Fog-Internet of things-assisted multi-sensor intelligent monitoring model to analyze the physical health condition

Abstract

BACKGROUND:

Internet of Things (IoT) technology provides a tremendous and structured solution to tackle service deliverance aspects of healthcare in terms of mobile health and remote patient tracking. In medicine observation applications, IoT and cloud computing serves as an assistant in the health sector and plays an incredibly significant role. Health professionals and technicians have built an excellent platform for people with various illnesses, leveraging principles of wearable technology, wireless channels, and other remote devices for low-cost healthcare monitoring.

OBJECTIVE:

This paper proposed the Fog-IoT-assisted multisensor intelligent monitoring model (FIoT-MIMM) for analyzing the patient’s physical health condition.

METHOD:

The proposed system uses a multisensor device for collecting biometric and medical observing data. The main point is to continually generate emergency alerts on mobile phones from the fog system to users. For the precautionary steps and suggestions for patients’ health, a fog layer’s temporal information is used.

RESULTS:

Experimental findings show that the proposed FIoT-MIMM model has less response time and high accuracy in determining a patient’s condition than other existing methods. Furthermore, decision making based on real-time healthcare information further improves the utility of the suggested model.

Keywords

IoT cloud computing fog computing multi-sensor system. physical health condition health monitoring system

1. Introduction

Hardware, software, physical objects, computing devices, and humans are integrated with the growing Internet of Things (IoT) ecosystem to enable communication, interaction, collection, and information exchange over a network [1]. The rapid development of IoT and miniature wearable bio-sensors provided a great opportunity for healthcare services [2]. On the wearable or cyber-physical systems, smart healthcare services collect various types of data through sensors in the sensing layer [3, 4]. IoT fog services distribute the latency sensitivity in smart healthcare systems at the edge of the network [5]. IoT fog in smart healthcare provides less latency, quick response, and readability to healthcare applications [6, 7]. System failure and latency are considered the main challenges in healthcare systems, and it can be solved by optimal job allocation and resource management by managing fog performance and functionalities [8, 9]. Data processing, push notification, channel management, and categorization are advanced services provided by fog-based healthcare systems at a hospital and at homes to avoid serious consequences by notifying emergencies in real-time to doctors to take immediate actions [10, 11].

Health monitoring is important to diagnose diseases and to provide appropriate treatment [12]. Abnormalities of patients, elderly peoples in real-time, are indicated to caretakers by detecting the abnormalities through a monitoring system and send the information to caretakers [13]. Fog-based health monitoring is a growing concept in recent years [14]. Fog increases network efficiency and reduces bandwidth requirement by adding real-time information to mobile users closer to the network edge [15]. Fog-based health monitoring system is suggested due to its high efficiency in distributing time-sensitive information to users [16]. Safeguarding security and privacy, organizational barriers, financial cost, training programs for healthcare providers, and technical problems should be considered while implementing a health monitoring system [17, 18]. In rural areas, low power wide area network (LPWAN) is used to implement the fog-based healthcare monitoring system. Long- short-term memory layers with a recurrent neural network (LTSM RNN) is implemented to enable real-time notification and alters when the demand for data to be transmitted to servers for analyzing purpose [19].

The multisensory data fusion approach determines the risk level of the patient. Technology plays a vital role in the aging population for monitoring the quality of life [20]. Automatic Tele-monitoring in the home concentrates on Geron technology, which provides an interesting solution by offering healthcare in a known atmosphere for older people and patients [21, 22]. Various distress situations are detected to increase the reliability of the system by multimodal fusion. Environment multimodal pour tele-vigilance medicale (EMUTEM) is used for home healthcare monitoring [23]. Fuzzy logic-based multisensory data fusion provides high flexibility for the EMUTEM framework [24]. Multiple sensors are used to obtain the breadth of information required, and multi-sensors fuse output to obtain the depth of information required when a single sensing method is insufficient [25]. Computed patient risk level gives meaningful information to perform multi-sensor data fusion. The input data and output data are mapped by fuzzy set theory used in Fuzzy interference system (FIS) to determine the risk level by having health condition information about the patient during emergencies to know how critical the patient’s health condition.

2. Related work

Mani et al. [26] proposed an IoT-guided healthcare monitoring system by fog computing services to manage real-time notifications. The objective of the proposed work is to reduce the cost of delivering the data in the cloud. IoT-based fog computing saves billion of patients’ lives. In spatial, temporal dimensions, the medical services are accelerated by the transmitted and communicated signals from the remote sensors.

An energy-efficient fog-assisted IoT system is suggested by Gia et al. [27] for monitoring diabetic patients with cardiovascular disease. A fog-based system monitors ECG, glucose, body temperature, contextual data life humidity, room temperature, air quality in real-time. The accurate system operation and energy-efficient wearable sensor nodes are obtained as a result.

Tiwari et al. [28] used fog-assisted healthcare architecture to reduce latency for pre-operative support. The proposed framework is to provide treatment for the patients in a remote location at the right time. Tele-surgery offers various opportunities for skilled doctors to provide medical services. Resource scheduling problems are reduced by the artificial intelligence, tactile internet and 5G technologies required by Tele-surgery.

A highly efficient fog and deep learning-based AAL fall detection system is recommended in [29]. The wearable tri-axial accelerometer collects the monitored data of patients. Deployment and management of deep learning models are supported by smart IoT gateway architecture. To optimize and evaluate the resource performance and interference time LSTM/GRU deep learning model is used. The proposed model is more accurate in detecting the fall, and it is efficient with less delay and service improvement.

A hybrid deep learning model is considered by Gumaei et al. [30] using multimodal body sensing data for human activity recognition. The proposed simple recurrent units and gated recurrent units are combined to process the sequence of input data and store and learn the past information for solving the instability and fluctuation problems. The proposed work performance is compared with the state of art methods.

Khan et al. [31] designed an IoT-based multisensor patient fall detection system. Visual-based classifier use of the camera and k-Nearest neighbor and Naive Bayes’ classifier is used to analyze the sensor data. An attendant is informed immediately when the fall is detected. The accuracy of the proposed method is evaluated, and various specifications are discussed.

