Cloud-based Monitoring Research Articles

Cloud Based Performance Data Analysis and Monitoring System for Express Bus in Malaysia

Road accidents are often associated with developing countries. One of the developing countries with high traffic accidents is Malaysia. Buses are also no exception in cases of road accidents. Express buses are the main public transportation used by Malaysians to move to other states. However, many road accidents involve express buses arise from driver attitudes, uncertain weather conditions, bus health conditions, and the road environment. Thus, the study was conducted to design and build prototype cloud-based monitoring systems that could analyze the raw data received. The study can help the bus management to monitor the health of the bus in real-time using Big Data Analysis technology using the appropriate cloud-computing platform. This study was conducted to develop an express bus analysis and monitoring system in Malaysia, where the system would have a dashboard that would produce the analysis of raw data (strain and time value) from the Internet of Things (IoT) tools such as sensors, smartphones, and others in the form of info-graphics. The results of this study indicate that rural travel has resulted in the deformation of equipment and hardware such as engines or tires due to the high strain percentage of 321.49%. Therefore, buses that use rural roads are more comfortable to experience damage. For cities and highways, the strains are low with negative values where the city has a percentage of 1.69x10-2% and the highway has a percentage of -1.98x10-3%, with the very low rate, deformation will not occur in a short time, so the equipment and the hardware will be in good condition for a long period. In conclusion, this study has succeeded in generating an analysis and monitoring prototype system, as well as this analytical and monitoring system design can be used in the real phase of developing the system.

IoT Based WSN Ground Water Monitoring System with Cloud-Based Monitoring as a Service (Maas) and Prediction using Machine Learning

The main source of water in the Indian Subcontinent is Groundwater. It is also the most rapidly depleting resource due to various reasons such as rampant unchecked irrigation and exploitation of groundwater by industries and other organizations. The current system is limited by short communication range, high power consumption and the system monitors only the water level and the report is available only to the consumer i.e. it is a single-user system. Due to the unavailability of a centralized system to monitor and prevent overuse of water resources, sudden water crises have become a major issue in India. This project aims at implementing an IoT (Internet of things) based water monitoring system that monitors the water level and the quality of groundwater and updates real-time data to the database. This system is designed to monitor the groundwater level of an entire village or a town. It updates the people and the concerned government authorities in case of any decrease in water level and water quality below the threshold value, and also monitors the water consumption during a period and predicts exhaustion time. This system predicts the availability of water in the future based on current demand and usage and the recharge rate using machine learning algorithms. The data collected and the analysis of the data is made available in a Public Cloud. The modules are based on Raspberry Pi Zero, sensor nodes and LoRa (Long Range) Module or Wi-Fi module according to the network requirement for connectivity

Ionospheric monitoring system based on the Internet of Things with ThingSpeak

The Internet of Things (IoT) is a growing technology that allows digital devices to be integrated into the network. The use of IoT technology makes it possible to collect information from various Global Navigation Satellite System (GNSS) receivers connected to the Internet, providing a unique opportunity to obtain information on spatial and temporal distribution. In this article, the ionospheric monitoring system is implemented using ThingSpeak (IoT). Ionospheric signal delay/Total Electronic Content (TEC) data from GNSS stations, Koneru Lakshmaiah Education Foundation (KLEF)—Guntur (16.44° N, 80.62° E, geographical), Port Blair (11, 43° N, 92,43° E)—geographical, Bengaluru (13,02° N, 77,57° E; geographical), Lucknow (26,76° N, 80,88° E; geographical) are used for analysis during the 2015 period. The ionospheric signal delays are computed from the ThingSpeak and the ionospheric TEC analysis are performed directly in the MATLAB software. The computed observed TEC values were compared to the IRI-16 (International Reference Ionosphere) model values. The standard deviation of the observed TEC for four GNSS stations is 17.26 TECU for Port Blair, 18.34 TECU for Bengaluru, 16.51 TECU for KLEF-Guntur and 14.07 TECU for Lucknow. The corresponding standard deviation of the IRI model is 11.38 TECU for Port Blair, 13.25 TECU for Bengaluru, 17.67 TECU for KLEF-Guntur and 15.66 TECU for Lucknow. As a result, the cloud-based monitoring system reduces operating costs, backs up and retrieves data. It optimizes productivity and collaboration around the world via the Internet.

