with each other. IoT devices have the capability to sense the surrounding environment and collect the data which can be processed by small computations within the IoT framework, which can lead to the development of real-time solutions. The real-time entities have their corresponding virtual entities. There also exists a communication between the virtual objects which is used to inform the real-life entities of the thing’s state. This control mechanism is also present within the IoT framework.
4.1.2 Using IoT to Enhance Healthcare Services
The IoT sensors and wearables are used vastly in the world today for various healthcare applications and services. IoT devices minimize the overall cost and save the lives of patients. IoT devices can be used to monitor real-time healthcare of patients, track patient activities, and collect patients’ data to provide an effective and mature solution [2]. The communication and interaction between patient and the healthcare provider becomes easier. In real-time monitoring of a patient, the wearable and sensor acquire data and sends it to the healthcare providers or doctors for monitoring health, which leads to the improvement of the treatment process [33]. IoT devices are connected with smart-phones for self-monitoring of health, and also support chronic diseases management, track patient, staff and equipment, and can be used to monitor aged patients [25]. Some of the challenges in IoT devices are, IoT devices are used in telehealth and telemedicine services. For example, the wireless glucometer, which is an IoT device, collects data from the patient and monitors the level of glucose on a real time-basis and sends notification to the patient’s mobile when there is a fall in the glucose level, indicating the need to take insulin. The advantages of IoT devices are monitoring remote patients, preventing unwanted hospitalization, minimizing cost, faster response time during an emergency, and effective treatment.
4.1.3 Edge Computing
Edge computing [26] brings data storage and computation closer; that is, these tasks are done within the IoT framework in the location that is nearer for the user end. It is a distributed computing, mainly designed to minimize the response time and to eliminate network latency, saving bandwidth. As the storage and computation is done in the edge nodes, data can be protected efficiently. When edge computing is coupled with machine learning technology, real-time solutions with high efficiency can be developed easily [4, 15, 39].
4.1.4 Machine Learning
Machine learning is basically the study of statistical models and algorithms with which the computer can automatically perform the specific tasks without any explicitly given human instruction. The computer relays on inference and patterns to perform the task. The training data is used to make the system learn to make decisions or predictions, thus eliminating the instructions that are programmed for performing tasks. Machine learning is currently used in a variety of industries to automate the process of decision making. For example, in the healthcare industry, machine learning is used in chronic disease management. It can, for example, predict seizures from electroencephalography data and notify the patient and healthcare provider before anything wrong occurs or it can perform a decision-making operation when an emergency situation arises [28, 38, 42].
4.1.5 Application in Healthcare
As population growth increases rapidly, the challenges to maintain patient health records and to analyze a huge amount of patient information also increase. Thus IoT and machine learning is used to automatically collect and process the huge set of data and thereby make healthcare systems more robust and dynamic. There are various applications of machine learning in the field of healthcare. These include identifying the diseases and diagnosing, smart health record systems, drug discovering and manufacturing, developing personalized medicines, emergency care, medical image diagnosis, clinical research and trails, disease outbreak prediction, etc. [16]. Some of the real-time applications are:
Determine sudden fluctuations in blood pressure. The observed fluctuations are analyzed and checked to see if they are normal or not and the emergency alert service is activated accordingly. Real-time monitoring is necessary to find such critical data and act accordingly. The data patterns of the patients are studied with the machine learning technique.
Closest healthcare center can be located and an ambulance can be guided there. The body sensors collect patient data which can be used to check up on the patient’s health.
Patient’s body posture and movement can be deducted and used to check if a patient is in need of help. Solutions on assisted living can be developed based on these types of services.
4.2 Related Works
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