Micro or Nano service composition is used in the continuous processing of health data in edge fog cloud computing [32]. The proposed work is created to handle big health data and execute services for fulfilling the non-functional requirements (NFRs). Analyzed ECG data will give a warning to the physician about the patient records. The content delivered to internal and external staff is studied. The feasibility of the study is evaluated by building interconnection between multiple service solutions.

IoT fog-based healthcare framework is modeled by Sood and Mahajan [33, 34] to identify and control hypertension attack. An artificial neural network is used to identify the risk level through hypertension patient health level. The information stored in the cloud is shared with the clinicians, doctors, etc. Precautionary measures are provided by temporary information created from the fog layer. Less response time, high bandwidth, and better accuracy are obtained.

An e-health system for monitoring elderly health based on IoT and fog computing is considered by Hassen et al. [35]. My signals HW V2 platform and the android app plays an important role in developing the proposed system. The general health parameters and physiological data are collected from older people, and the android app is used to monitor their health and communicate to healthcare providers continuously. The users who considered the proposed work as useful and easy to use and learn are evaluated to improve older adults’ healthcare services.

Zhang et al. [36] proposed emergency level-based healthcare information offloading over fog networks. The proposed work provides efficient healthcare services. Better service quality is provided by stored data of patients and prescription advice of doctors in the cloud. A metaheuristic is used to optimize the offloading problem and to find the best policy. Emergency supporting the measure is also designed to improve the service.

Internet of health things (IoHT) is suggested by Mukherjee et al. [37] using an integrated edge fog cloud network for personalized healthcare. The nearby health center is advised to the users by detecting the user’s mobility information and pattern. Theoretical analysis shows that energy consumption and delay are reduced greatly. An experiment is carried out with the help of health data of volunteer students in the laboratory.

A new fog IoT architecture for patients and elderly health monitoring is modeled by Debauche et al. [38]. Alone and the proposed system monitors older adults’ behavioral change and health state to provide healthcare. Fog IoT architecture is used to store the data, and Lambda cloud architecture is used to process the stored data. Graphical monitoring provides the originality of the proposed work. Data are accumulated to analyze the observed data automatically to achieve medical staff to follow the patients.

Tuli et al. [39] proposed an ensemble deep learning-based smart healthcare system by health fog to automatically diagnose heart disease in an integrated IoT and fog computing environment. Heart patient data are managed efficiently. The fog tests the proposed work-enabled cloud framework in terms of network bandwidth, jitter, accuracy, execution time, power consumption, and latency. Health-Fog configures different operation modes to provide better Quality of Service.

Mahmoud et al. [40] recommended a novel method including the cloud of things to provide energy efficiency in cloud-based healthcare. By using the iFogSim simulator, the proposed work’s performance is evaluated by comparing it with cloud-only policies and default allocation. The solution observed is more energy efficient by saving more energy than the cloud only and Fog-default.

3. Fog-IoT-assisted multi-sensor intelligent monitoring model

Fog-IoT is used to tackle the service deliverances to the remote patients by detecting the health condition. This work focuses on improving the accuracy of patient health and provides alerts to abnormal patients remotely. The data is sensed from the multi-sensor and forwards to the fog devices; this fog device finds the nearby nodes. The nearby nodes are identified by introducing a KNN method that deploys Euclidean distances. In this work, the FIoT-MIMM method is proposed to analyze the patient’s health condition by collecting the biometric and medical observing data. Figure 1 presents the proposed fog model for health monitoring.

Figure 1.

Fog model for health monitoring.

The patient’s health is monitored, and sensors collect the information. The collected information is given to the healthcare center through wireless infrastructure with fog nodes for detection, mapping, and analyzing process to take place to provide services according to the information collected.

3.1 Multi-sensor data differentiate

The scope of this paper is to generate an emergency state that occurs by detecting the abnormal condition of the patients. Here, the suggestion model is developed to store the medical data on the cloud and maps with the sensed information. By performing this, the service deliverance rate is improved for the varying multisensory data. The preliminary step is to analyze the multisensor data on the varying time interval, and it is equated in the below equation as follows.

$\displaystyle\theta=\frac{1}{m_{n}}\ast\mathop{\sum}\limits_{e_{0}}^{m_{0}}% \left[{\left({u_{i}+p_{a}}\right)\ast\left({r_{l}+a_{l}}\right)}\right]+\left(% w^{\prime}+\frac{u_{i}}{f_{d}}\right)*p_{a}(c_{k})$ (1a)

The analysis is done on the above Eq. (1a), which is associated with the varying data sensed from the multisensor. The number of data is represented as $m_{n}$ and the data $m_{0}$ , multisensor is defined as $u_{i}$ . The analysis is referred to as $\theta$ , in this patient is denoted as $p_{a}$ , the determination of various data is used to find the normal and abnormal condition of the patient, referred to as $r_{l}$ and $a_{l}$ . The sensed information about the patient’s condition has been forwarded with the feedback, which might be the alert or emergency message.

In this evaluation step, the forwarding of the alert signal is done on time, and it is equated as $(w^{\prime}+\frac{u_{i}}{f_{d}})*p_{a}(c_{k})$ , the time is defined as $c_{k}$ . The forwarding is referred to as $w^{\prime}$ , and the fog node is denoted as $f_{d}$ , in the IoT platform, the data is sent to the nearby fog nodes, and it is termed as $b_{0}$ . The data is sensed from the multisensor devices on varying time intervals; normal and abnormal data is detected and forwards the response. In this work, the service deliverance rate is improved by providing an alert before the emergency state occurs; analysis is termed as $\theta$ . In this differentiation of normal and emergency state of the patient is computed in the following sessions.

The data is differentiated to find the data’s normal and emergency state and provides the necessary step to avoid the critical situation level. Both normal and abnormal data is differentiating from the history of data analysis method that deploys efficient service delivery on time. The data is sensed and forwarded to the healthcare system and maps with the preceding state and provide the result promptly to estimate this. Here, differentiation is computed for every iteration step to find the state of the data. The following equation is used to evaluate the differentiation of data from the multisensor.

