IoT Clusters Platform for Data Collection, Analysis, and Visualization Use Case

Climate change is happening, and many countries are already facing devastating consequences. Populations worldwide are adapting to the season\u27s unpredictability they relay to lands for agriculture. Our first research was to develop an IoT Clusters Platform for Data Collection, analysis, and visualization. The platform comprises hardware parts with Raspberry Pi and Arduino\u27s clusters connected to multiple sensors. The clusters transmit data collected in real-time to microservices-based servers where the data can be accessed and processed. Our objectives in developing this platform were to create an efficient data collection system, relatively cheap to implement and easy to deploy in any part of the world. Since we have completed the first part, we are implementing a study case for a field used of the platform. Thus, we are implementing an environment monitoring technology base on weather data. For this study, the platform will collect real-time environmental data using sensors (Temperature, humidity, light and ultraviolet sensors, and other sensors). We are setting those sensors in relatively limited superficies due to resources problem. Next, we will use this data to find patterns in weather changes using Machine and Deep learning techniques since these environmental data come from a designated area. The main objective of this part is to find a weather pattern using collected data specific to this area. Data collected during this research and the IoT platform are available on campus for students to use for their class projects or future research. Currently, we are in the data collection process. We also evaluate the degradation and environmental effects on devices and sensors used. This study case is a needed step in the IoT Clusters Platform for Data Collection, Analysis, and Visualization research project. At the end of the project, the data collection framework from it will be efficient and cost less

Secure Cloud-based IoT Water Quality Gathering for Analysis and Visualization

Water quality refers to measurable water characteristics, including chemical, biological, physical, and radiological characteristics usually relative to human needs. Dumping waste and untreated sewage are the reasons for water pollution and several diseases to the living hood. The quality of water can also have a significant impact on animals and plant ecosystems. Therefore, keeping track of water quality is a substantial national interest. Much research has been done for measuring water quality using sensors to prevent water pollution. In summary, those systems are built based on online and reagent-free water monitoring SCADA systems in wired networks. However, centralized servers, transmission protocols, and data access can present challenges and disadvantages for those systems. This paper proposes a secure Cloud-based IoT water quality gathering architecture for water quality analysis and visualization to address the limitations of the current systems. The proposed architecture will send, analyze and visualize water quality data in the Cloud by utilizing specialized sensors and IoT-based gateways to capture water measurements (Dioxygen concentration, and temperature, among others). Then, they communicate securely to the Cloud-based server through a high-speed wireless network. We evaluated the performance of the proposed framework on a process-oriented approach to success metrics for cyberinfrastructures. The experiments were conducted in a laboratory and focused on network security and resiliency, the IoT prototype performance in dropping real-time data transmission, and remote access. The results demonstrate higher data collection and transmission effectiveness with minimal data loss and low energy usage over time. The accompanying cloud-based platform provided the flexibility needed for water quality monitoring and laboratory studies

GR-40 Design and Implementation of a Microservices Web-based Architecture for Code Deployment and Testing

Many tech stars like Netflix, Amazon, PayPal, eBay, and Twitter are evolving from monolithic to a microservice architecture due to the benefits for Agile and DevOps teams. Microservices architecture can be applied to multiple industries, like IoT, using containerization. Virtual containers give an ideal environment for developing and testing IoT technologies. Since the IoT industry has exponential growth, it is the responsibility of universities to teach IoT with hands-on labs to minimize the gap between what the students learn and what is on-demand in the job market. That can be done by using containerization. There are many approaches in the containerization field, but they can be difficult to use without depth knowledge in virtualization and code encapsulation. After a deep analysis of the containerization challenges, we came with an idea of a microservice infrastructure based on Docker, which is an open- platform for developing, testing, and running applications using containers, to solve the virtualization and code-encapsulation problem. Our infrastructure will provide a code development and testing web-based platform that allows users to securely go in the process of containerization without spending research time in learning virtualization. So, students and researchers can focus more on the development and testing of algorithms and codes. For example, it will be easy to develop containers that allow sensors to connect to an external server in few cliques, or to run a python code in a total isolate process in minutes without downloading any containerization software.Advisors(s): Dr. Maria Valero [email protected] Dr Hossain Shahriar [email protected](s): IoT/Cloud/Networkin

GR-182 - IoT Clusters Platform for Data Collection, Analysis, and Visualization Use Case

