Cloud-based Monitoring System Research Articles

Leveraging deep learning and computer vision technologies to enhance management of coastal fisheries in the Pacific region

This paper presents the design and development of a coastal fisheries monitoring system that harnesses artificial intelligence technologies. Application of the system across the Pacific region promises to revolutionize coastal fisheries management. The program is built on a centralized, cloud-based monitoring system to automate data extraction and analysis processes. The system leverages YoloV4, OpenCV, and ResNet101 to extract information from images of fish and invertebrates collected as part of in-country monitoring programs overseen by national fisheries authorities. As of December 2023, the system has facilitated automated identification of over six hundred nearshore finfish species, and automated length and weight measurements of more than 80,000 specimens across the Pacific. The system integrates other key fisheries monitoring data such as catch rates, fishing locations and habitats, volumes, pricing, and market characteristics. The collection of these metrics supports much needed rapid fishery assessments. The system’s co-development with national fisheries authorities and the geographic extent of its application enables capacity development and broader local inclusion of fishing communities in fisheries management. In doing so, the system empowers fishers to work with fisheries authorities to enable data-informed decision-making for more effective adaptive fisheries management. The system overcomes historically entrenched technical and financial barriers in fisheries management in many Pacific island communities.

Ship Operation Analysis and Optimization via Mobile Application

This paper is devoted to studying the structure and prospects of ship operations analysis and optimization via mobile applications, focusing on integrating multiple existing onboard monitoring and control systems. The main parts of the paper describe the current state of the most essential components of future overall shipping and ship design optimization using onboard and cloud-based monitoring systems from a dual transition point of view. Special attention is paid to the ship’s and its equipment’s efficiency improvements, fuel consumption and emissions reduction, cost-effectiveness enhancement, metrological accuracy, and compliance with current regulations. Timely development and deployment of the proposed onboard monitoring systems, in combination with up-to-date mobile applications and cloud computing, should play a crucial role in promoting sustainable and environmentally friendly shipping practices, improving operational performance, and reducing risks to human life at sea and the environmental impact of shipping. Another objective of this research is to review the current state of infrastructure used for fuel control, focusing on measurement systems and related analytics using appropriate mobile applications. The recommendations for integrating new and adopting existing monitoring systems and equipment for promising alternative fuels must be given to meet current regulations and provide required safety levels and measurement quality.

Enhancing Turbine Blade Manufacturing through MEMS-Based Milling Monitoring

Manufacturing steam turbine blades with intricate shapes poses a substantial challenge. These blades significantly impact turbine efficiency, reliability, and productivity. Their precise formation through milling processes demands vigilant monitoring, especially concerning the cutting tool’s condition, which is responsible for shaping the blade profiles and ensuring high-quality outcomes. Direct tool monitoring, however, disrupts productivity, prompting the need for an indirect Tool Condition Monitoring (TCM) system. Such a system becomes essential for detecting tool wear and damage early, ensuring dimensional accuracy and surface smoothness. This experiment monitors tool conditions by analyzing vibrations generated by an endmill cutter while machining martensitic stainless steel (MSS) AISI 420. The setup employs a cost-effective MEMS vibration sensor, the ADXL 345, and Raspberry Pi for real-time online TCM functionality as the signal processor. This study delves into the sensor’s ability to capture vibrations representing actual tool conditions, focusing on the affordability and effectiveness of MEMS-based monitoring. The successful real-time implementation of MEMS-based monitoring could be an easily accessible gateway for manufacturing industries to embrace cloud-based monitoring systems, aligning with current technological trends. This research aims to underscore the viability and potential adoption of affordable MEMS sensors for comprehensive tool condition monitoring, offering a seamless transition toward cloud-integrated monitoring systems for manufacturing industries.