$\displaystyle\sigma=\left\{\begin{array}[]{l}\left(e_{0}(m_{n})/o^{\prime}% \right)+\prod_{\complement}\left[\left({m_{0}+p_{a}}\right)\ast\left({w}^{% \prime}-c_{k}\right)\right]-z^{\prime}=r_{l}\\ \mathop{\Pi}\nolimits_{m_{n}}w^{\prime}(e_{0})*\left(\frac{m_{0}}{o^{\prime}}% \right)+C*\left(\frac{u_{i}}{e_{0}/f_{d}}\right)=a_{l}\end{array}\right.$ (1b)

Differentiate of data is examined in the above equation to analyze the normal and abnormal data that is associated with the suggestion model, and it is termed as $z^{\prime}$ . The first derivation is defined as the normal state of the patients; while acquiring the data from the multisensor, the mapping is performed for the sensed data with the previous medical data model, and it is denoted as $\prod_{\complement}[(m_{0}+p_{a})\ast(w^{\prime}-c_{k})]-z^{\prime}$ . The detection is referred to as $C$ the continuous data is sensed from the sensor, and it is represented as $o^{\prime}$ ; thus, the normal condition of the patient is determined. In this evaluation step, differentiation is denoted as $\sigma$ , which deploys various sensed data.

The second derivation relates to abnormal data. Here, the mapping with the previous state defines it as abnormal, and the alert message is forwarded. By evaluating this process, the fog nodes are responsible for finding the nearby nodes and sending the data; this evaluation step decreases the healthcare system’s cost of monitoring. The periodic monitoring is done from the multisensor devices and forwards the data to the fog node, and it is denoted as $C\ast(\frac{u_{i}}{e_{0}/f_{d}})$ . The detected abnormal state is sent as an alert to the patient to consult with the doctors; this processing is done remotely on IoT. Thus, the differentiation of data is done, and from that, mapping is performed. This requires a preceding and pursuing state, and it is formulated in the following equation.

$\displaystyle\tau=m_{n}\left({e_{0}}\right)+u_{i}+w^{\prime}(f_{g})*\left(% \frac{w^{\prime}(f_{g})}{\Pi_{\complement}b_{0}}\right)*z^{\prime}(r_{l}+a_{l}% )+y_{i}(c_{k})$ (2)

The mapping is performed by acquiring the data from the multisensor that deploys the patients’ varying state and forwards the response continuously. The preceding state of sensed information is mapped with the pursuing state and provides the result promptly, and it is equated as ${z}^{\prime}(r_{l}+a_{l})+y_{i}(c_{k})$ . The mapping is represented as $\tau$ ; in this approach, continuous processing is carried out and finds the patient’s condition. The data is acquired and forwarded to the fog node; in this evaluation step, the emergency information is sent first, in this priority is used. In this processing, the abnormal state is detected with the preceding state $y_{i}$ and alerts the patient on time.

For every stage of information acquiring, the mapping is associated with the fog node that deploys the preceding state. The suggestion model is used to provide feedback reliably. In this approach, the fog nodes are responsible for efficiently moving the data that deploys with the previous state. The mapping is done in a reliable manner associated with the varying data sensed from the multisensor devices on time. The detection of data from the multisensor is done for low-cost healthcare data monitoring, and it is estimated in the below session.

3.2 Detection

The data is detected from the multisensor that deploys the state of the data, associated with normal and abnormal conditions. Continuous detection is performed that deploys the fog nodes in the IoT, for this FIoT-MIMM method is proposed to analyze the healthcare condition. In this evaluation step, the alert $t_{0}$ the message is forwarded to the mobile phone to the remote patient to verify the body’s condition and takes the necessary step accordingly. The following equation is used to detect the data in low-cost healthcare monitoring promptly.

$\displaystyle C=\left(\frac{w^{\prime}+b_{0}}{o^{\prime}/m_{n}}\right)*\Pi_{% \tau}\left(m_{0}+\frac{\beta}{r_{l}*a_{l}}\right)+c_{k}+m_{n}(e_{0})$ (3a)

In the above equation, the detection is performed for the varying data sensed on the patient healthcare system’s different aspects. Here, it is associated to find normal and abnormal data, and it is represented as $({m_{0}+\frac{\beta}{r_{l}\ast a_{l}}})$ . The mapping is performed for every step of computation of data acquiring, and it is carried out on time. The forwarding of data to the patient is done by deploying the fog node here the nearby nodes are detected for further processing, and it is denoted as $(\frac{w^{\prime}+b_{0}}{o^{\prime}/m_{n}})$ .

The detection is determined in a reliable manner that estimates the data’s condition state in the IoT environment and provides feedback. The identification is denoted as $\beta$ ; it senses the data and detects the normal and abnormal state. If the detection is normal, the alert is not forwarded. If abnormal data is detected, the alert is sent to the patient to improve the condition state. It depends on the identification being carried out on time. Thus, low-cost healthcare data is monitored continuously, and feedback is transmitted, and the following equation is used to evaluate the determination of biometric and medical observing data.

$\displaystyle\vartheta=\left\{\overbrace{w^{\prime}(a_{l})+e_{0}*\left(\frac{f% _{d}+p_{a}}{m_{n}}\right)+y_{i}}^{n^{\prime}}\left|\overbrace{\mathop{\sum}% \nolimits_{z^{\prime}}(m_{0}+f_{d})*\left(\frac{l_{0}}{\beta}+y_{i}\right)}^{s% _{0}}\right.\right\}$ (3b)

The determination of various biometric and medical observing data is collected from the sensor based on the data’s nature. If the data is biometric, it includes the fingerprint, iris, and another patient’s body detection state. In contrast, medical observing data relates to the ECG signal and other data from the patient. The first derivation is associated with the biometric of the patient collected at the time of sensing, and the biometric is denoted as $n^{\prime}$ . The medical observing is termed as $s_{0}$ , that deploys the fog node for the varying data and identify with the history of data denoted as $({\frac{l_{0}}{\beta}+y_{i}})$ .

The determination is represented as $\vartheta$ ; here, the biometric and medial observing is used to define the data and forwards it to the fog nodes on time. In this processing, the patient condition is detected and determines the collected forms of data by equating the above Eq. (3b). In Fig. 2, the data detection process is presented.

Figure 2.

Data detection process.

Device data are given as input to the classifiers for classifying the data into normal and abnormal (emergency) data. Both normal and emergency data are given to data detection along with mapped data. Detected data is then divided into medical data and biometric data to store it for future use. The data sensed from the multisensor differentiate the state and provides the feedback appropriately. Thus, the data is determined and finds reliable processing on time, and from this fog, nodes are responsible for transmitting the data to the nearby nodes. The nearby nodes are detected by proposing the KNN method. The data differentiation and mapping for different monitoring intervals are presented in Fig. 3. In Fig. 4, the data points for different users and monitoring levels are illustrated.

