model. Proposals on Fog Computing platform and Software Defined Networks (SDN) for Industry allow for resource virtualization and access throughout the system enabling large composite application systems to be deployed on multiple nodes. The increase of reliability, redundancy and runtime parameters as well as the reduction of costs in such systems are of key interest to Industry and researchers as well. The development of optimization algorithms and methods is made difficult by the complexity of such systems and the lack of real-world data on fog systems resulting in algorithms that are not being designed for real world scenarios. We propose a set of use-case scenarios based on our Industrial partner that we analyze to determine the graph based parameters of the system that allows us to scale and generate a more realistic testing scenario for future optimization attempts as well as determine the nature of such systems in comparison to other networks types. To show the differences between these scenarios and our real-world use-case we have selected a set of key graph characteristics based on which we analyze and compare the resulting graphs from the systems.