0510 Effect of Sleep Coaches Using Cloud-Based Monitoring on PAP Adherence

0510 Effect of Sleep Coaches Using Cloud-Based Monitoring on PAP Adherence

A cloud-based near real-time slope movement monitoring system

A cloud-based near real-time slope movement monitoring system

A cloud-based near real-time slope movement monitoring system

A cloud-based near real-time data management system for geotechnical monitoring that can efficiently record, transfer, store, manage, process, analyse and evaluate monitoring data and generate easy to view and use information is presented. The database structure and manual of the system functionalities for addressing issues during data upload and processing, record handling for multisensory devices, data visualisation using common reference dates, data error correction and parametric handling of import files are presented in detail. New features that are in development include a more robust warning system and a journaling system for visual observations. The system can reliably and quickly generate alarms and information about excessive movements with respect to mining and other operations that require monitoring.

412: Cloud-based glucose monitoring for pregnancies with diabetes: Implications of testing adherence to perinatal complications

412: Cloud-based glucose monitoring for pregnancies with diabetes: Implications of testing adherence to perinatal complications

A Design of Cloud-Based Billing and Monitoring System for an Electric Company

A Design of Cloud-Based Billing and Monitoring System for an Electric Company

Versatile IoT system for Cloud-based sensor monitoring

In this age of automation, the acquisition and monitoring of sensor data is essential to ensure the correct condition and functioning of machines and systems without the need of manual verification. This paper describes the development, assembling and configuration of a system for sensor data acquisition and Cloud-based monitoring. The acquisition system is versatile, allowing the usage from one to six sensors and the replacement of those with little additions to the code. The monitoring system has versions for PHP and Java both on Portuguese and English language. The collected data is read and processed by a microcontroller board and sent to the Internet by a Raspberry Pi. Then, the monitoring system displays it as charts with several different search modes and options to facilitate and give more accuracy for sensor monitoring. All the software is free and open-source, allowing adaptations to suit the user's needs.

Research and Development of a Power Monitoring System for the Sustainable Energy Management System Implementation at Green School, Bali, Indonesia

Green School Bali, an international school which provides education for sustainability through entrepreneurial learning and natural environment won the Zayed Future Energy Prize for developing the new renewable energy projects. Currently, Green School is supported by 2 main power sources which come from Indonesian State Electricity Company (PLN) and Photovoltaics (PV) system electricity with power reach 23.1 kW and 21.6 kWp respectively. Both of the sources are distributed to the load based on the manual balancing and daily manual switching, which create the unbalanced load and high power loss. The key to achieving sustainable energy management is the availability of accurate measurement, recording, and analysis system of the power quality in the whole buildings. To meet the requirement of the sustainable energy management system at Green School, the monitoring system of the power and energy at Green School is developed. This system contains 2 data loggers and an Internet of Things (IoT) platform. The fixed data logger provides routine measurement and recording of power quality at the PLN meter, whereas the portable data logger provides the same function for each electricity panel. The monitoring system and human interface system integrate the intelligent humanmachine interface to an Ubidots platform and completed with online and offline data storage. This system is useful for supporting the sustainable energy management goal of Green School and the daily energy practitioner’s work with providing a proper advance power monitoring system that has low cost and compatible with Green School system’s demand. This project will also be used as a platform and teaching tool for students to develop similar projects and learn about IoT and cloud-based monitoring solutions with other measurements in the classroom.

A Study on the Introduction of a Public CCTV Monitoring Center Based on Cloud Computing

