1. Introduction
Smart health care and smart hospitals are concepts that have emerged as a result of innovation in the healthcare sector with the key supporting of technologies, especially the Internet of Things (IoT), individualized facilities and intelligent devoted machines (in terms of artificial intelligence). IoT is a rapidly evolving technology with potential for distributed computing and rapid information sharing in large-scale networks.
Since the COVID-19 pandemic [
1], considerable investments have been made in health care, which an important sector in everyday human life. The increasing population and the rise in chronic diseases have increased the demand for hospital resources and monitoring technology [
2]. Appropriate solutions and facilities are required to minimize the pressure on healthcare systems and severe patients [
3]. A potential solution to minimize the load on healthcare systems is the use of IoT technology. Therefore, efforts have been devoted [
4,
5,
6,
7,
8] to the development of innovative healthcare solutions. A large amount of research has been conducted on patient monitoring in specific diseases such as Parkinson’s disease and diabetes [
7].
Many studies have focused on specific tasks, such as the combination of rehabilitation with regular tracking of patient progress [
8]. The importance of emergency healthcare support has also been recognized [
9,
10] but has not been widely researched. Various studies have reviewed specific health-related technologies. For example, the authors of [
11] conducted a review with a primarily focus on a commercially obtainable solutions, remaining problems and best possible applications. Various topics were considered in [
12,
13], including system storage, analysis and data mining, as well as system integration. The authors of [
10] investigated big data control and sensing in terms of network-supported communication.
This paper represents a crucial contribution in that we explore all key aspects of end-to-end IoT related to health care and suggest a collective model that can be applied to any healthcare system based on IoT. This work is important because no such system for end-to-end communication and remote tracking of health issues has been established to date. Herein, we provide a detailed review of IoT technologies that related to the suggested model. The novelty of this article lies in its focus on aspects of IoT related to healthcare systems. Low-power networks are among the major needs in IoT systems. Herein, we compare standard licensed bands such as low-power Bluetooth, narrowband IoT, etc., with alternatives with specific attentiveness and discuss their suitability for different healthcare applications. Smartphones play an important role in daily life; these devices can be connected to various types of sensors to track the health status of individuals [
14]. Such devices can sense and record various types of data for automatic and efficient control of health care [
15]. IoT healthcare systems can also be effectively used to track and monitor human resources [
16]. Cloud resources can be used to control healthcare data of healthcare through various strategies, such as data integration, storage flexibility and parallel processing, although all these methods are subject security issues [
17]. Implanted sensors or wearable devices can be used for IoT healthcare applications using a limited power supply. However, frequent charging may be required, and the use of such technology may result in patient fatigue, requiring additional care support and influencing user involvement [
18]. Cloud data centers consume considerable energy resources, increasing costs; on the other hand, cloud service requires minimal energy and provides low latency [
19]. Security is one of the major concerns associated with healthcare monitoring, as crucial data can easily be corrupted by hackers.
Therefore, data privacy and protection are important considerations in IoT healthcare systems with respect to data transmission [
20]. Few researchers have proposed secure platforms or resources for healthcare systems based on IoT data transmission [
21,
22]. Therefore, in this study, we analyze a healthcare system in terms of computational duration, accuracy and development issues with the aim of arriving at a superior solution. The benefits of a smart healthcare system using IoT are shown in
Figure 1.
3. Related Work
The distributed nature of the cloud environment makes it the best choice to store health-related data and provides a flexible means of remote patient monitoring by various experts. Therefore, the combination of cloud and IoT technologies is promising for the real-time processing of signals, which generally increases the complexity of the architecture required to receive and send information. In order to minimize the complexity between cloud and IoT technology, a framework is suggested to handle real-time IoT statistics, with unrelated scientifically based information verified in the cloud environment using tools such as hybrid services [
23].
A framework based on service management for IoT devices is suggested for the cloud environment that consists of three types of layers and five crucial elements to serve consumers and gather information from patients, smart homes, smart healthcare devices, social networks, etc. The next level includes a service provider that provides physical assets, visualization, supervision service and security and privacy features. The last layer is the intermediate layer, which will help to manage services between consumers and providers, relying on available assets.
