Читать книгу Design and Development of Efficient Energy Systems - Группа авторов - Страница 67

In 2014, Sourav, et al. [8] proposed a healthcare monitoring system that can be used to monitor all the sensors that could function together; the interference within the sensors are removed. Each sensor’s delay in monitoring equipment and sample rate maintenance are mandatory for monitoring the healthcare system. As there are only limited resources, this system provides maintenance of best possible sampling rate and better healthcare quality. A variety of sensors are maintained simultaneously and with better quality of the data transfer, network bandwidth is used effectively.

In 2016, Vippalapalli, et al. [43] proposed an IoT-based smart healthcare system that collects data through wearable sensors. It is a low-cost system; the sensors are used to collect a patient’s real-time healthcare data, and that data is shared among themselves, analyzed and stored. It eliminates all the inefficiencies in the manual process. The data acquisition process is carried out by a wearable device based on Audrino with Body Sensor Network. This framework is integrated to Labview for providing remote monitoring of patients. In 2016, Dinesh, et al. [9] proposed a hadoop framework for monitoring the healthcare of the patient based on IoT, in which big data is used for analyzing the healthcare data and generating an emergency alert when necessary. Body Sensor network (BSN) is used for extracting the critical information. A summary of the observed critical data is sent to the healthcare provider on a real-time basis, thus improving the standards of the healthcare.

In 2017, Kinthada, et al. [19] proposed a framework which is used for monitoring patients’ medicine intake. It is used to monitor the dispensing of prescribed medicine and tracking the history of medication, including any dosage that has been missed. With the help of alarms, it sends an alert notification to the patients to take their medications. If the patient misses the dosage then a notification is sent to the healthcare provider and in times of emergency, medical staff are alerted. In 2017, Pinto, et al. [31] proposed an IoT-based living assistance for aged people that has the capability to monitor and store all the vital information regarding the patients’ if an emergency situation arises an alarm will be triggered. This work comprises a wrist band which is connected to a cloud server for monitoring and assisting the old-aged people. It is a low-cost solution working on low power with devices that have wireless communication. In 2017, Kirttana, et al. [20] proposed Heart Rate Variability (HRV) monitoring systems for remote hypertensive patients based on IoT. It is designed as a user friendly and low-cost system. HRV is used for measuring the variation of the time interval that is observed between consecutive sequences of heartbeats. The analysis of HRV can be used for the deduction of diabetics, cardiovascular diseases, chronic conditions and hypertension-related diseases. HRV data are monitored to deduct these type of diseases. In the proposed work, the data acquisition is carried out by a sensor based on wireless zigbee. The collected data are used to calculate the parameters of HRV system. The collected data from the patient are transmitted by a system based on arduino to the backend server that is using an IoT protocol called MQTT. The HRV data at the server is plotted as a graph.

In 2015, Madakam, et al. [23] proposed an overview about the IoT, its architecture and different technologies and its usage in day-to-day life. One of the major observations in the document is that IoT has no standard definition. These technologies vary from vendor to vendor and so interoperability is needed. The architectural level requires universal standardization. No standard protocols are created for global management betterment.

In 2017, Taleb, et al. [37] published a survey on Mobile Edge Computing (MEC) that explores the enabling technologies. The MEC deployment considers the MEC network platform with the mobility support and individual service perspectives. The mobile edge computing reference architecture, which offers third-party, content provider and multitenncy support to the developers’ application, is analyzed. In 2017, Mao, et al. [24] proposed a survey based on the mobile edge computing start of art technology. This proposal mainly focused on optimizing the computational resources and radio network. In 2017, Dolui, et al. [10] explored various edge computing types, such as Mobile Edge Computing, Cloudlet and fog computing along with the feature sets. To achieve real-time responses, edge computing becomes the research area for many researchers.

Observations from related works, in the solutions based on IoT, show the importance of context aware computing. The sensors, connectivity and computing technologies have been experiencing a bigger advancement in the past decade, thus now the focus is on developing low-cost wearables that could sense human health condition. Most of the applications in healthcare are now IoT-based systems. Many applications in real-time healthcare systems use cloud computing for computation and storage, but this has unpredictable or high network latency. Thus edge computing is preferred; it brings the data computation nearer to the user device. The usage of edge computing generates energy-efficient systems [7].