IoT-Based Big Data Secure Transmission and Management over Cloud System: A Healthcare Digital Twin Scenario

The Internet of Things (IoT) was introduced as a recently developed technology in the telecommunications field. It is a network made up of real-world objects, things, and gadgets that are enabled by sensors and software that can communicate data with one another. Systems for monitoring gather, exchange, and process video and image data captured by sensors and cameras across a network. Furthermore, the novel concept of Digital Twin offers new opportunities so that new proposed systems can work virtually, but without differing in operation from a “real” system. This paper is a meticulous survey of the IoT and monitoring systems to illustrate how their combination will improve certain types of the Monitoring systems of Healthcare–IoT in the Cloud. To achieve this goal, we discuss the characteristics of the IoT that improve the use of the types of monitoring systems over a Multimedia Transmission System in the Cloud. The paper also discusses some technical challenges of Multimedia in IoT, based on Healthcare data. Finally, it shows how the Mobile Cloud Computing (MCC) technology, settled as base technology, enhances the functionality of the IoT and has an impact on various types of monitoring technology, and also it proposes an algorithm approach to transmitting and processing video/image data through a Cloud-based Monitoring system. To gather pertinent data about the validity of our proposal in a more safe and useful way, we have implemented our proposal in a Digital Twin scenario of a Smart Healthcare system. The operation of the suggested scenario as a Digital Twin scenario offers a more sustainable and energy-efficient system and experimental findings ultimately demonstrate that the proposed system is more reliable and secure. Experimental results show the impact of our proposed model depicts the efficiency of the usage of a Cloud Management System operated over a Digital Twin scenario, using real-time large-scale data produced from the connected IoT system. Through these scenarios, we can observe that our proposal remains the best choice regardless of the time difference or energy load.

Cloud-Based Monitoring System for Foam Content at the Wellhead of Foam Drainage Gas Production

To achieve automatic online monitoring of foam content in wellhead pipelines during the foam drainage gas production of shale gas, a cloud-based monitoring system for the foam content at the wellhead of foam drainage gas production is proposed. The system mainly comprises a terminal foam content detection sensor and a cloud platform, which relies on a wireless network for data communication. In this system, the foam content detection sensor uses the absorption of infrared light by the foam in the pipeline as a basic detection principle. Furthermore, the sensor uses signal-processing circuits to convert infrared light of different intensities into model voltage signals of different sizes and uses LoRa wireless networks to transfer the measurement data to a server for storage in a time-series database while displaying real-time foam content data on a web page. The monitoring system was tested in a field, and the relationship model between the foam content and the detection voltage signal was fitted based on the test data. Based on this model, and then tested and compared with the manual sampling results, the results of the 40 data samples tested showed that the monitoring system was effective and met the field working conditions. The foam content calculated by the fitted model compared with the manually measured foam content showed an error range of 0.11%–15.37%. The test results show that the system has functional requirements to monitor foam content in the field, contributing to the automation, networking, and cloud-based monitoring of the foam content at the wellhead.

Dump Yard Fire Extinguishing System Using IoT

Nagpur municipal corporation (NMC) is the best waste disposal company. Nagpur municipal corporation (NMC) collects everything from household waste and stores all the garbage in different landfills. But these landfills pose a major problem for storing this waste safely as it catches fire very quickly. These toxic gases such as dioxins, furans, mercury and polychlorinated biphenyls are then released into the atmosphere and pollute the air with an unpleasant smell. The design we propose is based on a fire alarm and a landfill fire extinguisher. When garbage is gathered for the dump, our project flashes a warning. Smoke alarm, temperature sensor, and Wi-Fi module powered by batteries Our sensor checks for smoke and temperature; if either is present, an alarm will be shown on the signal from the Wi-Fi module fi that is linked to a junkyard. An IoT-based fire extinguishing system is proposed for dump yards, providing an efficient and unique approach to prevent and extinguish fire outbreaks using sensors, actuators, and a cloud-based monitoring system. This research presents an innovative IoT-based fire extinguishing system for dump yards that incorporates sensor nodes to detect fire, actuator nodes to discharge extinguishing agents, and a cloud-based monitoring system for remote monitoring and management. The proposed system provides an efficient and effective approach to prevent and control fire outbreaks in dump yards, ensuring safety and environmental protection. This paper presents a novel IoT-based fire extinguishing system for dump yards, which utilizes sensor nodes to detect fire outbreaks and actuator nodes to deploy extinguishing agents. The system also includes a cloud-based monitoring and control system that enables remote monitoring and management of the system. The proposed system aims to provide an efficient and reliable approach to prevent and extinguish fire outbreaks in dump yards.