The authors of [
24] suggested an IoT machine learning architecture for prior detection of human heart disease based on three-tier frameworks to gather data from sensors embedded in wearable devices. The suggested architecture is executed using Mahat (Apache) and Hbase (Apache) for data prognosis and cloud storage to help identify heart illness in the early phase.
The authors of [
25] designed a framework to track patients suffering from arthritis and identify them in early stages. The proposed framework is based on three stages, the first of which involves gathering data from sensors, followed by information storage in the cloud and optimization of the collected data containing patient information such as uric acid levels and data on swelling in the body. Open stack Apache red displacement was used to design the model.
The authors of [
26] designed and visualized sensor devices for household applications such as smart rooms for patients suffering from chronic diseases. No real-time data were considered in the experiment, as the proposed approach is prohibitively expensive. The framework of this approach can be assumed, and sensors can be used in place of cameras to minimize the total cost of the experiment. The outline of the proposed IoT framework is depicted in
Figure 2.
The authors of [
27] suggested a fuzzy ant tree classifier comprising a combination of of ant colony development, fuzzy regulation and a settlement tree, which was tested for an application involving organization of medical history. A thorough review of medical history can help to address issues that arise in elderly patients, supporting diagnosis and decision making with respect to pain management, medication, etc. The role of IoT in the proposed framework is to gather real-time information from sensors and identify health issues by verifying behavioral trends and physical need at home. Few researchers have conducted studies involving the support of elderly people using IoT tracking devices. Services for elderly people are often based on social platforms, enabling tracking using IoT devices. Sudden falls in elderly patients due to muscle pain can lead to death in some cases. To overcome such issues, an algorithm known as the fall identification algorithm was suggested to identify their condition and the specified area where the maximum chances of accident arise.
Open issues include cloud integration, big data handling, security, etc. Data received by IoT sensors can be effectively stored in cloud servers, and IoT devices can be used to transmit data to different cloud subsystems for disease identification. Cloud and big data integration may solve problems associated with medical data management. It is very important to analyze the limitations of data privacy in term of security to increase network trust.
The specified location contains RFID and information recognition data that can be used to easily track and minimize such cases. Such research can support elderly people by providing technology to enable safe homes where they can reside safely, effectively and, most importantly, comfortably in a monitored environment with the ability to continuously alert healthcare units and their family members in the event of alert signals [
28,
29,
30].
Many researchers have suggested health-related devices to help society that may prove beneficial for the development of a smart healthcare system. Various types of pulse sensors have been developed for different applications. A chest-worn platform based on a wrist sensor is considered one of the most comfortable systems for the long term [
31]. Photoplethysmography (PPG) sensors function using LED relay lights inside arteries to measure pulse variation and the rate and flow of blood oxygen in a form of a small wearable wrist sensor [
32].
Pressure sensors also support health care by manually measuring radial pulse using fingers, as depicted in
Figure 3; the sensor is placed close against the wrist, and the pulse is measured to achieve a waveform. The authors of [
33] proposed and verified a pressure sensor that is highly sensitive and flexible for the detection of pulse, which was found to provide satisfactory results. One drawback of the proposed sensor is that with increased sensitivity, noise is also increased due to the motion of the wearer. The use of an RF array based on an eccentric pulse sensor has also been proposed to measure pulse at various locations on the wrist, although noise is an issue due to motion [
34].
Respiratory rate and breath count per minute can also be used in healthcare applications to identify various respiratory-related issues, such as lung cancer, panic attacks, tuberculosis, asthma, etc. The importance of respiratory rate in the field of health care has led to the development of many sensors, such as a nasal sensor relying on a thermistor [
35].
The authors of [
36] developed an optical base sensor that is sufficiently sensitive to measure vibrations arising from respiration. The proposed sensor was only verified in one test but was found to be feasible for all conditions in further tests. The authors of [
37] developed a pressure sensor based on two plates containing capacitive and time-of-breathing plates that expand and contract during exhalation and inhalation, respectively. The authors reported a confidence of 94–95% in respiratory rate calculations compared to a nasal sensor. The authors of [
38,
39,
40] suggested the use of a stretch sensor to measure respiratory rate based on tensile force and stretching during inhalation.
