Real time control and monitoring of grid power systems using cloud computing

The use of grid power systems based on the combinations of various electrical networks, information technology, and communication layers called as Smart Grid systems. The technique of smart grid suppressed the problems faced by conventional grid systems such as inefficient energy management, improper control actions, grid faults, human errors, etc. The recent research on smart grid provides the approach for the real-time control and monitoring of grid power systems based on bidirectional communications. However, the smart grid is yet to improve regarding efficiency, energy management, reliability, and cost-effectiveness by considering its real-time implementation. In this paper, we present the real-time design of efficient monitoring and control of grid power system using the remote cloud server. We utilized the remote cloud server to fetch, monitor and control the real-time power system data to improve the universal control and response time. The proper hardware panel designed and fabricated to establish the connection with the grid as well as remote cloud users. The authenticated cloud users are provisioned to access and control the grid power system from anywhere securely. For the user authentication, we proposed the novel approach to secure the complete smart grid system. Finally, we demonstrated the effectiveness of real-time monitoring and control of the grid power method with the use of structure of practical framework

A Mobile Geo-Communication Dataset for Physiology-Aware DASH in Rural Ambulance Transport

Use of telecommunication technologies for remote, continuous monitoring of patients can enhance effectiveness of emergency ambulance care during transport from rural areas to a regional center hospital. However, the communication along the various routes in rural areas may have wide bandwidth ranges from 2G to 4G; some regions may have only lower satellite bandwidth available. Bandwidth fluctuation together with real-time communication of various clinical multimedia pose a major challenge during rural patient ambulance transport.; AB@The availability of a pre-transport route-dependent communication bandwidth database is an important resource in remote monitoring and clinical multimedia transmission in rural ambulance transport. Here, we present a geo-communication dataset from extensive profiling of 4 major US mobile carriers in Illinois, from the rural location of Hoopeston to the central referral hospital center at Urbana. In collaboration with Carle Foundation Hospital, we developed a profiler, and collected various geographical and communication traces for realistic emergency rural ambulance transport scenarios. Our dataset is to support our ongoing work of proposing "physiology-aware DASH", which is particularly useful for adaptive remote monitoring of critically ill patients in emergency rural ambulance transport. It provides insights on ensuring higher Quality of Service (QoS) for most critical clinical multimedia in response to changes in patients' physiological states and bandwidth conditions. Our dataset is available online for research community.Comment: Proceedings of the 8th ACM on Multimedia Systems Conference (MMSys'17), Pages 158-163, Taipei, Taiwan, June 20 - 23, 201

Remote supervision of production processes in the food industry

This paper presents a remote monitoring system based on the SCADA model (Supervisory Control And Data Acquisition), specifically developed for automated production processes in a food company. The goals defined for this project were the monitoring of three production lines and six silos of raw materials, along with the indication / evaluation of three performance measures demanded by the company: production rate, packed quantity and OEE (Overall Equipment Effectiveness). The developed system should also include other typical SCADA functionalities, namely alarms management, process trending and data logging. The applied methodology involved the detailed analysis of the existing automation systems, the functional specification of the remote monitoring system and the corresponding implementation (using the LabVIEW platform), test and validation. The project took about six months and the system was successfully implemented in one of the company's factories. All the objectives were achieved.info:eu-repo/semantics/publishedVersio

Edge-based Compression and Classification for Smart Healthcare Systems: Concept, Implementation and Evaluation

Smart healthcare systems require recording, transmitting and processing large volumes of multimodal medical data generated from different types of sensors and medical devices, which is challenging and may turn some of the remote health monitoring applications impractical. Moving computational intelligence to the net- work edge is a promising approach for providing efficient and convenient ways for continuous-remote monitoring. Implementing efficient edge-based classification and data reduction techniques are of paramount importance to enable smart health- care systems with efficient real-time and cost-effective remote monitoring. Thus, we present our vision of leveraging edge computing to monitor, process, and make au- tonomous decisions for smart health applications. In particular, we present and im- plement an accurate and lightweight classification mechanism that, leveraging some time-domain features extracted from the vital signs, allows for a reliable seizures detection at the network edge with precise classification accuracy and low com- putational requirement. We then propose and implement a selective data transfer scheme, which opts for the most convenient way for data transmission depending on the detected patient’s conditions. In addition to that, we propose a reliable energy-efficient emergency notification system for epileptic seizure detection, based on conceptual learning and fuzzy classification. Our experimental results assess the performance of the proposed system in terms of data reduction, classification accuracy, battery lifetime, and transmission delay. We show the effectiveness of our system and its ability to outperform conventional remote monitoring systems that ignore data processing at the edge by: (i) achieving 98.3% classification accuracy for seizures detection, (ii) extending battery lifetime by 60%, and (iii) decreasing average transmission delay by 90%

Applying Hyperspectral Imaging to Heart Rate Estimation for Adaptive Automation

Automation use continues to increase in Air Force systems with the goal of improving operator efficiency and effectiveness. Unfortunately, systems are often complex, potentially imposing increased mental task load on the operator, or placing the operator in a supervisory role where they can become dependent on automation. Adaptive automation is a proposed solution, where automation is triggered when an operator is overloaded, and disabled as the operator is underloaded. Changes in physiological measures have shown promise in triggering automation. Unfortunately heart rate measurement can be obtrusive and impractical in day-to-day operations. This research used the Air Force Multi-Attribute Task Battery to impose varying task loads on subjects while monitoring their performance, recording their heart rate information with an electrocardiogram and obtaining subjective estimates of mental workload. Simultaneously, hyperspectral images were captured to determine if changes in heart rate might be identified through these images, providing a remote assessment of heart rate (HR). HR and several heart rate variability measurements where significantly affected by Task Load. A linear regression model was developed to predict subjects\u27 perceived workload as a function of a proposed summary performance metric and HR measures. Additionally, this research identified several requirements for remote HR monitoring techniques