Distributed fibre optic inclinometer with cloud-based monitoring system

Geotechnical instrumentation and monitoring are important aspect of controlling subsurface ground movement, particularly in deep excavations and forewarning of geological disasters such as landslides, land subsidence, ground fissure, and surface collapse. A borehole inclinometer is an example of the established method to monitor such ground movements. For real-time data, a series of in-placed inclinometer probes are installed inside the borehole casing to provide the lateral displacement response over time. The limitation of the conventional system is the high cost and the practicality of inserting a high number of sensing probes into the casing for better measurement accuracy. Recent advancement in Distributed Optical Fibre Strain Sensing (DOFS) enables the incorporation of DOFS as a fibre optic (FO) inclinometer with lateral displacements to be plotted at centimetres spacing and is unaffected by the gauge lengths between the probes. This study enhances the FO inclinometer by developing a web-based application (SmartGeopipe©) to automate data acquisition which also includes complex data processing for the final pipe deflection profile visualisation. The FO inclinometer comprised of optical cables attached externally onto a remodified inclinometer casing and connected to a Brillouin Optical Time Domain Analysis (BOTDA) strain analyser. A standard installation procedure was established to safely secure the sensors during the casing installation process inside the borehole. SmartGeopipe© software is written using a predominantly monolithic Python / Cython backend and a multi-page frontend built with HTML, CSS, and JavaScript ES5 languages. All the computation work and data storing are done in a cloud server and SQL database, respectively. A case study of a station box excavation as part of the Mass Rapid Transit (MRT) tunnelling project was chosen to validate the performance of the FO inclinometer and the web application. Data from the FO inclinometer corroborated well with the manual measurements of the inclinometer probe. This ability supports the realization of an early warning system as an engineering control for hazard management in civil engineering infrastructure projects.

DNS Tunnelling, Exfiltration and Detection over Cloud Environments

The domain name system (DNS) protocol is fundamental to the operation of the internet, however, in recent years various methodologies have been developed that enable DNS attacks on organisations. In the last few years, the increased use of cloud services by organisations has created further security challenges as cyber criminals use numerous methodologies to exploit cloud services, configurations and the DNS protocol. In this paper, two different DNS tunnelling methods, Iodine and DNScat, have been conducted in the cloud environment (Google and AWS) and positive results of exfiltration have been achieved under different firewall configurations. Detection of malicious use of DNS protocol can be a challenge for organisations with limited cybersecurity support and expertise. In this study, various DNS tunnelling detection techniques were utilised in a cloud environment to create an effective monitoring system with a reliable detection rate, low implementation cost, and ease of use for organisations with limited detection capabilities. The Elastic stack (an open-source framework) was used to configure a DNS monitoring system and to analyse the collected DNS logs. Furthermore, payload and traffic analysis techniques were implemented to identify different tunnelling methods. This cloud-based monitoring system offers various detection techniques that can be used for monitoring DNS activities of any network especially accessible to small organisations. Moreover, the Elastic stack is open-source and it has no limitation with regards to the data that can be uploaded daily.

MACHINE LEARNING AND IOT-BASED LI-ION BATTERY CLOUD MONITORING SYSTEM FOR 5G BASE STATIONS

With the accelerated construction of 5G and IoT, more and more 5G base stations are erected. However, with the increase of 5G base stations, the power management of 5G base stations becomes progressively a bottleneck. In this paper, we solve the problem of 5G base station power management by designing a 5G base station lithium battery cloud monitoring system. In this paper, first, the lithium battery acquisition hardware is designed. Second, a new communication protocol is established based on Modbus. Third, the windows desktop upper computer software and the cloud-based monitoring system are designed. Finally, this paper designs the improved ResLSTM algorithm which is fused with ResNet algorithm based on Stacked LSTM. The algorithm designed in this paper is tested in comparison with SVM and LSTM. The performance of the algorithm designed in this paper is better than SVM and LSTM. Furthermore, the communication test, as well as the training and testing of the ResLSTM algorithm are outstanding. The 5G base station lithium-ion battery cloud monitoring system designed in this paper can meet the requirements. It has great significance for engineering promotion. More importantly, the ResLSTM algorithm designed in this paper can better guide the method of lithium-ion battery SOC estimation.