The authors of [
38] introduced a sensor based on a ferroelectric polymer that generates a charge when tensile pressure is applied, acting as a transducer. The authors of [
41] suggested a body temperature sensor based on a positive coefficient of temperature, the most common of which a negative coefficient or thermistor sensor.
The authors of [
42] suggested that temperature can be accurately detected with using sensor technology embedded in textiles. However, wearable sensors designed for continuous and non-spreading blood pressure (BP) detection remain a challenge in the domain of health care. A number of attempts have been made to achieve accurate estimation of BP using pulse train duration [
43,
44,
45,
46], which is the duration of pulse between the heart and other areas, such as radial arteries or earlobes. The authors of [
47] attempted to measure this property between the wrist and ear, whereas the authors of [
48] attempted to calculate pulse train duration between the fingertips and palm. A sensor measuring the level of oxygen was implemented using pulse oximetry sensors. The authors of [
49] suggested a low-energy oximeter for pulse detection with the aim of enhancing wearability. Two approaches can be used to minimize power absorption, the first of which is lower SNR (signal-to-noise-ratio) monitoring, which regularly calculates the SNR and regulates the stretch of duration, causing an LED to be in the “ON” condition. With higher SNR values, the LED reflects accurate readings. The second approach is phase-locked loop tracking, which calculates the probability of troughs and peaks of a photoplethysmographic signal, providing crucial real-time information.
The authors of [
50] designed a pulse oximeter based on ear reflection, which was designed to identify the level of blood oxygen even when a patient is suffering from hypothermia, shock, fear etc., which may cause blood systematization, increasing the chances of an unidentified pulse rate in the fingertips. Reasonable precision was achieved in surgical patients; however, the authors concluded that these sensors can be used in combination with wearable pulse oximeters on the fingers but not as an alternative.
The authors of [
51] suggested an oximeter that can be worn on the wrist that is concave in structure, avoiding external light, with improved noise resistance. The proposed device can also measure skin temperature in combination with three crucial sensors in a single wearable node. Many other wearable sensors have been proposed with a focus on tracking the specific condition of patients. ECGs (echocardiograms) are among the most common methods to measure heart-related issues, and various wearable devices have been developed to gather these signals. The authors of [
52] suggested a helmet with an electroencephalogram (EEG)-enabled sensor feature to measure the activity of the brain to identify conditions such as sleep disorders, seizures and progress after head injury.
The authors of [
53,
54] suggested an EEG system for a specific purpose such as driver drowsiness identification or stress management in the form of a wearable headband EEG system to measure activity. The authors of [
52] suggested an ECG sensor based on an armband to provide measurements with reasonable precision.
The authors of [
55] discussed a triaxial accelerometer embedded inside a cellphone (smartphone) using a machine learning approach to detect the posture of the user using a suitable algorithm, achieving an accuracy of 90%. The classification approach for the detection of posture was found to be less accurate for fall detection [
56], with an accuracy of 89% in an outdoor environment and 93% in an indoor environment. In [
57], a previously proposed camera-based wearable system was combined with an accelerometer, achieving 91% precision in fall identification.
In [
58,
59] a gyroscope, accelerometer and magnetometer were used to accurately identify falls also; the authors also added a barometer for accurate identification of falls based on changes in height. In further work, accuracies of 98 to 100% were achieved in fall detection, and the authors suggested the immediate implementation of the proposed system in healthcare settings. For elderly patients, the gait identification method is quite useful to obtain knowledge of specific conditions such as Parkinson’s disease. Gait identification was considered in patients with Parkinson’s disease and those healing from a stroke in [
60] using a wearable foot sensor designed to measure various parameters, such as walking speed, step length, etc.
In [
61], a sensor developed to control prosthetics of the lower limb was used for gait detection in lower-limb amputees. The authors of [
62] suggested placing three accelerometers on the ankle, knee and hip of Parkinson’s patients. A feature extraction approach was used in combination with an anomaly detection approach for gait freeze and anomaly identification in [
63] using waist-worn embedded devices with microcontrollers and accelerometers (triaxial) to track gait. The authors of [
64] suggested an optical sensor to measure blood glucose in the fingers, and in [
65] a microwave ultra-band approach was used to measure the level of blood glucose through an earlobe sensor. The abovementioned is not only a special-purpose sensor for disease applications but should be considered in the design of any healthcare system to tracking any ailment.