CCTV는 셉테드와 도시 방범 분야의 가장 대표적인 기계적 감시 및 통제수단으로써 현재 각 지방자치단체의 CCTV 통합관제센터를 통해 활발히 운영되고 있다. CCTV 통합관제센터는 IT 자원이 집약된 중소규모의 데이터센터로 정의할 수 있으며, CCTV를 포함한 통합관제센터에서 운영되는 대부분 장비와 시설은 IT를 기반으로 한다. 최근 몇 년 전부터 IT 업계를 중심으로 클라우드라는 개념이 새롭게 등장하였는데, 소유 중심의 전통적인 IT 자원 소비방식을 필요한 만큼 임대하여 소비하는 새로운 방식으로 전통적 소비 방식을 빠르게 대체해 나아가고 있다. 이렇게 클라우드가 큰 주목을 받는 이유는 전통적 방식보다 비용, 속도, 확장성, 생산성, 성능, 안정성 등 총 6가지 측면에서 더욱 경쟁력이 있기 때문이다. 따라서 본 연구에서는 공용 클라우드 서비스를 이용한 CCTV 통합관제센터 모델을 개념 설계를 통해 제시하고, 도입효과에 대해 비용적인 측면과 관리·운영적인 측면에서 검증하였다. 연구 진행 절차는 먼저 기계적 감시 및 CCTV 이론, 통합관제센터 현황, 클라우드 이론을 관련 문헌 및 통계 자료를 통해 조사하였고, 연구 본론에서 클라우드 영상 감시 기술(VSaaS)을 바탕으로 클라우드 기반의 CCTV 통합관제센터 설계방안(가이드라인)을 제시하였다. 또한, 부가적으로 클라우드 통합관제센터 도입의 적합성과 효과성을 기존 통합관제센터 표준모델과 비교를 통해 총 소유비용(TCO) 및 업무 효율성의 관점에서 검증하였다. 연구의 결과로서 첫째, CCTV 촬영부, 감시·제어부, 그리고 전송부에 대한 설계 가이드라인을 개발하였고 20가지 항목으로 일목요연하게 정리하였다. 둘째, 통합관제센터 표준모델을 바탕으로 클라우드 전환으로 예상되는 TCO 절감을 계산하였고, 그 결과 CAPEX 48%, OPEX 46%에 해당하는 TCO의 절감 가능성을 확인하였다. 셋째, 클라우드 도입에 따른 직·간접적 관리 요소의 축소로 인한 업무 부담경감, 신규 시스템 도입 및 용량 확장과 같은 업무 처리 속도 향상, 그리고 여유 공간 활용을 통한 업무 환경 개선 등 통합관제센터 업무의 효율성 향상 가능성을 확인하였다.CCTV is the most representative mechanical surveillance and control instrument in the field of surveillance and urban crime, and is now actively operated through the public CCTV monitoring center of each municipality. Public CCTV monitoring centers can be defined as small- and medium-sized data centers with integrated IT resources. Most equipment and facilities operated by monitoring centers including CCTV are based on IT. In recent years, the concept of cloud computing has emerged around the IT industry. It is rapidly replacing traditional consumption methods with a new way to lease and consume IT resource as needed. The reason cloud computing is attracting much attention is because it is more competitive than traditional methods in terms of cost, speed, scalability, productivity, performance and stability. Therefore, in this study, public CCTV monitoring center model using public cloud service is presented through conceptual design, and the effect of introduction is tested in terms of cost, management and operation. In the research process, surveillance by CPTED, CCTV, and cloud computing theory were investigated through related literature and statistical data. In the research, cloud-based public CCTV monitoring center design based on Video Surveillance as a Service (VSaaS) were presented. At the end of the study, the suitability and effectiveness of the cloud-based monitoring center was verified from the viewpoint of the total cost of ownership (TCO) and work efficiency through comparison with the existing CCTV monitoring center standard model. As a result of the study, firstly, the design guideline for the CCTV camera part, the monitoring and control part, and the transmission part was developed, and 20 items were clarified. Second, we calculated the expected TCO savings as a result of cloud conversion with the CCTV monitoring center standard model. As a result, we confirmed the possibility of TCO savings of 48% for CAPEX and 46% for OPEX. Third, we anticipated business efficiency enhancement of CCTV monitoring center such as reduction of work burden due to reduction of direct and indirect management factor by cloud computing, improvement of work process speed such as introduction of new system and expansion of capacity, and improvement of working environment through utilization of free space respectively.

Cloud-based monitoring system for lysimetric and agroclimatic data

The availability of irrigation water is one of the main concerns in Mediterranean agriculture. For this reason, it is so important to accurately determine the water needs of the crop in order to optimize the use of scarce water resources. This paper presents the design and operation validation of an agroclimatic data acquisition system composed of a set of wireless nodes with different functionalities to ascertain irrigation requirements based on weather and lysimetric data. These nodes were based on a common single-board computer to which, depending on the required features, additional hardware components were added. A comparative study of the different single-board computers available in the market was conducted and the Raspberry Pi device was selected due its high performance to cost ratio. With this philosophy, the design of two types of nodes was proposed: climate nodes and lysimetric nodes. Software drivers were developed for both to perform data acquisition and to send the measurements through a mobile network to an application hosted in the cloud. This application managed the storage of the data in a structured-relational database. The required interfaces were programmed to allow the user to view historical and real-time data, as well as configure additional parameters of the nodes related to the crop, geographical location and so on. This cloud application is accessible from any computer or mobile device with an internet connection. All the software tools and hardware platforms were low-cost and open source. Various performance tests were carried out both in the laboratory and in the field, obtaining evapotranspiration measurements consistent with the reference evapotranspiration calculated from data of the climate node.

