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Integrated Healthcare Monitoring Solutions for Soldier using the Internet of Things with Distributed Computing
Journal Pre-proof Integrated Healthcare Monitoring Solutions for Soldier using the Internet of Things with Distributed Computing Shuvabrata Bandopadhaya, Rajiv Dey, Ashok Suhag
PII:
S2210-5379(19)30408-1
DOI:
https://doi.org/10.1016/j.suscom.2020.100378
Reference:
SUSCOM 100378
To appear in:
Sustainable Computing: Informatics and Systems
Received Date:
19 December 2019
Accepted Date:
10 February 2020
Please cite this article as: Bandopadhaya S, Dey R, Suhag A, Integrated Healthcare Monitoring Solutions for Soldier using the Internet of Things with Distributed Computing, Sustainable Computing: Informatics and Systems (2020), doi: https://doi.org/10.1016/j.suscom.2020.100378
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Integrated Healthcare Monitoring Solutions for Soldier using the Internet of Things with Distributed Computing Shuvabrata Bandopadhayaa, Rajiv Deyb, Ashok Suhagc School of Engineering and Technology, BML Munjal University, Gurugram, India [email protected], [email protected], [email protected]
Highlights:
In this paper, a three-layer IoT architecture has been proposed as the healthcare monitoring solution for soldiers deployed in the adverse environmental condition.
The proposed three-layer service-oriented IoT architecture comprises of a wireless sensor body area network (WSBAN) as the end layer, the fog as the intermediate layer, and the cloud layer.
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The computational functionalities are distributed among all the layers that introduces layer-wise filtration of redundant information that belongs to the safe soldiers result in a reduction in data flooding and computational burden on the cloud. Hence, the system response time improves that suits the emergency applications.
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The proposed solution invokes human-like thinking and decision making ability in the system by implementing a fuzzybased data filtering at the WSBAN layer and time-series pattern analysis based data filtering at the fog layer.
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The first level of data filtering removes the data of absolutely safe soldiers in WSBAN layer and the second level of filtering removes momentary instability (MI) cases in the fog layer. On-demand, the fog also redirects the real-time data
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to the cloud.
Abstract—: This paper has proposed an integrated healthcare monitoring solution for the soldiers deployed in adverse environmental
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conditions, using the internet of things (IoT) with distributed computing. For these soldiers, the health parameters of every individual need to be monitored on a real-time basis and subsequent analysis of the dataset to be made for initiating appropriate medical support with the lowest possible delay. In this paper, a three-layer service-oriented IoT architecture has been proposed where the computational
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functionalities are distributed among all the layers. The proposed distributed computing mechanism has implemented two levels of filtration of redundant information that belongs to safe soldiers. The first level of filtering is done at the end-node using the Fuzzy classification approach and the second level of filtering is done at the intermediate node using the time-series pattern analysis approach. This layer-wise filtration process results in a reduction in data flooding and computational burden on the cloud due to which system
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response time improves to suit emergency applications. A prototype has been developed to validate the effectiveness of the proposed solution.
Keywords— Healthcare monitoring system; internet of things (IoT); distributed computing; Fuzzy classification; pattern recognistion; Long Range wide area network (LoRaWAN).
1.
Introduction
The defense is the most crucial topic of concern for any country; to preserve the sovereignty of a country, many soldiers sacrifice their precious lives across the globe. The military brigade is forced to deploy its soldiers in adverse environmental conditions where the health parameters of the human body tend to degrade at an alarming rate. The early detection of it can prevent the soldier from falling in a critical situation. Therefore, monitoring of healthcare parameters of every individual soldier and its
subsequent analysis needs to be done on a real-time basis so that appropriate medical support can be initiated with the lowest possible delay. Generally, the principal building block of a healthcare monitoring system is a body area network (BAN) with implanted or wearable sensors to collect real-time data of various healthcare parameters [1-2]. The Internet of Things (IoT) extends the Internet connectivity up to sensors making the data globally available, through IoT gateways serving as a bridge between the Internet cloud and the sensor network [3-5]. Service-oriented architecture (SoA) is widely adopted in recent IoT solutions, where the architecture is created based on the use of system services [6]. A holistic IoT architecture enables the exchange of data from the sensor network to an IoT based cloud with the extension of a human-machine interface for monitoring and reconfiguration [7-8]. Smart healthcare services use IoT technology to bridge the physical gap between patients and available medical resources. The real-time healthcare parameters like body temperature, pulse rate, blood pressure, respiration rate, and blood glucose levels, etc. of patients can be monitored by doctors from a remote location [9-13]. The cloud-based data can be utilized for diagnosis,
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providing treatment and prevention of future diseases [14]. Various types of cloud-assisted real-time health monitoring systems are discussed in [15-16] and references therein. However, the huge dataset generated from real-time sampling process makes the response time much slower to handle the emergencies. To reduce the system latency, a three-layer architecture has been proposed
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in the literature by introducing an intermediated node located at the proximity of sensors termed as fog node that may logically be viewed as the edge of the network [17-21].
Providing healthcare solution to soldiers deployed in adverse environmental condition, connectivity is the major challenge. A
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GPS based smart tracking solution for military applications was first proposed by Jacobsen et al. way back in 1996 [22]. However, the system does not have data transmission capability. In [23] Barnett et al. had proposed the LoRa (long-range)
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communication module as the last-mile link between remotely located soldiers and the corresponding control station. The LoRa comes under the category of low-power wide-area network (LpWAN) that supports low-power, low-rate, long-range data transmission [24]. The encrypted medical sensor data transmission through LpWAN has been discussed in [25]. The concept of
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medical data transmission from the end node to fog node via LpWAN has been discussed in [26-27]. There are few hand-waving arguments of remote healthcare monitoring applications for soldiers deployed in the war-zone is reported in [28] and some references therein without any hardware and software details and end-to-end data transmission architecture.