0709 The Efficacy of Home Sleep Apnea Tests Alone in Determining Optimal Treatment Modality for Sleep Disordered Breathing During the Height of the COVID-19 Pandemic

Abstract Introduction Home sleep studies have shown strong accuracy and reliability in diagnosing obstructive sleep apnea compared to PSG. Recent studies have suggested they can accurately detect central sleep apnea as well. The combination of better technology, stricter insurance requirements for in lab polysomnograms, and a rise in telemedicine has seen their utilization rapidly increase. Specifically, at the height of the COVID pandemic many sleep practices had to shut down their labs and rely on HSATs alone to evaluate patients with potential sleep disordered breathing. Methods The Albuquerque VA Sleep Center was one of these, which provided an opportunity to reflect on the effectiveness of this diagnostic modality over that timeframe. A total of 780 patients with suspected sleep disordered breathing were studied using ResMed ApneaLink II Machines from 3/16/21 to 7/1/21 while in lab PSGs were unable to be completed due to health and safety guidelines. Results Of these 780 patients, only 34 were determined to need further evaluation with an in lab titration study once the lab reopened. Given how few of these patients ended up with titration studies, no additional criteria were used to categorize them other than a provider deciding they needed the study. The charts of these patients were reviewed in detail to identify any common characteristics that could have contributed to them needing a more detailed evaluation with an in lab polysomnogram. This provided further information about the accuracy and reliability of HSATs, as well as traits of patients who would have been ideally studied with an in lab PSG instead. Conclusion Overall such a small percentage of patients, only 4%, needing further titration speaks to both the reliability of HSATs as diagnostic studies, and the effectiveness of remote titration through cloud based monitoring systems like AirView. Support (If Any)

0772 Clusters of Upper Airway Stimulation Adherence Patterns in the First 90 Days

Abstract Introduction Upper airway stimulation (UAS) therapy is effective for a subset of obstructive sleep apnea (OSA) patients with CPAP intolerance. While overall adherence is high, some patients have suboptimal adherence to UAS, which limits effectiveness. Our goal was to identify UAS therapy usage patterns during the first three months of therapy that affect adherence. Methods We retrieved anonymized UAS therapy usage data from 2,091 individuals stored in a cloud-based monitoring system during the first three months after device activation. We aggregated adherence data including mean and standard deviation (SD) of nightly hours of use, therapy pauses, hours from midnight when the therapy was turned ON and OFF, and percentage of missing days. We computed the difference of the stimulation amplitude between the first and last day. We performed cluster analysis with Gaussian mixture models and computed the centroids of each cluster highlighting their main differences. Results We identified six distinct clusters of UAS usage patterns. Clusters 1A (34% of the total cohort) and 1B (23%) had excellent therapy usage with 7.23h and 7.14h on days of use, respectively; with 1B distinguished by increased night-to-night variability. Clusters 2A (16%) and 2B (12%) had good mean therapy use of 6.63h and 6.21h, respectively, but their usage patterns were distinguished by a higher percentage of missing days (8% missing days in 2A and 23% in 2B) and less favorable therapy timing with an average therapy ON time after midnight. Clusters 3A (8%) and 3B (7%) were characterized by the lowest nightly use at 6.16h and 5.50h, respectively, and the highest night-to-night variability. 3A was further distinguished by the highest percentage of missing days (34%) while 3B was characterized by the frequent therapy pauses (mean 4.1 pauses per night) and the least increase in stimulation amplitude across the first 90 days. Conclusion Cluster analysis of UAS usage patterns identified six distinct groups that may enable custom interventions for improved long-term management. Differentiation of these groups may have clinical implications on conditions (e.g. therapy discomfort, comorbid insomnia, poor sleep hygiene) that impact adherence. Support (If Any)

Physics-informed deep learning for signal compression and reconstruction of big data in industrial condition monitoring

The onset of the Internet of Things enables machines to be outfitted with always-on sensors that can provide health information to cloud-based monitoring systems for prognostics and health management (PHM), which greatly improves reliability and avoids downtime of machines and processes on the shop floor. On the other hand, real-time monitoring produces large amounts of data, leading to significant challenges for efficient and effective data transmission (from the shop floor to the cloud) and analysis (in the cloud). Restricted by industrial hardware capability, especially Internet bandwidth, most solutions approach data transmission from the perspective of data compression (before transmission, at local computing devices) coupled with data reconstruction (after transmission, in the cloud). However, existing data compression techniques may not adapt to domain-specific characteristics of data, and hence have limitations in addressing high compression ratios where full restoration of signal details is important for revealing machine conditions. This study integrates Deep Convolutional Autoencoders (DCAE) with local structure and physics-informed loss terms that incorporate PHM domain knowledge such as the importance of frequency content for machine fault diagnosis. Furthermore, Fault Division Autoencoder Multiplexing (FDAM) is proposed to mitigate the negative effects of multiple disjoint operating conditions on reconstruction fidelity. The proposed methods are evaluated on two case studies, and autocorrelation-based noise analysis provides insight into the relative performance across machine health and operating conditions. Results indicate that physically-informed DCAE compression outperforms prevalent data compression approaches, such as compressed sensing, Principal Component Analysis (PCA), Discrete Cosine Transform (DCT), and DCAE with a standard loss function. FDAM can further improve the data reconstruction quality for certain machine conditions.