4. Internet of Things and Health Care
IoT is still a somewhat new area of research and its utilization in health care remains a nascent field. Various developments have been in the use of IoT systems for health care. A standardized and common model for the upcoming head-to-foot IoT healthcare structure is suggested to guide the evolution of such a system. Various explanations of IoT exist; at the most basic level, it can be described as a network of individual devices inter-related via machine-to-machine transmission, allowing for the collection and interchanging of information [
66,
67,
68]. Such mechanics can be applied in a wide range of industries, enabling the collection of large amounts of data. As a pilot technology of the Fourth Industrial Revolution [
69] IoT mechanics have already been fruitful in many areas, such as smart stationing [
70], agricultural fidelity [
71] and management of water usage [
72]. Remote health monitoring can be implemented for patients at home instead of in a hospital, minimizing pressure on hospital assets such as beds and doctors. Such an application could be beneficial for patients in rural areas, improving access to health care, as for the elderly living independently.
IoT technology can also improve access to healthcare resources by reducing pressure on systems and providing better facilities and constant monitoring. Remote health monitoring is subject to some limitations, the most important of which are related to security due to the massive quantity of sensitive data recorded in individual databases on a regular basis. Therefore, sensors must be recalibrated regularly to ensure that they are functioning correctly and to minimize service disruptions in the event that a patient is found to be outside of the coverage area of a base station or his/her battery is drained. Fortunately, such issues can easily solved, and steps can be taken to minimize such disadvantages. Remote health monitoring systems based on IoT are expected to become viable for healthcare settings in the near future. Many researchers have recognized the strengths of IoT as a suitable solution for remote health monitoring systems.
Many healthcare systems based on IoT are designed for a specific task, including diabetes supervision, rehabilitation, supportive surrounding living for the elderly, etc. Such systems have been structured for various applications and are strongly associated with enabling technologies. Several researchers are interested in the topic of rehabilitation and physical injury. For example, IoT technology can be used to develop a rehabilitation structure for individuals associated with their symptoms based on correlations with symptoms, disorders and therapies in a database.
Such a system requires the manual support of the doctor, who inputs symptoms and implements the suggested treatment in 90% of cases. If the doctor is completely satisfied with the system’s performance, no changes are required in the suggested plan of treatment [
66]. The proposed model for a healthcare system using IoT is shown in
Figure 3 and can be considered as a future system to guide decision making, serving as a strong foundation for various cases.
Various scheduling mechanisms have also been analyzed in recent years, showing that the scheduling mechanism performs well compared to the first-arrived and lower jump approaches in term of packet loss.
Figure 4 shows previous attempts at scheduling mechanisms, with efficient performance of that priority-based approach. Therefore, it is very necessary to analyze the packet status, which is discussed in the Results section.
6. IoT Security
Security is one of the major concerns for IoT because attackers or hackers can silently access data from sensors [
92,
93]. Therefore, it is crucial to know the various recent security approaches for IoT. One such approach known as IDP (IoT-dependent data placement) is used for privacy preservation. In this suggested approach, the main objective is to optimize the access of data concerning time and resource utilization and minimize power absorption according to the limitations of data safety [
94].
Power minimization and privacy preservation can be achieved using NSGA-ii (non-dominated sorting genetic algo.) algorithms. This approach was locally evaluated considering real-time health-related data from patient health profiles, and the approval task was performed by a cloud service [
95]. One method based on an encryption approach known as radio-frequency recognition is also used to provide security for medical data [
96]. The flow of data in a network environment is quite complex for health information, and data security relies on biometric security with resource limitations in wearable health tracking systems.