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In real war scenarios, a military brigade deploys hundreds of troops in different remote locations; each troop consists of soldiers whose number may range from few hundreds to thousands. The healthcare parameters of each individual soldier need to be monitored on a real-time basis which will generate a huge amount of data to be processed at the cloud to produce useful information. However, the major challenge in conventional healthcare monitoring solutions is to deal with the huge dataset in
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terms of real-time data transmission and its subsequent processing. The centralized processing of the huge dataset causes a severe computational burden on the cloud, and inefficient utilization of communication channel, in turn, slow system response. In this paper, a three-layer IoT architecture has been proposed as the healthcare monitoring solution for soldiers deployed in the adverse environmental condition in which the computational functionalities are distributed among all the layers. This distributed computing mechanism introduces layer-wise filtration of redundant information that belongs to the safe soldiers result in a reduction in data flooding and computational burden on the cloud due to which system response time improves to suit emergency applications. The major contributions of this paper are listed as follows:
(i) A three-layer service-oriented IoT architecture has been proposed namely wireless sensor body area network (WSBAN) as the end layer, fog as the intermediate layer, and the cloud layer. In the proposed work, WSBAN is a sensor network integrated with the body of every individual soldier that communicates with fog node via LoRa transmission protocol. The LoRa supports end-to-end encrypted low-rate, long-range data transmission best suited for remote locations. The fog nodes are being connected with the cloud via the IP backbone. (ii) A novel distributed computing mechanism has been proposed that filters out the redundant data to achieve faster response time. The proposed solution invokes human-like thinking and decision making ability in the system by implementing a fuzzybased data filtering at the WSBAN layer and time-series pattern analysis based data filtering at the fog layer. The first level of data filtering removes the data of absolutely safe soldiers in WSBAN layer and the second level of filtering removes momentary instability (MI) cases in the fog layer. On-demand, the fog also redirects the real-time data to the cloud. A prototype has been developed to validate the effectiveness of the proposed solution. As per the best knowledge of the authors, a distributed computing approach with stage-wise data filtering in IoT based soldier health monitoring system has not
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been reported in the literature so far. Organization of this paper is as follows: Section-II presents the proposed system architecture and data flow mechanism followed by details of the distributed computing framework in Section-III. In Section-IV, experimental
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setup and result analysis are provided followed by conclusions in Section-V.
Architecture of The Proposed System
Given L
N
L k 1
k
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Consider a military brigade having L soldiers distributed in N troops; each troop is deployed in a distinct remote location. where Lk is the number of soldiers in an arbitrary kth troop. The brigade needs to monitor the instantaneous real-
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time health status of each soldier in a centralized location which is assumed to be equipped with a specialized medical facility. The service-oriented architecture for the proposed system is shown in Fig. 1. The functionalities of the system are divided into
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three distinct layers: (A) Wireless sensor body area network (WSBAN) layer (B) Fog layer and (C) Cloud layer. 2.1. Wireless sensor body area network (WSBAN) Layer
The WSBAN layer is the first layer of the proposed architecture that comprises of WSBAN nodes carried by every soldier
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under supervision. A WSBAN node comprises of a sensor array integrated with the body of the soldier that is governed by a microcontroller attached with a wireless transmission module. In the proposed application, Arduino- Nano is the preferred
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microcontroller board due to its tiny size, best suited for wearable applications.
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Fig. 1: Architecture for the proposed integrated soldier healthcare monitoring system
The receiver hardware of fog node has been placed at the control unit of the troop that ranges 2-10 km from the soldiers
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depending on the location. The wireless network proposed for the given application is long-range wide area network (LoRaWAN) that falls under LpWAN that supports low-powered low-rate data transmission having radio range matched with the desired requirement. The functionalities of this layer are divided into the following three sub-layers namely: (i) data acquisition (ii) data
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processing (iii) data transmission.
In data acquisition sub-layer, the sensor array samples real-time healthcare parameters such as body temperature, respiration
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rate, heartbeat, etc., in a periodic interval ( t ). Let the data vector at any arbitrary sampling instant q may be represented as th s(q) [s1 (q), s2 (q), , s p (q)]T , where s p (q) denotes the p sensor output which is being communicated to the microcontroller.
The data processing sub-layer filters out the data corresponding to absolutely safe soldiers which results in a significant reduction
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in load on the subsequent layers. This filtration is done using fuzzy-based classification that takes the data vector s(q) as its input and classifies it into the following three classes with corresponding membership functions: Well, Alarming, and Critical. In the proposed work, the data which falls under the category of Alarming or Critical along with its respective defuzzified output, are
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only transmitted to fog for further processing. This results in effective utilization of bandwidth and reduced computational burden on the fog node. The functional block diagram of the WSBAN node is shown in Fig 2. The pseudo-code for the WSBAN node is presented in Table 1.
Temperature sensor Pulse rate/ Oxygen sensor
I2C interface
Fuzzy inference engine
Defuzzification
Alarming
Well
Critical
Fuzzy rule base
Yes
Send the data to fog layer
Alarming / Critical
No
Lora Tx / Rx module
Discard the data
Fig. 2: Functional block diagram of WSBAN layer.
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Step 1: Initialization. Step 2: Create a new channel in the given band using country specific frequency and allocate the data rate as specified. Step 3: Set transmit power Step 4: Infinite loop Step 5: Read the sensor values Step 6: Pass the sensor values as an input to the Fuzzy inference engine Step 7: if (Fuzzy output = = UNSAFE i.e. (Alarming or Critical)) Step 8: Send the data and it’s corresponding defuzzified output to the fog Step 9: Sense the channel Step 10: if (channel is busy) Wait and go to step 9. else Go to step 11. Step 11: Transmit the data. Step 12: Schedule the next transmission end if else discard the sensor data end if
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Table 1. Pseudo-code of WSBAN node
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2.2. Fog Layer In the proposed model, the fog nodes are placed at the control unit of the troop which is in the proximity of the soldier deployed zone that serves to all WSBAN nodes. The fog node comprises of a Raspberry Pi 3B as IoT gateway attached with a 32 GB SD card as an auxiliary memory, and LoRa class C transreciever module to receive the data transmitted from WSBAN node. The functionalities of this layer are divided into the following three sub-layers: (a) data reception and storing (b) data processing
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(c) data posting.
In data reception and storing sub-layer, the IoT gateway receives the sensor data-vectors from all the unsafe WSBAN nodes at each sampling instant of time and stores it in the auxiliary database. In the auxiliary database, a unique data field is created for each WSBAN as shown in Table 2.