Abstract P359: Achieving Normal Blood Pressure with Cloud-based Monitoring and Management

Introduction: We assessed the feasibility and impact of remote monitoring and medication management on hypertension control in a nursing home. Hypothesis: Uncontrolled hypertension is a causal factor for cardiovascular disease. We hypothesized that increased sensitivity to elevated blood pressure (BP) readings via remote monitoring and targeted medication management alerts would lead to higher rates of controlled hypertension. Methods: We followed 48 long-term nursing home patients (average age=68.4) over a 10-week period, of whom 21 were identified as uncontrolled hypertension with a reading >140/90 mmHg. BP readings were taken 3 times a week as compared to a norm of 1 measurement per 3 weeks. Blood pressure data was stored in a cloud and analyzed via algorithms. The physician was given a summary of data identifying those patients who met criteria for elevated BP with averaged per week mean systolic and diastolic blood pressure. Furthermore, patients were further stratified via an algorithm where systolic BP over 140 mmHg was reported as a fraction of the week with uncontrolled hypertension. Together these 3 variables guided the physician in making a determination of a decision to escalate pharmacologic therapy. Results: After monitoring and interventions there were significant decreases in the patients’ average BP (130.6 mmHg vs. 135.6 mmHg, p=0.027). Neurological (headache, vision changes) and cardiac symptoms (chest pain) were resolved or improved in 83% of patients (5 of 6). By end of the pilot 4.2% (2 of 48) remained hypertensive as compared to the initial 43.8% (21 of 48) found to be hypertensive at start of pilot; 95.8% (46 of 48) remained controlled by end of study. Furthermore, when compared with a similar cohort of patients, hospital admissions were 80% (2 versus 10) fewer in the study population. Conclusions: In agreement with our hypothesis, more frequent BP measurements and medication adjustments led to increased rates of hypertension control. We believe that normotension is achievable using cloud-based monitoring, reporting, and treatment. In conclusion, this new method requires serious consideration versus the current standard of care and could help provide improvements in skilled nursing facilities and other care settings.

Improving Patient Safety in Hospitals through Usage of Cloud Supported Video Surveillance.

BACKGROUND:Patient safety in hospitals is of equal importance as providing treatments and urgent healthcare. With the development of Cloud technologies and Big Data analytics, it is possible to employ VSaaS technology virtually anywhere, for any given security purpose.AIM:For the listed benefits, in this paper, we give an overview of the existing cloud surveillance technologies which can be implemented for improving patient safety.MATERIAL AND METHODS:Modern VSaaS systems provide higher elasticity and project scalability in dealing with real-time information processing. Modern surveillance technologies can prove to be an effective tool for prevention of patient falls, undesired movement and tempering with attached life supporting devices. Given a large number of patients who require constant supervision, a cloud-based monitoring system can dramatically reduce the occurring costs. It provides continuous real-time monitoring, increased overall security and safety, improved staff productivity, prevention of dishonest claims and long-term digital archiving.CONCLUSION:Patient safety is a growing issue which can be improved with the usage of high-end centralised surveillance systems allowing the staff to focus more on treating health issues rather that keeping a watchful eye on potential incidents.

Infrared Human Posture Recognition Method for Monitoring in Smart Homes Based on Hidden Markov Model

Smart homes are the most important sustainability technology of our future. In smart homes, intelligent monitoring is an important component. However, there is currently no effective method for human posture detection for monitoring in smart homes. So, in this paper, we provide an infrared human posture recognition method for monitoring in sustainable smart homes based on a Hidden Markov Model (HMM). We also trained the model parameters. Our model can be used to effectively classify human postures. Compared with the traditional HMM, this paper puts forward a method to solve the problem of human posture recognition. This paper tries to establish a model of training data according to the characteristics of human postures. Accordingly, this complex problem can be decomposed. Thereby, it can reduce computational complexity. In practical applications, it can improve system performance. Through experimentation in a real environment, the model can identify the different body movement postures by observing the human posture sequence, matching identification and classification process. The results show that the proposed method is feasible and effective for human posture recognition. In addition, for human movement target detection, this paper puts forward a human movement target detection method based on a Gaussian mixture model. For human object contour extraction, this paper puts forward a human object contour extraction method based on the Sobel edge detection operator. Here, we have presented an experiment for human posture recognition, and have also examined our cloud-based monitoring system for elderly people using our method. We have used our method in our actual projects, and the experimental results show that our method is feasible and effective.