Climate change is happening, and many countries are already facing devastating consequences. Populations worldwide are adapting to the season\u27s unpredictability they relay to lands for agriculture. Our first research was to develop an IoT Clusters Platform for Data Collection, analysis, and visualization. The platform comprises hardware parts with Raspberry Pi and Arduino clusters connected to multiple sensors. The clusters transmit data collected in real-time to microservices-based servers where the data can be accessed and processed. Our objectives in developing this platform were to create an efficient data collection system, relatively cheap to implement and easy to deploy in any part of the world. Since we have completed the first part, we are implementing a study case for a field used by the platform. Thus, we are implementing an environment monitoring technology base on weather data. For this study, the platform will collect real-time environmental data using sensors (Temperature, humidity, light and ultraviolet sensors, and other sensors). We are setting those sensors in relatively limited superficies due to resources problem. Next, we will use this data to find patterns in weather changes using Machine and Deep learning techniques since these environmental data come from a designated area. The main objective of this part is to find a weather pattern using collected data specific to this area. Data collected during this research and the IoT platform are available on campus for students to use for their class projects or future research. Currently, we are in the data collection process. We also evaluate the degradation and environmental effects on devices and sensors used. This study case is a needed step in the IoT Clusters Platform for Data Collection, Analysis, and Visualization research project. At the end of the project, the data collection framework will be efficient and cost less

Do Shared S-minus Functions Among Stimuli Lead to Equivalence?

We examined the claim that equivalence classes contain all positive elements in a reinforcement contingency by asking whether negative stimuli in a reinforcement contingency will also form an equivalence class, based on their shared function as S-minus stimuli. In Experiment 1, 5 subjects were tested for equivalence for positive and negative stimuli. Testing of positive stimuli preceded testing of negative stimuli. Two of five subjects demonstrated equivalence for positive stimuli, and three subjects demonstrated equivalence for negative stimuli. In Experiment 2, order of testing was reversed. Four of six subjects demonstrated equivalence for positive stimuli, and none demonstrated equivalence for negative stimuli. In Experiment 3, positive and negative stimuli were tested together. Only one of five subject demonstrated equivalence for positive and negative stimuli. These data suggest that negative stimuli may enter an equivalence class, and so Sidman paradigm should be expanded. Order of testing was found as a meaningful variable

Secure Cloud-based IoT Water Quality Gathering for Analysis and Visualization

Water quality refers to measurable water characteristics, including chemical, biological, physical, and radiological characteristics usually relative to human needs. Dumping waste and untreated sewage is the reason for water pollution and several diseases to the living hood. The quality of water can also have a significant impact on animals and plant ecosystems. Therefore, keeping track of water quality is a substantial national interest. Much research has been done for measuring water quality using sensors to prevent water pollution. In summary, those systems are built based on online and reagent-free water monitoring SCADA systems in wired networks. However, centralized servers, transmission protocols, and data access can present challenges and disadvantages for those systems. This paper proposes a secure Cloud-based IoT water quality gathering architecture for water quality analysis and visualization to address the limitations of the current systems. The proposed architecture will send, analyze and visualize water quality data in the Cloud by utilizing specialized sensors and IoT-based gateways to capture water measurements (Dioxygen concentration, and temperature, among others). Then, they communicate securely to the Cloud-based server through a high-speed wireless network. We evaluated the performance of the proposed framework on a process-oriented approach to success metrics for cyberinfrastructures. The experiments were conducted in a laboratory and focused on network security and resiliency, the IoT prototype performance in dropping real-time data transmission, and remote access. The results demonstrate higher data collection and transmission effectiveness with minimal data loss and low energy usage over time. The accompanying cloud-based platform provided the flexibility needed for water quality monitoring and laboratory studies

First human use of a wireless coplanar energy transfer coupled with a continuous-flow left ventricular assist device

The drive-line to power contemporary ventricular assist devices exiting the skin is associated with infection, and requires a holstered performance of the cardiac pump, which reduces overall quality of life. Attempts to eliminate the drive-line using transcutaneous energy transfer systems have been explored but have not succeeded in viable widespread application. The unique engineering of the coplanar energy transfer system is characterized by 2 large rings utilizing a coil-within-the-coil topology, ensuring robust resonance energy transfer while allowing for a substantial (>6 hours) unholstered circulatory support powered by an implantable battery source. Herein we report the first known human experience with this novel technology, coupled with a continuous-flow assist left ventricular assist device, in 2 consecutive patients evaluated with the primary end-point of system performance at 30 days post-implantation. ispartof: JOURNAL OF HEART AND LUNG TRANSPLANTATION vol:38 issue:4 pages:339-343 ispartof: location:United States status: publishe