Table 2. Data field for a given WSBAN node in auxiliary memory WSBAN ID Sampling Index
Skin Temp.
Pulse Rate
j j+1 j+2
Tj Tj+1 Tj+2
Pj Pj+1 Pj+2
Respiration Rate (% SpO2) Rj Rj+1 Rj+2
Defuzzified Output Fj Fj+1 Fj+2
. j+K
.. Tj+K
.. Pj+K
.. Rj+K
.. Fj+K
The fog data processing sub-layer is responsible to identify and filter out the momentary instable (MI) cases which are very common in a war scenario. This process is being carried out using the following steps: Step 1 (Pattern Identification): In this step, the health status pattern of every unsafe soldier are identified by fitting a second-order polynomial curve with the most recent defuzzified outputs using the linear regression technique. Step 2 (Pattern Classification): Based on the coefficient values of the polynomial generated in step 1, the patterns are further classified into the following three classes: (a) Abnormal (b) Momentary Instable (MI) (c) Reinvestigate. The Abnormal class is the most concerned class that includes the patterns that overshoots the Well region and diverges away at a significant rate. Such cases are to be addressed immediately; the entire data field is forwarded to the cloud without any delay. The MI class includes the patterns which temporarily overshoot the Well region but returns within the analysis time-window. Therefore, the data for this time-window is discarded from memory. If the Fuzzy output at WSBAN layer overshoot the Well region but doesn’t diverge away then it is considered in Reinvestigate class. In this case, the pattern for the next analysis window
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has to be reinvestigated and this process continues till it falls in either MI or Abnormal class. The block diagram of the fog node is shown in Fig 3. The pseudo code for the fog node is presented in Table 3.
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Raspberry Pi
Antenna
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Pattern identification
Pattern classification
3G/4G/Satellite link
LoRa Transreciever
yes Send data to cloud
No MI yes
No
Reinvestigation mode
na
Discard data
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Critical
re
Auxiliary data base
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Fig. 3: Functional block diagram of fog node
Table 3: Pseudo code of fog node
Initialization: Number of data vectors required for pattern analysis (K), Number of WSBAN nodes under the fog IDs of all WSBAN nodes based on LoRa authentication key Step1: Create the data field in auxiliary memory for each WSBAN node under the fog Step 2: Infinite loop Step 3: Receive the data vector from WSBAN node and store it in the corresponding data field based on WSBAN ID. Step 4: If (Number of data field == K) // Start pattern analysis Step 4a: Fetch K numbers of most recent crisp Fuzzy output (Fi) values. Step 4b: To identify the pattern, fit a curve best fitted with data set. Step 4c: Pass the coefficients of fitted curve as the inputs to the Fuzzy
inference engine for pattern classification. Step 4d: If (Fuzzy output fall in MI class) Discard the data field for corresponding WSBAN node from auxiliary memory Else If (Fuzzy output falls in Abnormal class) Transmit the data to cloud Else // Initiate Reinvestigation mode Go to Step 4 End If Else Go to step 3 End If
2.3. Cloud Layer The cloud layer comprises of IoT hub, master database, stream analytics, and visualization dashboard. The IoT hub receives the time series data vector corresponding to the soldiers having an abnormal pattern, transmitted by the fog. The data is then archived in the master database present in the cloud and its analysis can be done on the visualization dashboard. The military brigade headquarters and super-specialty hospitals are linked with the cloud to ensure medical assistance with the lowest possible
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delay. 3. Distributed Computing In this section, the components of the distributed computing process have been discussed in detail.
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3.1. WSBAN layer processing: In this layer, Mamdani fuzzy inference engine is used and three healthcare parameters are considered as input for the analysis namely temperature, pulse rate, and percentage oxygen (%SpO2) level. The crisp data
range of input parameters are further subdivided as follows:
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acquired from the sensors are first converted into fuzzy data using triangular membership functions as shown in Fig. 4. The
Temperature range is divided into four classes; namely hypothermia, normal, fever, and hyperthermia.
(b)
Pulse rate range is divided into three classes namely; low, normal, and high.
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(a)
(c) Percentage oxygen (%SpO2) range is divided into three classes namely; Critical, low, and normal. The total 36 number of knowledge-based fuzzy rules formulated for the inference engine and part of it is shown in Table 4. The
Table 4: Fuzzy rule base in WSBAN layer Rule 1:
If (Temperature is Hypothermia), and (Pulse-rate is Low), and (Oxygen-level is Critical), then (output is Critical) If (Temperature is Hypothermia), and (Pulse-rate is Normal), and (Oxygen-level is Critical), then (output is Critical) If (Temperature is Hypothermia), and (Pulse-rate is High), and (Oxygen-level is Critical), then (output is Critical) If (Temperature is Normal), and (Pulse-rate is Low), and (Oxygen-level is Critical), then (output is Alarming) If (Temperature is Normal), and (Pulse-rate is Normal), and (Oxygen-level is Critical), then (output is Alarming) If (Temperature is Normal), and (Pulse-rate is Low), and (Oxygen-level is Low), then (output is Alarming)
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Rule 2:
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fuzzy output is classified into the following three classes: Well, Alarming, and Critical as shown in Fig. 5.
Rule 3: Rule 4: Rule 5:
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Rule 6:
. . .
Rule 34: Rule 35: Rule 36:
If (Temperature is Fever), and (Pulse-rate is Normal), and (Oxygen-level is Normal), then (output is Alarming) If (Temperature is Normal), and (Pulse-rate is High), and (Oxygen-level is Normal), then (output is Well) If (Temperature is Hyperthermia), and (Pulse-rate is High), and (Oxygen-level is Critical), then (output is Critical)
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Fig. 4 Fuzzy input membership functions of WSBAN layer: (a) Temperature in Fahrenheit (b) Pulse rate (per minute) (c) Oxygen level (% SpO2)
Fig. 5: Fuzzy output membership function for WSBAN layer
If the decision falls in the category of Well, then no transmission takes place. However, if the decision lies in the category of
analysis. 3.2. Fog layer processing: (i)
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Alarming or Critical then the defuzzified output along with the sensor data vector is transmitted to the fog node for further
Pattern Identification: Let the window size for pattern identification is K samples. The pattern identification mechanism
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picks data points (tj, Fj) where, j = 1, 2,…, K, from the data field stored in the auxiliary memory and a linear regression curve is drawn best fitted with the data points. The generalized regression function is F f (t , a) , where t and F are predictor (independent) and criterion (dependent) variables respectively, a [a0 , a1 , a2 ,, an ]T is the regression parameter vector containing (n+1) unknown parameters.