Figure 3.

Data (differentiation and mapping).

Figure 4.

Data points.

The monitoring intervals vary for data differentiation ranges from low to high and high to constant. Here, the mapping is done from low to high value, and it deploys the detection of normal and abnormal data. If the monitoring interval increases, data differentiation decreases, and mapping show better improvement (Refer to Fig. 3). Monitoring interval varies for data points that range from low to high value, which is done to the user’s number. The users are defined from 8 to 16, and the data points are used to find the nearby nodes. This data point is determined by finding the Euclidian distance and provides reliable monitoring on time. If the data points increase, the number of users shows a higher range for 16 compares to 8 (Refer to Fig. 4).

4. KNN process

It is a classification method done for the previous state and new data sensed from the sensor device; for varying times, the sensor senses the data that deploys the patients’ feedback. In this processing, the patient’s condition is detected, and an alert is forwarded on time by determining the biometric and medical observing data. For evaluating this KNN process, the data points are necessary to map with reliable data and send it to mobile phones. The following equation is used to detect the nearby nodes for this Euclidean distance is measured.

$\displaystyle i\left({u,v}\right)=\sqrt{\sum_{m_{n}}^{q}(a^{\prime}-b^{\prime}% )^{2}}+(f_{g}+b_{0})$ (4)

The Euclidian distance is measured by determining the space related to the collection of data in IoT, and it is termed as $q$ . The initial node position in the plane is defined as $a^{\prime}$ and $b^{\prime}$ , here it is associated with the Euclidian vector. In this processing, the fog nodes identify the nearby nodes and forward the data to estimate the alert message on time. The two points in the plane are used to find the distance between the fog nodes, and it is represented as $u, v$ . By performing this, the emergency state is addressed and avoided for the varying data from the multisensor. The Euclidian is estimated by defining the k-nearest data denoted as similar data from the mapping. In KNN, the classification of a normal and emergency is detected that is associated by identifying the patient’s condition.

Thus, the distance is denoted as $i$ and deploys the fog nodes and the nearby nodes in the IoT and provides the service based on the patient’s condition. For this evaluation, the history of information is necessary for the different types of data; it might be of illness, on the normal, medium, and emergency state. Thus, in KNN, the Euclidian distance is used to detect the nodes from the initial node and perform the forwarding. In this manner, the Euclidian distance is estimated in the above equation and determines the FIoT-MIMM method. From this, the forwarding of biometric and medical observing data is computed in the below equation.

$\displaystyle m_{0}\left(w^{\prime}\right)=\prod^{i}_{f_{g}}{\complement\left(% m_{n}\right)+e_{0}+\left(\frac{r_{l}+a_{l}}{\Sigma{h_{0}+e_{0}}}\right)}*\left% (\frac{{u,v}/{a^{\prime}-b^{\prime}}}{\sigma+u_{i}}\right)+\left(y_{i}-c_{k}% \right)+b_{0}$ (5)

The forwarding is done to the nearby nodes; for this evaluation, the Euclidian distance is measured to the sensor data. The normal and abnormal data is detected in the Eq. (1b); the determination of data is carried out on time. This forwarding approach decreases the KNN is used to find the nearby nodes and decreases the processing time. If the processing time is decreased then, the forwarding is carried out reliably. In Fig. 5, the KNN representation for different data is illustrated.

Figure 5.

KNN representations for data (biometric and medical).

The biometric data is continuous, and medical data is discontinuous for time. The variation in time is given on the x-axis, and the data detection is given on the y-axis. The time increases for detected data represents a single search space for biometric data, so it is continuous. The double search space is represented for discontinuous medical data due to increased time for detected data. The searching differs for every set of acquiring data from the multisensor, and it is denoted as $\left(\frac{r_{l}+a_{l}}{\int{h_{0}+e_{0}}}\right)$ . The fog nodes are transferring the data to the nearby nodes that deploy the varying data by performing the identification phase. This is carried out by continuous searching, and it is termed as ${\ h}_{0}$ to the node in the IoT platform. Here, the time is used to deploy the abnormal data and alert the message to the patient, and it is termed as $\left(y_{i}-c_{k}\right)+b_{0}$ . In this processing, the searching is evaluated in a reliable manner associated with the mapping to find the state of the patients. The following equation is used to find the data point that uses the suggestion model to provide feedback to the patients.

$\displaystyle\vartheta\left(d_{a}\right)=\left.\begin{array}[]{c}\left(o^{% \prime}+e_{0}\right)+m_{0}*\Sigma{\beta\left(a_{l}\right)+y_{i}}\\ =\left(\frac{e_{0}*a_{l}}{\sigma+m_{n}}\right)+\prod_{c_{k}}{u,v*\left(i*f_{g}% \right)+z^{\prime}}\end{array}\right\}$ (6)

In the above equation, the data points are used to define the patients’ state for this KNN uses the similarity of data, and this is done by mapping approach. The mapping is carried out for the varying data associated with the patient’s abnormal condition; in this evaluation step, the message is sent to the remote patient. In this processing, the identification is done with the preceding data. The time is estimated to provide the service to the user. The fog node does this, and it utilized cloud computing; the medical healthcare data is stored in the cloud environment. The points in the planes are used to define the fog nodes that relate to the nearby nodes to the easy and fast transmission that avoids the emergency state, and it is computed as $\ u,v*\left(i*f_{g}\right)$ .

The suggestion model is designed to forward the feedback regarding the data identified in the preceding state. Differentiate is done on KNN to examine the data points on the plane. It is defined as similar data that is already detected at the stage of mapping. Thus, the data point is detected, and it is termed as $d_{a}$ , from this method, the fog layers are used if there is precaution or suggestion is necessary for the patient in an emergency. The following equation is used to estimate the temporal information provided to the patient by deploying the FIoT-MIMM method.