Subclinical postoperative atrial fibrillation: a randomized trial

BackgroundPostoperative atrial fibrillation (POAF) is the most common complication of cardiac surgery, requiring interventions and prolonging hospital stay. POAF is associated with increased mortality and a higher rate of systemic thrombo-embolism. The rates of recurrent AF, optimal follow-up and management remain unclear. We aimed to evaluate the incidence of recurrent atrial fibrillation (AF) events, during long term follow-up in patients with POAF following cardiac surgery.MethodsPatients with POAF and a CHA2DS2-VASc score of ≥2 were randomized in a 2:1 ratio to either implantation of a loop recorder (ILR) or ECG monitoring using periodic Holters. Participants were followed prospectively for 2 years. The primary end point was the occurrence of AF longer than 5 min.ResultsThe final cohort comprised of 22 patients, of whom 14 received an ILR. Over a median follow up of 25.7 (IQR of 24.7–44.4) months, 8 patients developed AF, representing a cumulative annualized risk of AF recurrence of 35.7%. There was no difference between ILR (6 participants, 40%) and ECG/Holter (2 participants, 25% p = 0.917). All 8 patients with AF recurrence were treated with oral anticoagulation. There were no cases of mortality, stroke or major bleeding. Two patients underwent ILR explantation due to pain at the implantation site.ConclusionsThe rate of recurrent AF in patients with POAF after cardiac surgery and a CHA2DS2-VASc score of ≥2 is approximately 1 in 3 when followed systematically. Further research is need to assess the role of ILRs in this population

ECMO for COVID-19 patients in Europe and Israel

Since March 15th, 2020, 177 centres from Europe and Israel have joined the study, routinely reporting on the ECMO support they provide to COVID-19 patients. The mean annual number of cases treated with ECMO in the participating centres before the pandemic (2019) was 55. The number of COVID-19 patients has increased rapidly each week reaching 1531 treated patients as of September 14th. The greatest number of cases has been reported from France (n = 385), UK (n = 193), Germany (n = 176), Spain (n = 166), and Italy (n = 136) .The mean age of treated patients was 52.6 years (range 16–80), 79% were male. The ECMO configuration used was VV in 91% of cases, VA in 5% and other in 4%. The mean PaO2 before ECMO implantation was 65 mmHg. The mean duration of ECMO support thus far has been 18 days and the mean ICU length of stay of these patients was 33 days. As of the 14th September, overall 841 patients have been weaned from ECMO support, 601 died during ECMO support, 71 died after withdrawal of ECMO, 79 are still receiving ECMO support and for 10 patients status n.a. . Our preliminary data suggest that patients placed on ECMO with severe refractory respiratory or cardiac failure secondary to COVID-19 have a reasonable (55%) chance of survival. Further extensive data analysis is expected to provide invaluable information on the demographics, severity of illness, indications and different ECMO management strategies in these patients

GR-127 - IoT Clusters Platform for Data Collection, Analysis, and Visualization

The Internet of Things (IoT) popularity leads more scientists and students to research this field. IoTs have an efficient way of monitoring complex infrastructure systems and the environment around them. Thus, they intervene in several areas such as health care, engineering, or monitoring the effects of climate change. IoT\u27s primary function is to collect data and share them with a distant server through the internet or a private network. Research on IoTs is firstly about creating efficient light devices composed of sensors that follow rigorous security protocols to guarantee the integrity of the data from the collection to its final destination. Secondly, the challenge is to store the data on a secure platform accessible by competent people for its analysis and visualization. The next generations of IoT devices will have to pass through multiple tests to satisfy collection, transmission, and storing challenges. Our research implementation provides a physical system allowing users to set and configure sensors on Raspberry Pis or Arduino for data collection, a secure data transfer using APIs, and a cloud base storing space for visualization and analysis. The objective is to make research on IoT devices easier by providing a ready-to-use platform that allows research teams to focus on developing and testing new devices. Also, it offers real-time visualization of collected data via a web bases application and an adequate database for future analysis. Our platform aims to help students conduct IoT research projects or provide a complete database to those interested in data science on various sensors or IoT devices