For simplicity, the polynomial linear regression technique has been used in this work [29]. The regression function of an nth order polynomial is given by
n
f (t ) a0 a1t a2t 2 ant n ai t i . i 0
(1)
The estimate of kth criterion variable is given by Fˆ f (t ) a t i and the corresponding estimation error is ek Fk Fˆk . ik k k n
i 0
The least-square criterion aims to minimize the following objective function given by (2), K
K
k 1
k 1
ek2 Fk Fˆk
2
K
n
k 1
i 0
Fk ai t ki
2
,
(2)
which is achieved by solving the set of following (n+1) partial differential equations given by (3), 0, 0, 0,, 0. a0 a1 a2 an
(3)
The solution of (3) can also be represented as
a T1.(tf ) ,
(4)
where, T is (n 1) (n 1) matrix, where its (p;q)th element is represented as K
T( p; q) t k( pq2) ,
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(5)
k 1
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and tf is (n 1) 1 vector and its pth element is represented as K
tf ( p) Fk tkp1 . k 1
(6)
f (t ) a0 a1t a2t 2 ,
(ii)
(7)
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and its polynomial coefficients are evaluated using (4).
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In the proposed work, a 2nd polynomial is considered by substituting n=2 in (1), which is represented as
Pattern Classification: In the proposed work Sugeno fuzzy model has been used to classify the patterns into three classes
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namely MI, Abnormal, and Reinvestigate. The inference engine takes the coefficients a2 and a1 as input parameters where a2 ranges from -1 to +1 and it is divided into three categories i.e. High-negative, Low-negative, and positive and a1 ranges from 0 to 9 and it is divided into three categories i.e. Low, Mid, and High. Therefore, the maximum possible number of rules is 9 as shown
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in Table 5. In both these cases, triangular membership function is used.
Table 5: Fuzzy rule base for Pattern classification Rule 1: If (a1 is Low) and (a2 is High negative) then (output is MI) Rule 2: If (a1 is Low) and (a2 is Low negative) then (output is Reinvestigate) If (a1 is Low) and (a2 is Positive) then (output is Abnormal) If (a1 is Mid) and (a2 is High negative) then (output is MI) If (a1 is Mid) and (a2 is Low negative) then (output is Reinvestigate) If (a1 is Mid) and (a2 is Positive) then (output is Abnormal) If (a1 is High) and (a2 is High negative) then (output is Reinvestigate) If (a1 is High) and (a2 is Low negative) then (output is Abnormal) If (a1 is High) and (a2 is Positive) then (output is Abnormal)
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Rule 3: Rule 4: Rule 5: Rule 6: Rule 7: Rule 8: Rule 9:
4.
Experimental Setup & Result Analysis
The experimental setup and analysis of the result have been discussed in this section. The WSBAN node has been fitted on the body of some volunteers as shown in Fig. 6. The sensor array is composed of a temperature sensor (MAX30205 is a digital sensor having inbuilt 16-bit ADC), and pulse rate/oxygen level sensor MAX3012 which is connected to the Arduino-Nano attached with a
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Dragino LoRa transreceiver. The schematic diagram of WSBAN layer is given in Fig. 7.
Fig 6. Real image of WSBAN node +5v
SCl GND
I2C BUS
SDA
Vcc MOSI
MOSI
MISO
MISO
SCl
SCK
SCl
GND
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SDA
SCK
SS
SDA
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MAX30205
4.7k
SS SPI BUS
GND MAX30102
Fig 7: WSBAN module schematic diagram
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Dragino LoRa Semtech SX1276
Arduino Lilypad
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In the prototype testing, three cases have been considered (i) a normal person having all parameters in normal ranges, (ii) a normal person asked to run for 10min (iii) a patient suffering from mild fever. The sensor data is acquired for all the three cases
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for 20 minutes with a sampling interval of 60 seconds as shown in Fig. 8 and their corresponding defuzzified outputs are shown in Fig. 9. In this work, 35 is considered as the threshold value of deffuzified output and if the data falls within this threshold then no data transmission to the fog takes place. In Fig. 9 it can be observed that case-1 can be considered as safe and the remaining two cases are overshooting the threshold. Hence, the data vector of these two cases had been transmitted to the fog via LoRa module
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for further analysis. In the fog node, the time-series defuzzified output for both the cases is fitted with a second-order polynomial as shown in Fig 10. The coefficients of the polynomials of case-2, 3 have been used as the inputs to the Sugeno fuzzy model for classification. Table 6 shows the polynomial coefficient values for the above two cases and their classification results. The data pattern of case 2 falls under MI class and does not get transmitted to the cloud. The data pattern of case 3 falls under Reinvesitigate class and need to be analysed for the next window. Table 6: Coefficients of polynomials and pattern classification results Sl. No. a1 a2 Case 2 7.8199 -0.3496 Case 3 0.7849 -0.0301
Pattern Class MI Reinvestigate
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Fig. 8: Results related to WSBAN node: Sensor readings (a) Temperature in oF, (b) pulse rate per minute, (c) %SpO2,
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Fig. 9: Corresponding defuzzified outputs of three cases
(a)
(b)
Fig. 10: Results in fog layer Time-series defuzzyfied output fitted with second order polynomials (a) case 2, (b) case 3.
5.
Conclusions In this paper, a novel approach of distributed computing based health monitoring system has been proposed for the soldiers
deployed in adverse environmental conditions using the internet of things. The step-wise filtration of the redundant information at the WSBAN node and fog node has been done which results in improved channel utilization and reduced computational burden on the cloud. As a result, the system response time has improved significantly. A prototype of the proposed work has been
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developed and from the result analysis, it has been found that the layer-wise filtering approach removes the unnecessary data which results in fast system response time. Design of suitable wearable node may be considered as the direction of future
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research.