$\displaystyle w^{\prime}\left(d_{a},l_{0}\right)=\left(p_{a}*\delta\right)-a_{% l}+\left(\frac{t_{f}*\rho}{\Sigma_{z^{\prime}}{e_{0}+m_{0}}}\right)*c_{k}\left% (v_{0}\right)+\Sigma_{\tau}{\left(q*\frac{o^{\prime}}{\delta}\right)}+\tau$ (7)

The forwarding of data is done for the abnormal patients to decreases the risk, and it deploys the detection of data by mapping with the history of data processing; the condition is termed as $l_{0}$ . In this stage, the fog nodes are responsible for transmitting the data to the nearby nodes, and the feedback is forward on time; the feedback is termed as $\delta$ . The temporal information and suggestion model is used to deliver the data if any precaution is necessary for the patients. In this evaluation step, the temporal information is used in the fog layers, and it is represented as $\rho$ , the temporal information is defined as $t_{f}$ . Thus, the temporal information is forwarded if there are any precautions and suggestions for the patient. The continuous detection is done and alerts the patient to consult directly with the doctors; service deliverance is denoted as $v_{0}$ . The following equation is used to assign the new data points here; this refers to the similar data mapped with the preceding state.

$\displaystyle g_{i}=\left(m_{0}*a_{l}\right)+\prod_{\beta}{o^{\prime}\left(c_{% k}\right)*\left(\frac{e_{0}+p_{a}}{{n^{\prime}}/{s_{0}}}\right)}+w^{\prime}*% \rho(z^{\prime}+t_{f})$ (8a)

The new data points are assigned $g_{i}$ in the KNN. The Euclidian distance is estimated for the varying data from the multiple sensors. In this processing, the patient’s condition is detected, and it defines the biometrics and medical observing and determines the fog layers for temporal information. Thus, the assigning is used to improve the detection process; in this, for different time intervals, the multiple data is sensed, and similar data is acquired. In Fig. 6, the data point assignment for different data is illustrated.

Figure 6.

Data point assignments.

The x-axis increases for data detection value in the y-axis develops continuous biometric data and discontinuous medical data. The data point’s response space and search space are developed separately and new points in continuous biometric data and discontinuous medical data. Thus, the forwarding is determined by utilizing the suggestion model and temporal information associated with the continuous detection of data from the sensor. Thus, the assigning of data is evaluated to determine the plane’s data points and analyze the user’s abnormal condition, and the suggestion is forwarded at the emergency state. In this fog, layers deal with the temporal information associated with the detection process; the following part is used to identify the service deliverances. In Table 1, the data assigning and forwarding for different detection factor is tabulated.

Table 1

Data assigning and forwarding for different detection factor

Detection factor	Data
	Assigning	Forwarding
0.1	0.488	0.320
0.2	0.505	0.366
0.3	0.542	0.379
0.4	0.545	0.419
0.5	0.560	0.433
0.6	0.576	0.546
0.7	0.872	0.752
0.8	1	0.95
0.9	1	0.95
1	1	0.95

The detection factor for varying data deploys by assigning to the number of data and provides efficient sharing. The forwarding is done from the low to high and resides to constant and determines better processing. Here, assigning and forwarding for the proposed work shows low to a high and constant value. In Table 2, the data points for different detection factor and fog nodes are tabulated.

Table 2

Data points for detection factor and fog nodes

Detection	# Data points
factor	Fog Nodes $=$ 10	Fog Nodes $=$ 20	Fog Nodes $=$ 30	Fog Nodes $=$ 40
0.1	4	11	16	30
0.2	5	14	18	31
0.3	5	14	21	31
0.4	6	17	21	34
0.5	7	18	22	34
0.6	8	19	26	36
0.7	12	24	32	47
0.8	12	24	32	47
0.9	12	24	32	47
1	12	24	32	47

The detection factor is done that deploys the data points, and it is associated with the fog nodes. If the detection is improved then, the number of data points for the proposed work shows better processing. Here, the different fog nodes ranges from 10 to 40 and shows a higher detection factor for 16 compare to 10.

4.1 Identify service deliverances

The service deliverances are identified by determining the time that deploys the mapping method of data with data history. This identification is carried out on the initial stage, and forwarding is done on the fog nodes. Here, temporal information is handled by the fog layers that are used in an emergency. Evaluating this service delivery is determined and provides the patient’s result; in this, feedback and suggestion are forwarded on time. This work aims to improve the service deliverances, for this identification is determined and examined in the below equation.

$\displaystyle\beta=\Sigma_{h_{0}}{\left(m_{0}+a_{l}\right)*\left({i(f_{g}+b_{0% })}/{q}\right)*d_{a}\left(\vartheta\right)+\left(\tau*c_{k}\right)}$ (8b)

The service deliverance estimated in the above Eq. (8b) is related to the forwarding of the data in the fog nodes; thus, distance is detected between them. The points are used to define the similar data from the history mapping represented as $d_{a}\left(\vartheta\right)+\left(\tau*c_{k}\right)$ . The Euclidian distance is determined on the space and identifies the abnormal data, and it is denoted as $\left({i(f_{g}+b_{0})}/{q}\right)$ . In this processing, data from physical healthcare condition and maps with the healthcare system and fog computing does it.

The mapping is performed on the IoT environment associated with the healthcare system that stores the collection of biometrics and medical observing data. The mapping is evaluated; post to this process, the forwarding is carried out; it finds the nearby nodes and passes the data. The nearby nodes provide the patient feedback on time; this evaluation step addresses the service failure and improves the better service deliverances. The analysis is done for the precaution and suggestion the patient asks for, and it uses Equation (9); this is used to improve the suggestion model.

$\displaystyle\theta\left(z^{\prime}\right)=m_{n}*\left(\frac{g_{i}}{\prod{t_{f% }+w^{\prime}}}\right)+\Sigma_{v_{0}}{m_{0}+t_{0}*\left(p_{a}-c_{k}\right)+% \left(\frac{h_{0}(l_{0})}{d_{a}}\right)}$ (9)

The analysis of the suggested model is examined in the above equation, which is measured to forward the reliable data to the patient. Here, the suggestion model is improved with the data points on KNN and determines the nearby node. The KNN is used to find the nearby nodes on the space, transmit the medical data, and tackle the service deliverances. Thus, the searching is done for similar data from the sensor and suggests the patient termed as $\left(\frac{h_{0}(l_{0})}{d_{a}}\right)$ . The emergency alert is forwarded continuously; for this, the below equation is derived.