A cloud-based monitoring system for performance analysis in IoT industry

As enterprise information systems grow in scale and computing resources remain limited, some computing system services run into occasional abnormalities such as degraded stability and the failure to respond in time. Fewer monitoring tools mean that system maintenance managers may not be notified when abnormal events occur. It becomes difficult to diagnose and manage the problems promptly, to decrease service interruptions and to fully grasp how computing resources are being utilized. Monitor systems that provide enterprise-wide services must perform better if they are to meet the needs of customers. Therefore, it is necessary for the system administrator to monitor the system. In this case study, we propose and develop a cloud-based monitor system that uses Java to run on the J2EE platform. We build a cloud-based performance analysis and monitoring mechanism whose system architecture has three components: a server resource performance monitor, an enterprise application systems monitor and an abnormal event notification system. The performance analysis and monitoring mechanisms are integrated and include an active diagnosis system and maintenance module to issue notifications when any system experiences abnormal operations. This results in an increase in enterprise system availability and effectively lowers the frequency of abnormal operations. System resource usage reports compiled from the data enable the optimal allocation of the enterprise’s limited computing resources. This monitoring system ensures high quality and lowers the operational cost of providing information services, enhancing the provider–customer relationship.

IoT-Based Air Quality and Sound Intensity Monitoring System using Raspberry Pi

In day to day life, the increase in Air and Sound pollution has become a distressing problem. It has now become a vital issue that is to be considered. To overcome this problem, an IoT based system to monitor the pollution levels constantly has been proposed. Nowadays Internet of things (IoT) is one of the most widely used and researched technology to monitor the environmental changes. It gives an innovative approach where various devices can be connected together with the use of the internet. By interconnecting different objects located at different locations, we can collectively analyze the data at a single place. This feature is useful in data analytics. Raspberry Pi mini-computer is used to collect different data from different sensors and this data is monitored. In our proposed system we are using four different modules namely Air Quality Monitoring System, Sound Intensity Monitoring System, Cloud based Monitoring System, Notification system. These modules are integrated together to monitor the environmental changes. This system can be implemented in remote areas where the bulky equipment cannot be placed. Industrial areas where the pollution levels are high can be constantly monitored and precautionary measures can be implemented if the pollution is more.

Development of IoT Based Smart Dustbin Monitoring System

The objective of this proposed system is to stay clean and to cover all urban areas of Indian cities. In existing waste collection systems, there is no proper planning for waste collection due to certain problems that make the city unsanitary. The work which consists in cleaning the trash cans is not responsible and which aggravates the system in the event of emergency, an appropriate monitoring is compulsory to keep the city clean and green. Only the manual waste collection system is available. There are no automatic or technological systems. This proposed system is designed for efficient waste collection using the Internet of Things. The system uses a cloud-based monitoring system for monitoring waste. With the use of a cloud-based system, there is no need to regularly check the bins. The proposed model has dustbins contain Arduino based sensing and monitoring system which gives the information about the levels of filling of dustbins and locations. Whenever any dustbin is filled up, a message is sent to the concerned central station where regularly monitoring the levels and positions of dustbins is to be done. This station monitors all the dustbin levels and gives the directions of collecting the garbage by sending message to the garbage collectors and authority. This will avoid overflow of waste in the bin. A prototype of proposed system to be designed as per the consideration of all problems in garbage collection process and by this the proposal makes the city towards a smart city. That fulfils the goals of Swachhbharat.

A Cloud-Based Monitoring System for Performance Assessment of Industrial Plants

The paper presents the realization of a control-loop performance monitoring system operating in a cloud as a single unity for the global supervision of data coming from various industrial plants lo...

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.

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.

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.

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.