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PII:
S2210-5379(19)30408-1
DOI:
https://doi.org/10.1016/j.suscom.2020.100378
Reference:
SUSCOM 100378
To appear in:
Sustainable Computing: Informatics and Systems
Received Date:
19 December 2019
Accepted Date:
10 February 2020
Please cite this article as: Bandopadhaya S, Dey R, Suhag A, Integrated Healthcare Monitoring Solutions for Soldier using the Internet of Things with Distributed Computing, Sustainable Computing: Informatics and Systems (2020), doi: https://doi.org/10.1016/j.suscom.2020.100378
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier.
Integrated Healthcare Monitoring Solutions for Soldier using the Internet of Things with Distributed Computing Shuvabrata Bandopadhayaa, Rajiv Deyb, Ashok Suhagc School of Engineering and Technology, BML Munjal University, Gurugram, India [email protected], [email protected], [email protected]
Highlights:
In this paper, a three-layer IoT architecture has been proposed as the healthcare monitoring solution for soldiers deployed in the adverse environmental condition.
The proposed three-layer service-oriented IoT architecture comprises of a wireless sensor body area network (WSBAN) as the end layer, the fog as the intermediate layer, and the cloud layer.
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The computational functionalities are distributed among all the layers that introduces layer-wise filtration of redundant information that belongs to the safe soldiers result in a reduction in data flooding and computational burden on the cloud. Hence, the system response time improves that suits the emergency applications.
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The proposed solution invokes human-like thinking and decision making ability in the system by implementing a fuzzybased data filtering at the WSBAN layer and time-series pattern analysis based data filtering at the fog layer.
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The first level of data filtering removes the data of absolutely safe soldiers in WSBAN layer and the second level of filtering removes momentary instability (MI) cases in the fog layer. On-demand, the fog also redirects the real-time data
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to the cloud.
Abstract—: This paper has proposed an integrated healthcare monitoring solution for the soldiers deployed in adverse environmental
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conditions, using the internet of things (IoT) with distributed computing. For these soldiers, the health parameters of every individual need to be monitored on a real-time basis and subsequent analysis of the dataset to be made for initiating appropriate medical support with the lowest possible delay. In this paper, a three-layer service-oriented IoT architecture has been proposed where the computational
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functionalities are distributed among all the layers. The proposed distributed computing mechanism has implemented two levels of filtration of redundant information that belongs to safe soldiers. The first level of filtering is done at the end-node using the Fuzzy classification approach and the second level of filtering is done at the intermediate node using the time-series pattern analysis approach. This layer-wise filtration process results in a reduction in data flooding and computational burden on the cloud due to which system
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response time improves to suit emergency applications. A prototype has been developed to validate the effectiveness of the proposed solution.
Keywords— Healthcare monitoring system; internet of things (IoT); distributed computing; Fuzzy classification; pattern recognistion; Long Range wide area network (LoRaWAN).
1.
Introduction
The defense is the most crucial topic of concern for any country; to preserve the sovereignty of a country, many soldiers sacrifice their precious lives across the globe. The military brigade is forced to deploy its soldiers in adverse environmental conditions where the health parameters of the human body tend to degrade at an alarming rate. The early detection of it can prevent the soldier from falling in a critical situation. Therefore, monitoring of healthcare parameters of every individual soldier and its
subsequent analysis needs to be done on a real-time basis so that appropriate medical support can be initiated with the lowest possible delay. Generally, the principal building block of a healthcare monitoring system is a body area network (BAN) with implanted or wearable sensors to collect real-time data of various healthcare parameters [1-2]. The Internet of Things (IoT) extends the Internet connectivity up to sensors making the data globally available, through IoT gateways serving as a bridge between the Internet cloud and the sensor network [3-5]. Service-oriented architecture (SoA) is widely adopted in recent IoT solutions, where the architecture is created based on the use of system services [6]. A holistic IoT architecture enables the exchange of data from the sensor network to an IoT based cloud with the extension of a human-machine interface for monitoring and reconfiguration [7-8]. Smart healthcare services use IoT technology to bridge the physical gap between patients and available medical resources. The real-time healthcare parameters like body temperature, pulse rate, blood pressure, respiration rate, and blood glucose levels, etc. of patients can be monitored by doctors from a remote location [9-13]. The cloud-based data can be utilized for diagnosis,
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providing treatment and prevention of future diseases [14]. Various types of cloud-assisted real-time health monitoring systems are discussed in [15-16] and references therein. However, the huge dataset generated from real-time sampling process makes the response time much slower to handle the emergencies. To reduce the system latency, a three-layer architecture has been proposed
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in the literature by introducing an intermediated node located at the proximity of sensors termed as fog node that may logically be viewed as the edge of the network [17-21].
Providing healthcare solution to soldiers deployed in adverse environmental condition, connectivity is the major challenge. A
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GPS based smart tracking solution for military applications was first proposed by Jacobsen et al. way back in 1996 [22]. However, the system does not have data transmission capability. In [23] Barnett et al. had proposed the LoRa (long-range)
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communication module as the last-mile link between remotely located soldiers and the corresponding control station. The LoRa comes under the category of low-power wide-area network (LpWAN) that supports low-power, low-rate, long-range data transmission [24]. The encrypted medical sensor data transmission through LpWAN has been discussed in [25]. The concept of
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medical data transmission from the end node to fog node via LpWAN has been discussed in [26-27]. There are few hand-waving arguments of remote healthcare monitoring applications for soldiers deployed in the war-zone is reported in [28] and some references therein without any hardware and software details and end-to-end data transmission architecture.
