1. Introduction
The Internet of things (IoT) is a crucial technological innovation in networking. IoT has brought limitless prospects and influenced daily life [
1,
2,
3,
4]. It will bring a revolution in healthcare and biomedical infrastructure. A reliable and accurate IoT-based healthcare monitoring system is a challenging goal in modern-day society [
5]. The need to provide good-quality healthcare to the people by reducing cost, improving accuracy, and fulfilling the insufficiency of the medical staff are important reasons to worry.
The UNICEF report [
6] in the year 2016 stated that India is among the riskiest countries for neonates. According to a study by UNICEF, a government with a low-income level has a higher health mortality rate. Providing quality care to patients by reducing the cost and shortages of nursing supervisors is the primary concern. Recent global population aging and the pervasiveness of chronic illnesses are also becoming major issues [
7]. Premature birth also results in a high chance of prolonged run diseases influencing the child and caretaker [
8,
9,
10].
The health monitoring system provides nursing and crucial care for infants. The Internet of things (IoT) is considered a revolution for information and communication technology as it happened at the beginning of the 21st century [
11]. It provides a platform to connect sensors, databases, and other devices to the Internet. It enables a global infrastructure for physical networked architecture working on the web [
12]. The architecture should be highly synchronised and accurate to deploy the IoT for critical applications, such as health monitoring systems.
The proposed system’s main aim is to provide a simple, economic, multifunctional, and convenient health monitoring system that can constantly take readings of various parameters and provide information about patients’ health conditions. The health monitoring system comprises parameters such as body temperature, heartbeat, room temperature, humidity, etc. The sensors calculating the above-stated parameters are connected to a central processing unit (CPU). The CPU processes the acquired data and displays them on a monitor using a graphical user interface. It also stores the data in the health report, which plots the data to show the variations of various parameters and the number of times the value occurred (frequency). Thus, the main contribution of this paper can be summarised as follows.
This paper aims to design an efficient IoT-aware health monitoring system that leverages the characteristics of various body and room sensors at the physical layer. It helps provide efficient nursing care while sustaining the quality of services at the application layer.
Deployment of a network-layered architecture includes generating data at the physical layer to access, process, and transmit data at the network layer for analysis and decision-making at the decision-support layer, and application support for health care practitioners and patient caretakers.
Analytical proof that the proposed system results in increased healthcare facilities and reduces the effort of the medical consultants. The data collected during the process are also accurate and will be analysed.
The proposed system is also reviewed through simulations discussing the collected and actual data during the monitoring process. The results observed the proposed solution’s gain and ease of use.
Further, the paper is arranged in various sections.
Section 2 presents the motivation and literature survey behind the research.
Section 3 gives an overview of the IoT-based health monitoring system architecture.
Section 4 analyses the sensors deployed during the experimental setup for the proposed approach. The sensors were analysed based on their performance and accuracy in patient data collection. It also discusses the clock synchronisation issues and an adaptive non-linear clock synchronisation technique. The research is concluded in
Section 5.
2. Literature Review
Recent technological advancements have opened gates for deploying devices to make intelligent environments. Specifically, in medical sciences, various sensors are developed to measure vital signs such as body temperature, pressure, movement, ECG, heart rate, etc. This development motivates the design of innovative facilities capable of improving the patient’s healthcare. Among the various research activities presented in the literature, those related to using sensors for health care and told to synchronise time between the sensors of WSNs are mainly focused. In [
13], an intelligent WSN is presented for nursing patients’ monitoring, tracking, and localisation facilities within health care and nursing institutions. In [
14], architecture for automatic tracking and monitoring of patients is presented based on the RFID, 6LoWPAN, WSN, and constrained application protocol.
Additionally, established literature related to the sensors and WSNs are implemented to meet the definite requirements for ongoing health care. In [
15], an automatic health monitoring system is presented based on WSN sensors and mobile cloud computing (SMCC). It can detect hyperthermia, hypothermia, cardiac issues, and irregular body movements. It also provides information on Android-based applications and stores the data in the cloud. In [
16], a WSN-based reliable jaundice detection system is presented. The system was implemented for healthcare industries and intensive care units (NICU). An integrated monitoring system [
17] for women is introduced using mobile cognitive radio and body sensors. These are connected to a WSN. Wireless sensor networks are also deployed to analyse bioelectric signals produced by the human body. This deployment’s significant problems are energy consumption and radioelectric interference since these networks consist of small and limited nodes. In [
18], two different priority schemes were proposed to improve the performance of these networks. They consist of reducing the number of transmissions from low-energy nodes and prioritising data toward the sink node for fast and efficient processing.
A survey on the state-of-the-art of radio frequency identification (RFID) is discussed by Sara A. [
19]. It is applied to body-centric systems for collecting data related to the living environment of user-based on temperature, various gases, and humidity.