Therefore, IoT is integrated in medical devices in which information is processed to optimize security for various medical applications. The service provided by the cloud consists of three servers, i.e., a database server, key-assigning center and authentication server. For healthcare data, secure cryptography is used based on a lattice framework. This system consists of four stages, i.e., data encryption, data decryption, key generation and a setup stage; a lattice polynomial is used as input for the first two stages, and key assignment is implemented in form of the public and private keys. The second stage is shared by the data server, and in the final stage, the data are treated as an input parameter and integrated with a random polynomial. If a user wants to forward a request to deal with medical data, the key-assigning center sends the hidden key pair to the data server through a secure channel, and the data server executes the blank text, taking the input and hidden key pair.
Security has been deployed in low-energy Bluetooth in a variety of ways, the most secure of which is LE secure relation, which follows a numeric comparison approach, and the ECHO (elliptical curve Hellman—Diffie) algorithm, which involves the use of a private and public key for every device to achieve secure key sharing. Two keys in the form of a master and slave are used, i.e., a recognition-resolving key and a signature-resolving key [
97]. Potential attackers can be prevented from using 128-bit advanced encryption standard. The attacker’s identity, eavesdropping and man-in-the-middle can be effectively tackled using the security feature of low-energy Bluetooth. For Zigbee, various security features are available, some of which are optional and are used by network developers.
The security modes of Zigbee are based on 128 advanced encryption standards with security keys, i.e., master, link and network keys. The optional master key is used to secure the sharing and creation of the link, whereas the network key is essential and is shared by all devices using a network layer security process that manages all transmissions inside the network. The link layer is used to secure transmission in the application layer [
98]. In a recent study, it was found that by sniffing the link key, a network can be easily exploited during device joining. From a healthcare point of view, if Zigbee is to be deployed, it must follow all optional security processes and trustable key management protocols.
In Sigfox, security is deployed using a private key attached to each message to minimize the chances of intercepting and spoofing attacks. Advanced attackers target the service provider and hardware node and may disclose the unique key, affecting healthcare systems. Message limitations, i.e., limited to about 140 messages per day, minimize the effects of bluff attacks; in the case of healthcare, even a small amount of fraudulent information affects the performance of the healthcare system and can degrade the patient’s condition. The performance of Sigfox is suitable for non-critical purposes for which acknowledgements and message delivery receipts are unimportant. However, for healthcare applications, successful message transmission at an appropriate speed is essential. Security compromises may be harmful to patients and affect medical records. Therefore, it is recommended that Sigfox be avoided for critical healthcare applications.
The LoRaWAN standard provides security by assigning a unique key to every node in the network that is known only to the network provider and the node. This measure can prevent man-in-middle attacks, with percept encoded and not interpretable [
99]. A node with a unique key would fail, and the key would be detected using sophisticated castrate hardware or using a network attack in the server. If a key were identified, then attackers could easily use future messages from the node or transmit malicious messages to the base station. For appropriate security, key management must be taken care of by service providers and developers to protect sensitive data in healthcare databases from malicious attacks. Therefore, LoRaWAN can not sufficiently provide total security.
Narrowband IoT follows 3 GPP S3 security levels both in the application and transport layers [
100]. It consists of several mechanisms for device identification, entity verification, user identity, data integrity, etc. It also supports optional mechanisms to ensure data confidentiality, application authentication, etc. An optional encryption approach is recommended for real threats that arise in radio communication to protect delicate health-related data. The use of narrowband IoT with all optional and mandatory mechanisms is a secure approach for healthcare applications. It is secure and highly efficient and supports long-range communication between multiple devices.
In the application layer scheme, a signal-scrambling key is delivered to authorized parties. The algorithm should be distributed in small data packets to reduce the risk of attacks, as it contains statistically crucial characteristics that reduce the probability of successful transmission and make it easy for an attacker to access the data. The steganography-based technique is used to protect electronic health data using encryption techniques and conceal the data inside the ECG signal, preventing discovery of hidden messages within the health records [
101]. The homography encryption method is also used to secure data, allowing for encryption using a public key before it is sent to the cloud. It also follows mathematical functions over the data being encrypted, which enables machine learning without decrypting information in the cloud. If the request is sent by an authorized user, the system using an authority key can use a secure channel with a secret key to encrypt the data. This technique is a promising methods to integrate machine learning and security, but it has not been implemented in IoT-based health care [
102].