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In real war scenarios, a military brigade deploys hundreds of troops in different remote locations; each troop consists of soldiers whose number may range from few hundreds to thousands. The healthcare parameters of each individual soldier need to be monitored on a real-time basis which will generate a huge amount of data to be processed at the cloud to produce useful information. However, the major challenge in conventional healthcare monitoring solutions is to deal with the huge dataset in
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terms of real-time data transmission and its subsequent processing. The centralized processing of the huge dataset causes a severe computational burden on the cloud, and inefficient utilization of communication channel, in turn, slow system response. In this paper, a three-layer IoT architecture has been proposed as the healthcare monitoring solution for soldiers deployed in the adverse environmental condition in which the computational functionalities are distributed among all the layers. This distributed computing mechanism introduces layer-wise filtration of redundant information that belongs to the safe soldiers result in a reduction in data flooding and computational burden on the cloud due to which system response time improves to suit emergency applications. The major contributions of this paper are listed as follows:
(i) A three-layer service-oriented IoT architecture has been proposed namely wireless sensor body area network (WSBAN) as the end layer, fog as the intermediate layer, and the cloud layer. In the proposed work, WSBAN is a sensor network integrated with the body of every individual soldier that communicates with fog node via LoRa transmission protocol. The LoRa supports end-to-end encrypted low-rate, long-range data transmission best suited for remote locations. The fog nodes are being connected with the cloud via the IP backbone. (ii) A novel distributed computing mechanism has been proposed that filters out the redundant data to achieve faster response time. The proposed solution invokes human-like thinking and decision making ability in the system by implementing a fuzzybased data filtering at the WSBAN layer and time-series pattern analysis based data filtering at the fog layer. The first level of data filtering removes the data of absolutely safe soldiers in WSBAN layer and the second level of filtering removes momentary instability (MI) cases in the fog layer. On-demand, the fog also redirects the real-time data to the cloud. A prototype has been developed to validate the effectiveness of the proposed solution. As per the best knowledge of the authors, a distributed computing approach with stage-wise data filtering in IoT based soldier health monitoring system has not
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been reported in the literature so far. Organization of this paper is as follows: Section-II presents the proposed system architecture and data flow mechanism followed by details of the distributed computing framework in Section-III. In Section-IV, experimental
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setup and result analysis are provided followed by conclusions in Section-V.
Architecture of The Proposed System
Given L
N
L k 1
k
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Consider a military brigade having L soldiers distributed in N troops; each troop is deployed in a distinct remote location. where Lk is the number of soldiers in an arbitrary kth troop. The brigade needs to monitor the instantaneous real-
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time health status of each soldier in a centralized location which is assumed to be equipped with a specialized medical facility. The service-oriented architecture for the proposed system is shown in Fig. 1. The functionalities of the system are divided into
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three distinct layers: (A) Wireless sensor body area network (WSBAN) layer (B) Fog layer and (C) Cloud layer. 2.1. Wireless sensor body area network (WSBAN) Layer
The WSBAN layer is the first layer of the proposed architecture that comprises of WSBAN nodes carried by every soldier
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under supervision. A WSBAN node comprises of a sensor array integrated with the body of the soldier that is governed by a microcontroller attached with a wireless transmission module. In the proposed application, Arduino- Nano is the preferred
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microcontroller board due to its tiny size, best suited for wearable applications.
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Fig. 1: Architecture for the proposed integrated soldier healthcare monitoring system
The receiver hardware of fog node has been placed at the control unit of the troop that ranges 2-10 km from the soldiers
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depending on the location. The wireless network proposed for the given application is long-range wide area network (LoRaWAN) that falls under LpWAN that supports low-powered low-rate data transmission having radio range matched with the desired requirement. The functionalities of this layer are divided into the following three sub-layers namely: (i) data acquisition (ii) data
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processing (iii) data transmission.
In data acquisition sub-layer, the sensor array samples real-time healthcare parameters such as body temperature, respiration
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rate, heartbeat, etc., in a periodic interval ( t ). Let the data vector at any arbitrary sampling instant q may be represented as th s(q) [s1 (q), s2 (q), , s p (q)]T , where s p (q) denotes the p sensor output which is being communicated to the microcontroller.
The data processing sub-layer filters out the data corresponding to absolutely safe soldiers which results in a significant reduction
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in load on the subsequent layers. This filtration is done using fuzzy-based classification that takes the data vector s(q) as its input and classifies it into the following three classes with corresponding membership functions: Well, Alarming, and Critical. In the proposed work, the data which falls under the category of Alarming or Critical along with its respective defuzzified output, are
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only transmitted to fog for further processing. This results in effective utilization of bandwidth and reduced computational burden on the fog node. The functional block diagram of the WSBAN node is shown in Fig 2. The pseudo-code for the WSBAN node is presented in Table 1.
Temperature sensor Pulse rate/ Oxygen sensor
I2C interface
Fuzzy inference engine
Defuzzification
Alarming
Well
Critical
Fuzzy rule base
Yes
Send the data to fog layer
Alarming / Critical
No
Lora Tx / Rx module
Discard the data
Fig. 2: Functional block diagram of WSBAN layer.
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Step 1: Initialization. Step 2: Create a new channel in the given band using country specific frequency and allocate the data rate as specified. Step 3: Set transmit power Step 4: Infinite loop Step 5: Read the sensor values Step 6: Pass the sensor values as an input to the Fuzzy inference engine Step 7: if (Fuzzy output = = UNSAFE i.e. (Alarming or Critical)) Step 8: Send the data and it’s corresponding defuzzified output to the fog Step 9: Sense the channel Step 10: if (channel is busy) Wait and go to step 9. else Go to step 11. Step 11: Transmit the data. Step 12: Schedule the next transmission end if else discard the sensor data end if
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Table 1. Pseudo-code of WSBAN node
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2.2. Fog Layer In the proposed model, the fog nodes are placed at the control unit of the troop which is in the proximity of the soldier deployed zone that serves to all WSBAN nodes. The fog node comprises of a Raspberry Pi 3B as IoT gateway attached with a 32 GB SD card as an auxiliary memory, and LoRa class C transreciever module to receive the data transmitted from WSBAN node. The functionalities of this layer are divided into the following three sub-layers: (a) data reception and storing (b) data processing
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(c) data posting.
In data reception and storing sub-layer, the IoT gateway receives the sensor data-vectors from all the unsafe WSBAN nodes at each sampling instant of time and stores it in the auxiliary database. In the auxiliary database, a unique data field is created for each WSBAN as shown in Table 2.
Table 2. Data field for a given WSBAN node in auxiliary memory WSBAN ID Sampling Index
Skin Temp.