$\displaystyle\complement\left(o^{\prime}\right)=\left(\frac{m_{0}*d_{a}}{\prod% {\rho+g_{i}}}\right)+\left(\frac{w^{\prime}+p_{a}}{{a_{l}}/{\beta}}\right)*% \sum^{v_{0}}_{h_{0}}{\left(z^{\prime}+\frac{y_{i}}{b_{0}}\right)-g_{n}}$ (10a)

The emergency state is detected if there is abnormal data detected at the multisensor devices and is associated with the mapping process. The continuous detection of abnormal data is estimated and provides the suggestion by performing mapping, and it is represented as $\left(z^{\prime}+\frac{y_{i}}{b_{0}}\right)$ . The KNN is used to define the nearby nodes and forwards the data if there is any emergency occurs, and it is termed as $g_{n}$ . The service deliverance is improved, and the accuracy level is evaluated in the below equation.

$\displaystyle\varphi=\frac{l_{0}\left(\complement\right)+\theta(g_{n})}{m_{n}}$ (10b)

In the above Eq. (10b), the accuracy level is improved, which is associated with the true positive and true negative, and it is defined as patient condition and emergency detection. The number of data sensed in the multisensor devices deploy the biometric and medical observing. The new data is assigned by evaluating the KNN method associated with the FIoT-MIMM approach and decreasing the service deliverances. Thus, the suggestion model is enhanced by deploying the temporal information acquired from the patient in the emergency. If an emergency occurs, the alert message is forwarded to the mobile phone and improves physical healthcare. In Figs 7 and 8, the service delivery and accuracy for different assigning factors are illustrated.

Figure 7.

Service delivery % (assigning factor).

Figure 8.

Accuracy (assigning factor).

The assigning factor varies for service delivery ratio that deploys fog nodes from 10 to 40. If the service delivery ratio increases, the assigning factor for the proposed work increases. The fog node for 10 shows a better assigning factor than 40, and the service delivery ratio relies low on high (Refer to Fig. 7). The assigning factor for the accuracy level shows a higher range for the number of users from 8 to 16 (Refer to Fig. 8). If the assigning factor increases, the accuracy level also increases for the varying users. The accuracy ranges from low to high, and user 8 shows a higher assigning factor than 16 values. If the number of users increases, the accuracy of the proposed work increases.

5. Results and discussion

This section presents the discussion on the performance of the proposed FIoT-MIMM using the OPNET simulations. In this simulation, 16 user devices are deployed 50 fog nodes serve that. The network is modeled as illustrated in Fig. 1, in which a centralized server is considered a data analyzer. This system makes use of the existing recommendation for responding to 15 monitored intervals. The storage of the server is 1TB with a processing speed of 2.4 GHz. The performance is verified using the selected metrics accuracy, service delivery ratio, response time, and data utilization. For verifying the consistency of the proposed method, a comparative analysis with SAOA [24], HealhtFog [27], and MAIoHTF [25] is performed.

5.1 Accuracy

Figure 9.

Accuracy (monitoring intervals).

Figure 10.

Accuracy (fog nodes).

The accuracy of the proposed work increases for varying monitoring intervals and fog nodes. Here, the initial step is to acquire the data from the multisensor and analysis the state is either normal or abnormal. Based on this evaluation step, the accuracy rate is improved, and this data is associated with the sensor data. If there is abnormal activity is detected, the alert signal is forwarded to the end-user, and it is denoted as $\left(w^{\prime}+\frac{u_{i}}{f_{d}}\right)*p_{a}(c_{k})$ . From the fog nodes, the data is forwarded to the medical center in the IoT environment. The response is provided as the signal. This alert signal is provided on time, and the data relates to the matching process with the preceding data. This matching is carried out in the IoT platform that deploys by differentiating the patient’s health’s normal and abnormal activity. Here, the detection of normal and abnormal is related to the number of data acquired from the sensor devices. In this evaluation step, the accuracy rate is enhanced and determines the patient’s condition’s reliable identification. If the patient is normal, the alert is not provided; else, the signal is forwarded. In this processing, the monitoring intervals are used to detect the condition’s state and determine the result on time (Refer to Figs 9 and 10).

5.2 Service delivery ratio

Figure 11.

Service delivery ratio (fog nodes).

Figure 12.

Service delivery ratio (users).

In Figs 11 and 12, the service delivery ratio increases for varying fog nodes, and the number of users accesses the data. If the patient is in an emergency state, the data is acquired from devices and matches the medical center in IoT. From this, if the data is normal, the forwarding is carried out as the alert signal. In this manner, the service delivery ratio is improved, and it is formulated as $\complement*\left(\frac{u_{i}}{{e_{0}}/{f_{d}}}\right)$ , it is done from the classification process. Differentiate of normal and abnormal data is evaluated in the better processing rate and determines the result on time. Thus, the delivery is given to the emergency and abnormal state of the patient. By processing this, the data is monitored regularly and detects the normal and abnormal state. In this evaluation step, the service is given on time, and it is associated with continuous data processing. The mapping with the previous state and pursuing data is done with the healthcare system. The delivery ratio for the proposed work is associated with the mapping process. Here, the fog nodes are used to forward the data to the IoT platform and determine the result on time. Thus, the fog nodes are responsible for carrying the request and response from the patient’s health and establishing a better result as the signal. The service is provided to the end-user by identifying the normal and abnormal data represented as $\left(\frac{w^{\prime}(f_{g})}{\int_{\complement}{b_{0}}}\right)$ .

5.3 Response time

Figure 13.

Response time (fog nodes).

Figure 14.

Response time (users).

The response time for varying fog nodes and the user decreases in the proposed method and deploys the detection process. The data is forwarded to the fog nodes and determines the mapping with the suggestion model computed as $\left(m_{0}+\frac{\beta}{r_{l}*a_{l}}\right)$ . Differentiate of data is done and finds the normal and abnormal data and here the prediction by mapping. The prediction is related to the previous state of processing and determines the low-cost for healthcare monitoring. In this evaluation, the service delivery is forwarded on time and shows a better accuracy level. If the accuracy is improved, then the proposed work’s response time decreases for varying data from the sensor devices. Form the multisensor, the data is acquired and performs the mapping, and the suggestion model is used to determine the data and provides the result. In this processing step, the patient’s request is related to the emergency state, and from that, the response is given. The matching process responds, and the suggestion is given if there is an emergency case. If the emergency state occurs, the alert signal is forwarded to the patient on time, and it is represented as $\left(\frac{f_{d}+p_{a}}{m_{n}}\right)+y_{i}$ . The varying data is computed and derives from continuous processing (Refer to Figs 13 and 14).