WBAN applications involve the use of public spaces such as exercise areas and parks. In all cases, users have IOT devices such as smartwatches that collect biosignals from sensors implanted in the human body. The biosignal received from the body is sent to the static sink in the deployed area. Sink nodes contain 5G modules to send the information to the healthcare center for the processing [
103]. Sensor nodes face many operational problems because of their low computing capacity, and mobility represents an open challenge in wireless body area networks, which are affected by the loss of messages due to topological calculation. Herein, we discuss a advanced version of MAC named MT-MAC, which was designed to ensure successful delivery of messages. A node handover approach between virtual clusters was considered for network integration, and the concept of connected dominated sets was used to achieve efficient energy transfer [
104]. Voice data can also be used to track a person suffering from a viral infection; such a technique is suitable in the case of COVID-19, in which social distancing is important. Such a dataset can be used in comparison with an original voice database to support healthcare management [
105]. The use of a deep reinforcement teaching method based on intelligent routing for IoT in WSNs can significantly reduce latency and increase the life time of the network. This suggested algorithm separates the complete network into unequal clusters relying on the present data load within the current sensor node, preventing the immature death of any network during communication [
106]. A hierarchical approach for sensor networks in which transmission is more secure with longer node lengths and the operation of the network is improved with shared important management policy relies on the LEACH protocol [
107].
7. Analysis and Results
To analyze the performance of narrowband IoT for deployment in standalone and in-band systems, we considered a bandwidth of 150 KHz for a general LTE cell. The framework consists of a tri-sector plot with a distance of 1600 m. Narrowband IoT for in-band communication is placed in the LTE system using a separate resource block. Narrowband IoT is placed in every cell using a similar resource block; the considered synchronized network and other considered parameters are shown in
Table 3 for simulation.
The 150 KHz bandwidth consists of 10 subcarriers with 15 KHz spacing among uplink and downlink; compared to a single tone in uplink, this configuration achieves worse performance, i.e., 15 KHz or 3.74 KHz, and the achieved result in our case provides a lower jump compared to a single-tone IoT-based system. Therefore, we found it more practical to analyze narrowband IoT behavior.
The rate of the data (
) for
resource block and node
is:
where
is the signal to noise plus interference and is expressed as:
where
denotes the transmitted power,
is the gain of channel between the base station and node
of the resource block,
is the interference noticed by
and considered to be minimum and
is power spectrum of noise volume. Depending on the SIN, the comparable coupling loss is calculated as:
For analysis purposes, a combined chase approach is used, which relies on coupling loss so that similar information is renewed ‘N’ number of times. The renewed values for different coupling loss values are shown in
Table 4.
The incoming duration is scattered in three stages of successive transmissions with equal incoming durations of 10 min, 40 min and 2 h, corresponding to 44.6%, 30.4% and 30.3% of devices, respectively. The mean appearance record per device is evaluated as:
The network rate (NR) for packets relative to the quantity of devices is evaluated as:
The general sensor types, along with their size, data coverage and duration of transmission, are shown in
Table 5.
The performance of the system level for narrowband IoT is analyzed using the Matlab tool for supporting devices, effective throughputs and delay. Considering the parameters mentioned in
Table 3 and
Table 4, a simulation was conducted for both standalone and in-band communication for 600 random samples. For different implementation scenarios such as separate node patterns and multiple-node patterns, the cumulative distribution function evaluates the successful means in terms of throughput, which is the quantity of information transfer bits per second relative to the total overhead of authorized information, as shown in
Figure 5.
Figure 5 shows that the obtained successful throughput is significantly smaller than the maximal data cost of 232.4 kbps and 250 kbps at the time of downlink and uplink, respectively, for the proposed narrowband IoT in the literature. This situation arises because of the shorter information of packet length compared to the overhead of the regulated channels, especially the physical control downlink, which requires a large number of repetitions for proper transmission. In the case of uplink, 40% of the allocated resources are protected for physical narrowband channel access (random), and some are protected for narrowband physical (shared channel) patterns with two packets, which degrades the result, whereas the use of multiple nodes improves the packet size and increases the gain in term of throughput.
In the case of in-band implementation, performance is degraded significantly compared to a standalone configuration because of LTE regulation.