Pulse Rate
j j+1 j+2
Tj Tj+1 Tj+2
Pj Pj+1 Pj+2
Respiration Rate (% SpO2) Rj Rj+1 Rj+2
Defuzzified Output Fj Fj+1 Fj+2
. j+K
.. Tj+K
.. Pj+K
.. Rj+K
.. Fj+K
The fog data processing sub-layer is responsible to identify and filter out the momentary instable (MI) cases which are very common in a war scenario. This process is being carried out using the following steps: Step 1 (Pattern Identification): In this step, the health status pattern of every unsafe soldier are identified by fitting a second-order polynomial curve with the most recent defuzzified outputs using the linear regression technique. Step 2 (Pattern Classification): Based on the coefficient values of the polynomial generated in step 1, the patterns are further classified into the following three classes: (a) Abnormal (b) Momentary Instable (MI) (c) Reinvestigate. The Abnormal class is the most concerned class that includes the patterns that overshoots the Well region and diverges away at a significant rate. Such cases are to be addressed immediately; the entire data field is forwarded to the cloud without any delay. The MI class includes the patterns which temporarily overshoot the Well region but returns within the analysis time-window. Therefore, the data for this time-window is discarded from memory. If the Fuzzy output at WSBAN layer overshoot the Well region but doesn’t diverge away then it is considered in Reinvestigate class. In this case, the pattern for the next analysis window
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has to be reinvestigated and this process continues till it falls in either MI or Abnormal class. The block diagram of the fog node is shown in Fig 3. The pseudo code for the fog node is presented in Table 3.
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Raspberry Pi
Antenna
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Pattern identification
Pattern classification
3G/4G/Satellite link
LoRa Transreciever
yes Send data to cloud
No MI yes
No
Reinvestigation mode
na
Discard data
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Critical
re
Auxiliary data base
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Fig. 3: Functional block diagram of fog node
Table 3: Pseudo code of fog node
Initialization: Number of data vectors required for pattern analysis (K), Number of WSBAN nodes under the fog IDs of all WSBAN nodes based on LoRa authentication key Step1: Create the data field in auxiliary memory for each WSBAN node under the fog Step 2: Infinite loop Step 3: Receive the data vector from WSBAN node and store it in the corresponding data field based on WSBAN ID. Step 4: If (Number of data field == K) // Start pattern analysis Step 4a: Fetch K numbers of most recent crisp Fuzzy output (Fi) values. Step 4b: To identify the pattern, fit a curve best fitted with data set. Step 4c: Pass the coefficients of fitted curve as the inputs to the Fuzzy
inference engine for pattern classification. Step 4d: If (Fuzzy output fall in MI class) Discard the data field for corresponding WSBAN node from auxiliary memory Else If (Fuzzy output falls in Abnormal class) Transmit the data to cloud Else // Initiate Reinvestigation mode Go to Step 4 End If Else Go to step 3 End If
2.3. Cloud Layer The cloud layer comprises of IoT hub, master database, stream analytics, and visualization dashboard. The IoT hub receives the time series data vector corresponding to the soldiers having an abnormal pattern, transmitted by the fog. The data is then archived in the master database present in the cloud and its analysis can be done on the visualization dashboard. The military brigade headquarters and super-specialty hospitals are linked with the cloud to ensure medical assistance with the lowest possible
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delay. 3. Distributed Computing In this section, the components of the distributed computing process have been discussed in detail.
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3.1. WSBAN layer processing: In this layer, Mamdani fuzzy inference engine is used and three healthcare parameters are considered as input for the analysis namely temperature, pulse rate, and percentage oxygen (%SpO2) level. The crisp data
range of input parameters are further subdivided as follows:
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acquired from the sensors are first converted into fuzzy data using triangular membership functions as shown in Fig. 4. The
Temperature range is divided into four classes; namely hypothermia, normal, fever, and hyperthermia.
(b)
Pulse rate range is divided into three classes namely; low, normal, and high.
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(a)
(c) Percentage oxygen (%SpO2) range is divided into three classes namely; Critical, low, and normal. The total 36 number of knowledge-based fuzzy rules formulated for the inference engine and part of it is shown in Table 4. The
Table 4: Fuzzy rule base in WSBAN layer Rule 1:
If (Temperature is Hypothermia), and (Pulse-rate is Low), and (Oxygen-level is Critical), then (output is Critical) If (Temperature is Hypothermia), and (Pulse-rate is Normal), and (Oxygen-level is Critical), then (output is Critical) If (Temperature is Hypothermia), and (Pulse-rate is High), and (Oxygen-level is Critical), then (output is Critical) If (Temperature is Normal), and (Pulse-rate is Low), and (Oxygen-level is Critical), then (output is Alarming) If (Temperature is Normal), and (Pulse-rate is Normal), and (Oxygen-level is Critical), then (output is Alarming) If (Temperature is Normal), and (Pulse-rate is Low), and (Oxygen-level is Low), then (output is Alarming)
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Rule 2:
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fuzzy output is classified into the following three classes: Well, Alarming, and Critical as shown in Fig. 5.
Rule 3: Rule 4: Rule 5:
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Rule 6:
. . .
Rule 34: Rule 35: Rule 36:
If (Temperature is Fever), and (Pulse-rate is Normal), and (Oxygen-level is Normal), then (output is Alarming) If (Temperature is Normal), and (Pulse-rate is High), and (Oxygen-level is Normal), then (output is Well) If (Temperature is Hyperthermia), and (Pulse-rate is High), and (Oxygen-level is Critical), then (output is Critical)
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(b)
(c)
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Fig. 4 Fuzzy input membership functions of WSBAN layer: (a) Temperature in Fahrenheit (b) Pulse rate (per minute) (c) Oxygen level (% SpO2)
Fig. 5: Fuzzy output membership function for WSBAN layer
If the decision falls in the category of Well, then no transmission takes place. However, if the decision lies in the category of
analysis. 3.2. Fog layer processing: (i)
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Alarming or Critical then the defuzzified output along with the sensor data vector is transmitted to the fog node for further
Pattern Identification: Let the window size for pattern identification is K samples. The pattern identification mechanism
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picks data points (tj, Fj) where, j = 1, 2,…, K, from the data field stored in the auxiliary memory and a linear regression curve is drawn best fitted with the data points. The generalized regression function is F f (t , a) , where t and F are predictor (independent) and criterion (dependent) variables respectively, a [a0 , a1 , a2 ,, an ]T is the regression parameter vector containing (n+1) unknown parameters.