5.4 Data utilization

Figure 15.

Data utilization (monitoring intervals).

The data utilization for varying monitoring intervals and fog nodes increases and shows better performances than the other three methods. The utilization of the data is determined from the previous state and deploys the efficient processing denoted as $\left(\frac{{u,v}/{a^{\prime}-b^{\prime}}}{\sigma+u_{i}}\right)$ . Here, the Euclidean distance is used to find the data near the fog node and forward it. In this processing, data utilization is done by observing the biometric and medical data. Here, the utilization is done for the varying data from the multisensor and deploys the continuous searching of nearby nodes and provides the result. The data points are used to determine the similarity match with the previous state and detect the data. In this evaluation step, the emergency state is detected and examines the data points for efficient sharing. For this data utilization, the communication is evaluated on time, and it is termed as $\left(\frac{t_{f}*\rho}{\int_{z^{\prime}}{e_{0}+m_{0}}}\right)*c_{k}\left(v_{0% }\right)$ . This work uses the suggestion model that deploys the feedback method. The feedback is derived from the better processing and deploys the efficient similar data mapping. This utilization is done by evaluating the better processing of matching with the previous state. The suggestion model is used along with the matching process and sent the feedback promptly (Refer to Figs 15 and 16). In Tables 3–5, the above results are summarized with the findings.

Table 3

Results summary for monitoring intervals

Metrics	SAOA	HealthFog	MAIoHTF	FIoT-MIMM	Findings
Accuracy	0.928	0.934	0.946	0.952	9.6% High
Data utilization	0.940	0.950	0.960	0.969	11.4% High

Table 4

Results summary for fog nodes

Metrics	SAOA	HealthFog	MAIoHTF	FIoT-MIMM	Findings
Accuracy	0.927	0.935	0.943	0.948	7.8% High
Service delivery ratio	90.33	92.62	93.18	94.084	6.12% High
Response time (s)	3.476	2.365	1.83	0.72	11.97% Less
Data utilization	0.939	0.95	0.96	0.973	14% High

Table 5

Results summary for users

Metrics	SAOA	HealthFog	MAIoHTF	FIoT-MIMM	Findings
Service delivery ratio	91.39	92.49	93.1	94.149	5.47% High
Response time (s)	4.533	3.684	2.632	1.503	9.74% Less

Figure 16.

Data utilization (fog nodes).

6. Conclusion

This paper proposed a Fog-IoT-assisted multisensory intelligent monitoring system to improve the service delivery ratio of mobile healthcare and remote monitoring systems. The proposed method relies on observed wearable sensor data for service responses without prolonged delays. The data is segregated using k-nearest neighbor learning for its continuous and discontinuous sequence. The discontinuous observed data points are mitigated by reassigning temporal information based on the learning. Suggestions and recommendations for the patients are assisted based on the information analyzed by the learning. The segregated sensor data learning reduces service response delays, improving detection and data utilization accuracy. In the future, variant sensor data is planned to be analyzed using dataset correlation for augmenting the multi-application (smart medicine) support. Besides, online learning practices are planned to be integrated for improving the efficiency of classifier training.

Footnotes

Conflict of interest

None to report.

References

Mutlag

Abd Ghani

Arunkumar

Mohammed

Mohd

. Enabling technologies for fog computing in healthcare IoT systems. Future Generation Computer Systems.2019; 90: 62-78.

Hsu

Manogaran

Panchatcharam

Vivekanandan

. A New Approach for Prediction of Lung Carcinoma Using Back Propagation Neural Network with Decision Tree Classifiers. In 2018 IEEE 8th International Symposium on Cloud and Service Computing (SC2) 2018 Nov 18 (pp. 111-115). IEEE.

Namasudra

Devi

Kadry

Sundarasekar

Shanthini

. Towards DNA based data security in the cloud computing environment. Computer Communications.2020 Feb 1; 151: 539-47.

Santos

Endo

da Silva Lisboa

MFF

da Silva

LGF

Sadok

Kelner

Lynn

. Analyzing the availability and performance of an e-health system integrated with edge, fog and cloud infrastructures. Journal of Cloud Computing.2018; 7(1): 16.

Amin

Kumar

Biswas

Iqbal

Chang

. A light weight authentication protocol for IoT-enabled devices in distributed Cloud Computing environment. Future Generation Computer Systems.2018 Jan 1; 78: 1005-19.

de Moura Costa

da Costa

da Rosa Righi

Antunes

. Fog computing in health: A systematic literature review. Health and Technology.2020; 10: 1025-1044.

Kaur

Garg

Aujla

Kumar

Rodrigues

Guizani

. Edge computing in the industrial internet of things environment: Software-defined-networks-based edge-cloud interplay. IEEE communications magazine.2018 Feb 13; 56(2): 44-51.

Farahani

Firouzi

Chang

Badaroglu

Constant

Mankodiya

. Towards fog-driven IoT eHealth: Promises and challenges of IoT in medicine and healthcare. Future Generation Computer Systems.2018; 78: 659-676.

Elhoseny

Abdelaziz

Salama

Riad

Muhammad

Sangaiah

. A hybrid model of internet of things and cloud computing to manage big data in health services applications. Future Generation Computer Systems.2018 Sep 1; 86: 1383-94.

10.

Moghadas

Rezazadeh

Farahbakhsh

. An IoT patient monitoring based on fog computing and data mining: Cardiac arrhythmia usecase. Internet of Things.2020; 11: 100251.

11.

Darwish

Hassanien

Elhoseny

Sangaiah

Muhammad

. The impact of the hybrid platform of internet of things and cloud computing on healthcare systems: opportunities, challenges, and open problems. Journal of Ambient Intelligence and Humanized Computing.2019 Oct 1; 10(10): 4151-66.

12.

Habib

Makhoul

Darazi

Couturier

. Health risk assessment and decision-making for patient monitoring and decision-support using wireless body sensor networks. Information Fusion.2019; 47: 10-22.