Figure 6 shows the average quantity of patients that can be included both for multiple and separate nodes in different deployment cases. It was also observed that multiple nodes increase the number of supported patients. This result shows that an increase in packet size minimizes the quantity of information that can be sent per patient, resulting in a lower overhead rate so that more patients can be monitored.
Figure 5 and
Figure 6 show that with an increase in the size of packets, the constructive throughput also increases. However, such an increase may cause a longer delay, as shown in
Figure 7.
Figure 7 represents the delay for narrowband IoT under different conditions, where delay is the average duration required to finish the transfer with the desired overhead regulated information. It was also observed that with increased packet size, the time required for transmission also increases, resulting in increased delay for every device in the network. However, an increment in packet size improves the overall throughput, enabling more patients per cell to be served. Therefore, it is very clear that the quantity of supported devices, packet size, delay and throughput rely on one another.
The communication protocols considered in the analysis are the message queuing telemetry transport protocol (MQTPP) [
108] and the limited application protocol (LAP), for which the mean transfer duration of the central server was calculated as [
109]:
where
STn and
ST1 denote the time stamp of end packets and initial packets, respectively. The above communication protocol was considered to identify the efficiency of minimizing the time delay (average). Analysis of the central server data packets of different sizes is depicted in
Table 6.
The total packet loss was evaluated using the following equation [
108]:
The packet loss with respect the packet size is shown in
Figure 8 for MQTTP, which shows that with increasing packet size, the packet loss also increases. Therefore, it is necessary to continue tracking the size of packet while sending the information in the network to maintain the quality with minimal loss.
Keeping the above parameters fixed, the network was simulated by changing the sensor quantity per room and the packet length. The packet delivery ratio is shown in
Figure 9, which is expressed as:
where
yi denotes the total packets, ‘
i’ is the node and
N is the total nodes in a network. The throughput of the network for MQTTP and LAP is depicted in
Figure 10.
8. Conclusions
The proposed model can be implemented in the future based on IoT for healthcare systems. This system may be suitable for observations in specific situations, as well as for implementation in general system. Various unnoticeable, wearable sensors were discussed and analyzed, with a primary focus on healthcare applications. Long- and short-range communication standards were also compared to reflect the suitability of this approach for healthcare applications. We observed that IoT (narrow band) and Bluetooth low energy (BLE) are the most acceptable modes for long- and short-range communication with respect to healthcare applications. Cloud computing was found to be best means of handling and organizing large amounts of data. It was also observed that encryption and acquire command policies greatly enhanced safety; however, for real-time applications, no such standard is available for wearable healthcare systems relying on IoT.
With the modernization of wearable technologies in various areas, as well as cloud and communication technologies, we identified various significant domains for upcoming future research. Encryption methods based on lightweight storage in the cloud and machine learning present opportunities for researchers to explore build new approaches in the domain of healthcare system relying on IoT. The vital objective of our paper was to identify the activities and issues associated with the field of IoT for healthcare system applications. Existing evidence shows that IoT greatly supports patient monitoring and data to delivery to control centers. ECG tracking systems can be implemented using machine learning approaches to easily identify any disease. Researchers have applied various approaches to reduce consumption by implementing algorithms to improve efficiency. An IoT system with existing features can reliably, effectively and adaptively track patient condition, as well as the health of elderly patients, using specific sensors, cameras, speakers. etc.
In the future, IoT systems can be improved by using a suitable power absorption model with a secure cloud platform to enhance security performance. We conducted a performance analysis of narrowband IoT using single- and multi-node approaches for healthcare applications and found that multiple-node deployment crucially performs well in term of the quantity of patients and throughput with an increase in delay. Therefore, it is important to optimize the desired delay, throughput and device density. Reliability and efficiency were analyzed with the help of two protocols; we found that for small packets, the message queuing telemetry transport protocol (MQTTP) was efficient compared to limited application protocol (LAP) in sending information from the sensor architecture to the central server. MQTTP also performs well in high-traffic scenarios with a small packet size. Therefore, MQTTP may be considered an efficient protocol for communication in smart healthcare systems. Scheduling mechanisms can help to improve the overall communication performance to deliver packets with appropriate analysis of transmission in the future.