For simplicity, the polynomial linear regression technique has been used in this work [29]. The regression function of an nth order polynomial is given by
n
f (t ) a0 a1t a2t 2 ant n ai t i . i 0
(1)
The estimate of kth criterion variable is given by Fˆ f (t ) a t i and the corresponding estimation error is ek Fk Fˆk . ik k k n
i 0
The least-square criterion aims to minimize the following objective function given by (2), K
K
k 1
k 1
ek2 Fk Fˆk
2
K
n
k 1
i 0
Fk ai t ki
2
,
(2)
which is achieved by solving the set of following (n+1) partial differential equations given by (3), 0, 0, 0,, 0. a0 a1 a2 an
(3)
The solution of (3) can also be represented as
a T1.(tf ) ,
(4)
where, T is (n 1) (n 1) matrix, where its (p;q)th element is represented as K
T( p; q) t k( pq2) ,
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(5)
k 1
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and tf is (n 1) 1 vector and its pth element is represented as K
tf ( p) Fk tkp1 . k 1
(6)
f (t ) a0 a1t a2t 2 ,
(ii)
(7)
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and its polynomial coefficients are evaluated using (4).
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In the proposed work, a 2nd polynomial is considered by substituting n=2 in (1), which is represented as
Pattern Classification: In the proposed work Sugeno fuzzy model has been used to classify the patterns into three classes
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namely MI, Abnormal, and Reinvestigate. The inference engine takes the coefficients a2 and a1 as input parameters where a2 ranges from -1 to +1 and it is divided into three categories i.e. High-negative, Low-negative, and positive and a1 ranges from 0 to 9 and it is divided into three categories i.e. Low, Mid, and High. Therefore, the maximum possible number of rules is 9 as shown
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in Table 5. In both these cases, triangular membership function is used.
Table 5: Fuzzy rule base for Pattern classification Rule 1: If (a1 is Low) and (a2 is High negative) then (output is MI) Rule 2: If (a1 is Low) and (a2 is Low negative) then (output is Reinvestigate) If (a1 is Low) and (a2 is Positive) then (output is Abnormal) If (a1 is Mid) and (a2 is High negative) then (output is MI) If (a1 is Mid) and (a2 is Low negative) then (output is Reinvestigate) If (a1 is Mid) and (a2 is Positive) then (output is Abnormal) If (a1 is High) and (a2 is High negative) then (output is Reinvestigate) If (a1 is High) and (a2 is Low negative) then (output is Abnormal) If (a1 is High) and (a2 is Positive) then (output is Abnormal)
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Rule 3: Rule 4: Rule 5: Rule 6: Rule 7: Rule 8: Rule 9:
4.
Experimental Setup & Result Analysis
The experimental setup and analysis of the result have been discussed in this section. The WSBAN node has been fitted on the body of some volunteers as shown in Fig. 6. The sensor array is composed of a temperature sensor (MAX30205 is a digital sensor having inbuilt 16-bit ADC), and pulse rate/oxygen level sensor MAX3012 which is connected to the Arduino-Nano attached with a
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Dragino LoRa transreceiver. The schematic diagram of WSBAN layer is given in Fig. 7.
Fig 6. Real image of WSBAN node +5v
SCl GND
I2C BUS
SDA
Vcc MOSI
MOSI
MISO
MISO
SCl
SCK
SCl
GND
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SDA
SCK
SS
SDA
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MAX30205
4.7k
SS SPI BUS
GND MAX30102
Fig 7: WSBAN module schematic diagram
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Dragino LoRa Semtech SX1276
Arduino Lilypad
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4.7k
In the prototype testing, three cases have been considered (i) a normal person having all parameters in normal ranges, (ii) a normal person asked to run for 10min (iii) a patient suffering from mild fever. The sensor data is acquired for all the three cases
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for 20 minutes with a sampling interval of 60 seconds as shown in Fig. 8 and their corresponding defuzzified outputs are shown in Fig. 9. In this work, 35 is considered as the threshold value of deffuzified output and if the data falls within this threshold then no data transmission to the fog takes place. In Fig. 9 it can be observed that case-1 can be considered as safe and the remaining two cases are overshooting the threshold. Hence, the data vector of these two cases had been transmitted to the fog via LoRa module
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for further analysis. In the fog node, the time-series defuzzified output for both the cases is fitted with a second-order polynomial as shown in Fig 10. The coefficients of the polynomials of case-2, 3 have been used as the inputs to the Sugeno fuzzy model for classification. Table 6 shows the polynomial coefficient values for the above two cases and their classification results. The data pattern of case 2 falls under MI class and does not get transmitted to the cloud. The data pattern of case 3 falls under Reinvesitigate class and need to be analysed for the next window. Table 6: Coefficients of polynomials and pattern classification results Sl. No. a1 a2 Case 2 7.8199 -0.3496 Case 3 0.7849 -0.0301
Pattern Class MI Reinvestigate
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Fig. 8: Results related to WSBAN node: Sensor readings (a) Temperature in oF, (b) pulse rate per minute, (c) %SpO2,
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Fig. 9: Corresponding defuzzified outputs of three cases
(a)
(b)
Fig. 10: Results in fog layer Time-series defuzzyfied output fitted with second order polynomials (a) case 2, (b) case 3.
5.
Conclusions In this paper, a novel approach of distributed computing based health monitoring system has been proposed for the soldiers
deployed in adverse environmental conditions using the internet of things. The step-wise filtration of the redundant information at the WSBAN node and fog node has been done which results in improved channel utilization and reduced computational burden on the cloud. As a result, the system response time has improved significantly. A prototype of the proposed work has been
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developed and from the result analysis, it has been found that the layer-wise filtering approach removes the unnecessary data which results in fast system response time. Design of suitable wearable node may be considered as the direction of future
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REFERENCES:
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