A physical layer data-specific transceiver design for healthcare IoT applications is proposed by [
20] that inherits generated information characteristics and reduces data transmitted with overheads. It also maintains the quality-of-service requirements of the application. Various data compression techniques were also analysed to improve e-health applications. A codebook-based online single-data compression technique is applied to monitor patients’ health using wearable devices [
21]. It will help in representing data patterns efficiently. Another lossy compression algorithm for biometric signals is analysed in ECG [
22] data using auto-encoders. A mental disorder monitoring system using EEG [
23] data are examined for lightweight 1.5-D multi-channel compression. A remote patient monitoring system for lossy compression techniques using multidimensional bio-medical signals is anticipated [
24] using linear prediction based on the codebook approach.
Publication No.US20160015277 [
25] relates the method of video evaluation of a patient for the heart rate and respiration rate under dim light or at night. This device comprises a video camera along with a source of infrared light. It evaluates the patient’s heart rate and respiratory rate using plethysmograph analysis. Patil and Mhetre [
26] discuss the patient monitoring system based on the GSM network, working only in the emergency case or when the parameter value is out of the described range. It does not store the benefits of health parameters as it is designed over a microcontroller PIC18F4520 which does not provide space to store the health parameter values. De et al. [
15] present a sensor–mobile cloud computing system for automatic neonatal health monitoring.
The article [
27] discussed essential parameters for monitoring a newborn’s health, such as sleeping activity, oxygen level, respiration patterns, etc., necessary for ensuring salubriousness for their health. This approach lets parents observe the patient and ensure their good health. Another article [
28] presents critical parameters such as body temperature, pulse rate, and moisture for monitoring patient health. It also presents the storage for measured values on the cloud with suitable security. In [
29], a low-cost incubator for monitoring premature baby health is proposed. It majors the critical health parameters in real-time. It automatically sends the alerts and immediately takes necessary actions to safeguard babies. IoT-based flexible, pervasive, intelligent healthcare platforms for various healthcare, physiological, and environmental parameters monitoring are discussed in [
30,
31,
32]. Sun et al. defined a privacy-aware and lightweight fine-grained access control mechanism for IoT-oriented smart health [
33].
As all the above, the discussed research focuses on designing a smart and intelligent healthcare system for monitoring and analysing patients. There is still much scope for improvement in the techniques used for data exchange between the monitoring and analysing devices. A strong, synchronised patient healthcare system is a requirement in high demand.
3. Architecture Overview
This paper aims to design a reliable, accurate, and IoT-based health monitoring system. The system visualises a real-time environment by collecting the patient’s body parameters and providing them to the control centre. The data collected are analysed at the decision support layer. Accordingly, alert or warning messages are sent in case of emergency.
Figure 1 and
Figure 2 illustrate the sensor deployment and network architecture for the IoT-based health monitoring system. It comprises sensors to monitor body temperature, respiration, heart rate, blood pressure, room temperature, humidity, and ambient light. The data collected in real-time are transmitted to the IoT cloud for storage and further action. Hospitals, nursing institutes, and emergency centres are connected to the cloud. In case of any emergency, it generates alerts and warning messages which are communicated via mobile applications.
The network layer is connected to the processing unit for processing collected data and storage devices. After processing the data, the network layer transmits the data to the decision support layer. In this layer, the decision regarding the health of the patient is simulated. The choice is based on body activity, heart/respiratory activities, and room environment. An alert or a warning message will be generated based on data collected within 30 s. The messages are then transmitted to the application layer for further processing. The application layer then sends information to a local or remote user based on its knowledge base.
Figure 3 presents the working schema that depicts the network component and a communication protocol to connect the components.
Figure 4 shows the GUI for the mobile application of the proposed health monitoring system.
Figure 5 presents the proposed WSN-based IoT setup along with sensors for collecting the data. The structure was arranged using Raspberry Pi and various body and room sensors. The system presented here is the functional layout of the physical layer. As shown in
Figure 2, this layer is responsible for collecting data and gathering information. These data are then transmitted to the network layer. At the network layer, the reading obtained is stored in a database and processed accordingly. A wireless ZigBee XBP24-ZB architecture is used to transmit readings through the gateway to the cloud database.
Table 1 presents the specification of the proposed WSN architecture.
The application layer is classified based on various factors such as the health monitoring business model, the network used, availability, coverage, heterogeneity, and real-time data requirements.
Table 2 presents the characteristics of the smart health monitoring application domain. Significant factors are network connectivity, network size, and bandwidth requirements.
The decision support layer provides management services to the above layer. It provides an operational support system service analytical platform, including statistical analysis, data mining, etc. It also performs periodic IoT data filtering and triggers periodic events based on sensor data, which may require immediate response and delivery. The network layer focuses on communication technologies. Routers, switches, and hubs are required to transmit a massive volume of IoT data to the storage or cloud. For the proposed system, a LAN relates to a microcontroller for transmission.
Finally, the physical layer comprises sensors and intelligent devices for collecting information.
Section 5 discusses the deployment of sensors and their analysis at the physical layer.