13.

Kumar

Lokesh

Varatharajan

Babu

Parthasarathy

. Cloud and IoT based disease prediction and diagnosis system for healthcare using Fuzzy neural classifier. Future Generation Computer Systems.2018 Sep 1; 86: 527-34.

14.

Yacchirema

Sarabia-Jácome

Palau

Esteve

. A smart system for sleep monitoring by integrating IoT with big data analytics. IEEE Access.2018; 6: 35988-36001.

15.

Varatharajan

Manogaran

Priyan

. A big data classification approach using LDA with an enhanced SVM method for ECG signals in cloud computing. Multimedia Tools and Applications.2018 Apr 1; 77(8): 10195-215.

16.

Forooghifar

Aminifar

Atienza

. Resource-aware distributed epilepsy monitoring using self-awareness from edge to cloud. IEEE Transactions on Biomedical Circuits and Systems.2019; 13(6): 1338-1350.

17.

Molano

Lovelle

Montenegro

Granados

Crespo

. Metamodel for integration of internet of things, social networks, the cloud and industry 4.0. Journal of Ambient Intelligence and Humanized Computing.2018 Jun 1; 9(3): 709-23.

18.

Albahri

Zaidan

Hashim

Albahri

Alsalem

. Real-time remote health-monitoring Systems in a Medical Centre: A review of the provision of healthcare services-based body sensor information, open challenges and methodological aspects. Journal of Medical Systems.2018; 42(9): 164.

19.

Sharma

Luhach

Sinha

. An optimal load balancing technique for cloud computing environment using bat algorithm. Indian J Sci Technol.2016 Jul; 9(28): 1-4.

20.

Sharma

Luhach

Jyoti

. A novel approach of load balancing in cloud computing using computational intelligence. International Journal of Engineering and Technology.2016; 8(1): 124-8.

21.

Jaleel

Mahmood

Hassan

Bano

Khurshid

. Towards medical data interoperability through collaboration of healthcare devices. IEEE Access.2020; 8: 132302-132319.

22.

Sangeetha

Amalanathan

. Outlier detection in neutrosophic sets by using rough entropy based weighted density method. CAAI Transactions on Intelligence Technology.2020; 5(2): 121-127. doi: 10.1049/trit.2019.0093.

23.

Uddin

Hassan

Alsanad

Savaglio

. A body sensor data fusion and deep recurrent neural network-based behavior recognition approach for robust healthcare. Information Fusion.2020; 55: 105-115.

24.

Veerappan

Albert

. Multiple-criteria decision analysis process by using prospect decision theory in interval-valued neutrosophic environment. CAAI Transactions on Intelligence Technology.2020; 5(3): 209-221. doi: 10.1049/trit.2020.0040.

25.

Hernández-Peñaloza

Belmonte-Hernández

Quintana

Álvarez

. A multi-sensor fusion scheme to increase life autonomy of elderly people with cognitive problems. IEEE Access.2017; 6: 12775-12789.

26.

Mani

Singh

Nimmagadda

. An IoT guided healthcare monitoring system for managing real-time notifications by fog computing services. Procedia Computer Science.2020; 167: 850-859.

27.

Gia

Dhaou

Ali

Rahmani

Westerlund

Liljeberg

Tenhunen

. Energy efficient fog-assisted IoT system for monitoring diabetic patients with cardiovascular disease. Future Generation Computer Systems.2019; 93: 198-211.

28.

Tiwari

Kumar

Tiwari

. FOG assisted healthcare architecture for pre-operative support to reduce latency. Procedia Computer Science.2020; 167: 1312-1324.

29.

Sarabia-Jácome

Usach

Palau

Esteve

. Highly-Efficient Fog-Based Deep Learning Aal Fall Detection System. Internet of Things, 2020; p. 100185.

30.

Gumaei

Hassan

Alelaiwi

Alsalman

. A hybrid deep learning model for human activity recognition using multimodal body sensing data. IEEE Access.2019; 7: 99152-99160.

31.

Khan

Qamar

Zaheen

Al-Ali

Al Nabulsi

Al-Nashash

. Internet of things based multisensor patient fall detection system. Healthcare Technology Letters.2019; 6(5): 132-137.

32.

Sánchez-Gallegos

Galaviz-Mosqueda

Gonzalez-Compean

Villarreal-Reyes

Perez-Ramos

Carrizales-Espinoza

Carretero

. On the continuous processing of health data in edge-fog-cloud computing by using micro/nanoservice composition. IEEE Access.2020; 8: 120255-120281.

33.

Liu

Nguyen

Pham

. An Efficient Method for Sweep Coverage with Minimum Mobile Sensor, 2014 Tenth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, Kitakyushu, Japan, 2014, pp. 289-292, doi: 10.1109/IIH-MSP.2014.78..

34.

Sood

Mahajan

. IoT-fog-based healthcare framework to identify and control hypertension attack. IEEE Internet of Things Journal.2018; 6(2): 1920-1927.

35.

Hassen

Dghais

Hamdi

. An E-health system for monitoring elderly health based on Internet of Things and Fog computing. Health Information Science and Systems.2019; 7(1): 24.

36.

Zhang

Cho

Chen

. Emergency-level-based healthcare information offloading over fog network. Peer-to-Peer Networking and Applications.2020; 13(1): 16-26.

37.

Mukherjee

Ghosh

Behere

Ghosh

Buyya

. Internet of Health Things (IoHT) for Personalized Health Care using Integrated Edge-Fog-Cloud Network.

38.

Debauche

Mahmoudi

Manneback

Assila

. Fog IoT for Health: A new Architecture for Patients and Elderly Monitoring. Procedia Computer Science.2019; 160: 289-297.

39.

Tuli

Basumatary

Gill

Kahani

Arya

Wander

Buyya

. Healthfog: An ensemble deep learning based smart healthcare system for automatic diagnosis of heart diseases in integrated iot and fog computing environments. Future Generation Computer Systems.2020; 104: 187-200.

40.

Mahmoud

Rodrigues

Saleem

Al-Muhtadi

Kumar

Korotaev

. Towards energy-aware fog-enabled cloud of things for healthcare. Computers Electrical Engineering.2018; 67: 58-69.