The Use of Remote Monitoring for Internal Cardioverter Defibrillators (ICDS): The Infusion of Information Technology and Medicine

The clinical use of automated implantable cardioverter defibrillators (AICDs) has been rapidly increasing since the results of several randomized trials confirmed the efficacy of AICDs in the secondary and primary prevention of sudden cardiac death. Patients with AICDs require high-quality care and intense follow-up to ensure safe and effective device performance. According to international guidelines these patients should be followed at 1- to 4 month intervals, depending on the device model and the patient’s clinical status (Schoenfeld, 2004). Given the expanding indications for use and the complexity of these devices, there is an urgent need to develop new means of ICD follow-up, so as to optimize patient safety and the use of healthcare resources. An internet-based remote-monitoring system could provide a practical substitute to time-consuming and expensive in-office visits. Although the initial experience with these systems has been favorable, many practical issues remain. In particular, more information is required on the usability and safety of remote monitoring for patient-initiated transmissions and cost effectiveness of the system as a substitute for routine in-office visits during long-term follow-up

Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges

Chronic diseases are becoming more widespread. Treatment and monitoring of these diseases require going to hospitals frequently, which increases the burdens of hospitals and patients. Presently, advancements in wearable sensors and communication protocol contribute to enriching the healthcare system in a way that will reshape healthcare services shortly. Remote patient monitoring (RPM) is the foremost of these advancements. RPM systems are based on the collection of patient vital signs extracted using invasive and noninvasive techniques, then sending them in real-time to physicians. These data may help physicians in taking the right decision at the right time. The main objective of this paper is to outline research directions on remote patient monitoring, explain the role of AI in building RPM systems, make an overview of the state of the art of RPM, its advantages, its challenges, and its probable future directions. For studying the literature, five databases have been chosen (i.e., science direct, IEEE-Explore, Springer, PubMed, and science.gov). We followed the (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) PRISMA, which is a standard methodology for systematic reviews and meta-analyses. A total of 56 articles are reviewed based on the combination of a set of selected search terms including RPM, data mining, clinical decision support system, electronic health record, cloud computing, internet of things, and wireless body area network. The result of this study approved the effectiveness of RPM in improving healthcare delivery, increase diagnosis speed, and reduce costs. To this end, we also present the chronic disease monitoring system as a case study to provide enhanced solutions for RPMsThis research work was partially supported by the Sejong University Research Faculty Program (20212023)S

Path Planning Optimization with Flexible Remote Sensing Application

The purpose of the path planning optimization is to find the most favorable route between starting and arrival points based on defined criteria and target functions. The change in the characteristics of each route becomes complicated when there is an increase in the number of intermediate points. This study predominately analyses the monitoring of a limited area. The author demonstrates how the path of the autonomous systems will change in different conditions and further introduces the possibility of using mobile remote sensing systems. The test is performed firstly in 2D flat area, then 3D spaces, and then—taking a forest fire as an example—the ideal conditions changed to reality. The study reveals findings on efficiency, based both on professional and economic considerations. The utilization of remote sensing technologies was found to optimize the observation of the given area generating new problems, such as what is the size of the monitored area at a given moment and how can we increase it for the higher effectiveness. An increase in the size of the monitored area results into an efficient and functional autonomous system albeit generating a shorter and modified path. Mobile autonomous systems therefore can be replaced by stable systems; simultaneously under real conditions, they can be more efficient than stable ones

Effectiveness and safety of pulse oximetry in remote patient monitoring of patients with COVID-19

Context A surge of COVID cases globally is often portrayed as “very likely”, which overwhelms health systems and challenges their capacities. A mitigation strategy is seen by remotely monitoring COVID patients in out-of-hospital settings to determine the risk of deterioration. Description of the problem We need an indicator to enable remote monitoring of COVID patients at home that can be measured by a handy tool; pulse oximetry which measures peripheral blood oxygen saturation (SpO2). Evidence shows that SpO2 is a reliable indicator of deterioration among COVID patients. The UK initiated a national programme (COVID Oximetry @ Home (CO@H)) to assess the theory. The concept can be potentially applied in other countries in various settings. As part of CO@H, we conducted a systematic review of the evidence on the safety and effectiveness of pulse oximetry in remote monitoring of COVID patients. Results Our review confirms the safety and potential effectiveness of pulse oximetry in remote home monitoring among COVID patients. We identified 13 research projects involving 2,908 participants that assessed the proposed strategy. Evidence shows the need to monitor at-rest and post-exertional SpO2. At-rest SpO2 of ≤ 92% or a decrease of 5% or more in post-exertional SpO2 should indicate care escalation. The recommended method for measuring at-rest SpO2 is after 5-10 min of rest, and assessing post-exertional SpO2 is after conducting a 1-min sit-to-stand test. We could not find explicit evidence on the impact on health service use compared with other models of care. Lessons Remote monitoring of COVID patients could alleviate the pressure on health systems and save hospital resources. Monitoring SpO2 by pulse oximetry can be widely applied, including in resource-limited settings, as the tool is affordable, reliable, and easy to use. Key messages • Adopting relevant health technologies in remote patient monitoring is critical to combat the pandemic. • Pulse oximetry is an affordable, easy to use and widely available tool to monitor patients with COVID-19 at home