A cloud-based monitoring system via face recognition using Gabor and CS-LBP features

Face detection and recognition is an important topic in security. Currently, ubiquitous monitoring has received a large amount of attention. This paper proposes a cloud-based ubiquitous monitoring system via face recognition. It consists of a monitoring client module for face detection and recognition and a cloud storage module for data visualization. In the monitoring client module, the center-symmetric local Gabor binary pattern feature extraction method is proposed for face recognition, which combines improved multi-scale Gabor and center-symmetric local binary pattern (CS-LBP) features. This method maintains crucial local features, reduces the Gabor filter complexity, and adds rotational invariance and more precise texture information. A large number of experiments on the ORL, Yale-B, and Yale databases show that the proposed method obtains significantly better recognition rates than the LBP, CS-LBP, and Scale Gabor methods. Furthermore, we propose a Web browser-based data visualization that renders the geographic locations of the face detection and recognition results.

Development of the Internet-Enabled System for Exercise Telerehabilitation and Cardiovascular Training.

Sustained exercise training could significantly improve patient rehabilitation and management of noncommunicable diseases in the community. This study aimed to develop a universal telecare system for delivery of exercise rehabilitation and cardiovascular training services at home. An innovative bilateral leg training device was equipped with an electronic system for the ongoing measurement of training activities with the device. A single-item parameter reflecting the intensity of training was monitored using several modern telecommunication technologies. According to the application protocol, eight volunteers first tried the device for 30-60 min to determine their personal training capacity. Then, they were provided with equipment to use at home for 4 weeks. Adherence to daily training was assessed by the number of training days per week, training intensity, and duration of training sessions. The system provided reliable recording of training activities with the device using (1) long-term data logging without an ongoing connection to the computer, (2) wireless monitoring and recording of training activities on a stand-alone computer, and (3) a secure cloud-based monitoring over the Internet connection using electronic devices, including smartphones. Overall analysis of recordings and phone feedbacks to participants took only approximately 5 h for the duration of study. This study, although of a pilot nature, described the comprehensive exercise telerehabilitation system integrating mobile training equipment with personalized training protocols and remote monitoring. A single-item electronic parameter of the system usage facilitated time-effective data management. Wireless connection allowed various locations of device application and several monitoring arrangements ranging from real-time monitoring to long-term recording of exercise activities. A cloud-based software platform enabled management of multiple users at distance. Implementation of this model may facilitate both accessibility and availability of personalized exercise telerehabilitation services. Further studies would validate it in the clinical and healthcare environment.

Novel opportunities for wildlife conservation and research with real‐time monitoring

The expansion of global communication networks and advances in animal-tracking technology make possible the real-time telemetry of positional data as recorded by animal-attached tracking units. When combined with continuous, algorithm-based analytical capability, unique opportunities emerge for applied ecological monitoring and wildlife conservation. We present here four broad approaches for algorithmic wildlife monitoring in real time--proximity, geofencing, movement rate, and immobility--designed to examine aspects of wildlife spatial activity and behavior not possible with conventional tracking systems. Application of these four routines to the real-time monitoring of 94 African elephants was made. We also provide details of our cloud-based monitoring system including infrastructure, data collection, and customized software for continuous tracking data analysis. We also highlight future directions of real-time collection and analysis of biological, physiological, and environmental information from wildlife to encourage further development of needed algorithms and monitoring technology. Real-time processing of remotely collected, animal biospatial data promises to open novel directions in ecological research, applied species monitoring, conservation programs, and public outreach and education.

MOBILE CLOUD COMPUTING FRAMEWORK FOR PATIENTS' HEALTH DATA ANALYSIS

The advent of cloud computing and the ubiquity of broadband wireless coverage and wide spread usage of smart phones around the world carries the potential for transforming health care services, reducing health care cost and ensuring faster care for urgent cases. To these objectives, we present a cloud-based mobile health monitoring solution that takes advantage of cloud infrastructure and mobile processing capability to address the rising cost of health care monitoring. The solution enables health care providers to remotely analyze, monitor and diagnose patient's data. The solution integrates a powerful data analysis tool, cloud computing and mobile services. This paper presents a proof of concept that has been developed to monitor, record and analyze heart rate. The design enables a physician to develop custom analysis and monitoring to collect key indicator or set alerts without a need for infrastructure implementations to store or transfer the data.

Cloud Computing Application in Building Monitoring System

Popularization of the building monitoring system requires huge data storage, information management and computing services. However, the analog signal transmission cannot meet the requirements anymore, therefore cloud computing has to play its role. For this purpose, this article brings up an overall design idea and physical deployment of a cloud-based building monitoring system.