The upgrade meant of WSN leads a very big way for the growth of mobility sensors for that improved technology several algorithms are designed. On the other hand, packet loss ratio and end-end delay are occurred due to the sensor node’s mobility which degrades the network lifetime in terms of energy consumption. Due to this, an intelligent cluster-based routing protocol has emerged. The intra-cluster routing is developed by assenting timeslot to each cluster member using TDMA-MAC. The inter-cluster routing is designed by using fuzzy logic which is helpful to route the collector messages. Though the sensor nodes are mobile, the specific schedule will be allocated for the dynamic type cluster only. Each cluster has a cluster head (CH). Though many CH algorithms is available, a novel approach for the selection of CH in a cluster is provided by the algorithm of CH election with counting. The movement of sensor nodes is examined based on their timeslot and the travelled distance. Artificial bee colony algorithm (ABC), which hires the swarm-based artificial intelligence technique used to enhance the performance of the network.
ABC has three categories of bees to find the cluster head; they are onlooker bee, scouts bee, and employed bees. Furthermore, the colony’s first half consists of employed artificial bees, and the next half comprises the onlooker bees. The proposed technique, the ABC algorithm is used to form the cluster and to consider the cluster head on their mobility factor. Here, the cluster head is elected in terms of mobility factor using the ABC algorithm. Additionally, fuzzy logic is used for inter-cluster communication based on mobility factors. The above-mentioned wireless sensor and cluster process in detail is explained. Now with help of a wireless sensor network (WSN) an android application is developed and using artificial intelligence (AI) patients’ data are continuously monitored. In past, conventionally the doctor or the nurse manually measure the IV fluid flow rate and adjust it. Practically, it is very difficult for the nurse to stay near the patient all the time. Now the improvement in the medical domain is monitoring the patient wirelessly. The sensed parameters values are transmitted to the Raspberry Pi to update the data continuously via ADC which will convert the analog signal to a digital signal. Through RF transmitter data is sent wirelessly to the doctor which can be monitored in their cabin. If any critical condition occurs the visual indication will occur in the cabin . The drawback of this system is remote monitoring and visual communication. A Remote Patient Monitoring (RPM) system is purposed towards improving patient monitoring on a general hospital ward. In this system, information is gathered from the patient and sent to the server. The RPM system can complement the role of nurses in monitoring patients’ information. This system is designed and stimulated. The result illustrated the capabilities and limitations of the chosen technology . The main drawback of this system is the unproved accuracy of devices.
It is an expensive method, especially in small areas. The system using RF Zigbee will help nurses in remote monitoring of IV fluid to the large extent . Zigbee is a small power consumption device, which has long-range communication outdoor. The drawback of this system is to monitor the IV fluid using remote only in certain long distances. Later the patient’s physiological conditions are automatically monitored using GSM . But by using a GSM modem only systolic pressure is applied and the intravenous fluid level is below a certain limit and identifies the fluid level, is too low.
The system uses a CC2500 Trans receiver which can automatically monitor the IV fluid flow rate and health parameters by using a microcontroller. There is no need to stay near to the patient at all times . It wirelessly sends the data to the nurse or doctor on a computer that is not secured because it can be theft easily and it will display the result in an IV fluid droplet rate. To design an intravenous fluid control device the main difficulty was to develop a device that should respond rapidly and the detection of the fluid drop is not perfect. The system using a flow sensor with a Bluetooth connection is used to overcome this difficulty. The device is used in medical applications as well as in chemicals to sense the flow of fluid. These devices calculate the flow rate constantly at a low cost, especially in developing countries [10–11]. But the Bluetooth connection is for 15month only. A monitoring system retro-fittable for chamber-based intravenous therapy has two main parts - chambers and pole. The chamber has two detection sensors and the pole has a microcontroller and GSM-based communication module. The data are transmitted to cloud service HTTP API call. The drawback of this system is there is no control of IV fluid and data loss. In the warning system based on RFID ID technology, RFID ID TAG is enabled when the IV bottle is empty because of EM loading . The drawback of this system implementation of RFID is to be difficult and cost-effective.
P&N IC SYSTEM:
The proposed system of this paper is to detect and control the IV fluid flow rate and to monitor the oxygen level in the brain using a mobile application and to eject the venflon using a robotic hand this process is done using P2N IC system(Patient to Nurse intercommunication system) as shown in Fig. 2. In Fig. 2, we can see that the SpO2 sensor, Temperature sensor and the Pressure sensor are connected to the amplifier. The IR sensor is connected to the SCU. Further the connection is connected to the Micro Controller Arduino Mega. The Micro Controller Arduino Mega is mainly connected to the LCD and to the Buzzer for alertness and for the observation. The data are collected from the patient using WSN and continuously monitored using Artificial Intelligence. The first process of this system is to monitor the intravenous fluid rate using sensors . The sensor collects the fluid rate continuously from the IV fluid bottle and updates it in the android application. If the fluid rate falls below the sensor there will be no detection in the sensor. This condition is updated in the application as an abnormal condition. When the application gets that update it intimates the nurse about the abnormal condition of the patient. The application comprises the patient details like temperature rate, pressure rate, intravenous fluid rate, the oxygen level in the brain, heartbeat rate. Added to that it contains the status of the patient and there are options to increase or decrease or to stop the IV fluid flow rate and pressure rate. And also there is an option to eject the venflon from the hand using a robotic hand when the IV fluid flow rate is stopped using the application .
The next step of this system is to control the intravenous fluid rate. After getting the intimation the nurse may increase or decrease or even stop the IV fluid flow using the application. When the IV fluid flow rate is stopped using the application, if need, the venflon is ejected by giving the command ‘eject’ in the application. Using a wireless sensor network the data are collected from the nurse . In this system, vital sensors are gathered from the patients and sent to the control unit for centralized monitoring. A single ward is considered a cluster node and the nurse is considered a cluster head (CH). Using a wireless sensor network the data are collected from the CH and sent to the doctor who is considered as a base station.
Figure 3 shows the monitoring process of patient. The information is continuously monitored in the base station using Artificial Intelligence. Through artificial intelligence, the abnormal conditions are intimated to the doctor who is monitoring the base station. This system consists of sensors, relay circuits, robotic arms, etc. If any part of this system gets at faulted, it will show the wrong value which leads to various problems for the patient as well as the doctor . So through the internet connection, an alert mail will be sent to the base station if any part of the system gets at faulted. Through the alert mail, the doctor can rectify the fault immediately.
The Fig. 4 shows the flow chart which represents the process of monitoring system of oxygen level in the brain and IV fluid flow rate. This paper illustrates, the automatic monitoring system of oxygen level in the brain and controlling system of IV fluid flow rate and ejection of venflon using android application.
- STEP 1: START
- STEP 2: It establishes the connection between the network and the controller.
- STEP 3: The patient’s details like temperature rate, pressure rate, IV fluid flow rate, oxygen level, and heartbeat rate are read by the sensor.
- STEP 4: The sensor values are projected on the display.
- STEP 5: The data collected from the sensor are uploaded to the cloud.
- STEP 6: If the sensor values are greater than the threshold values which are already programmed with the Arduino, it will send the data to the android application. If the sensor values are lesser than the threshold value the data will be retained in the cloud.
- STEP 7: After the IV fluid flow rate is stopped using a mobile application if there is a need to eject the venflon, through the ejection option in the application the venflon can eject using a robotic arm.
- STEP 8: If there is no need for ejection, the data will be retained on the cloud.
- STEP 9: This system contains a sensor, relay circuit, transformer circuit, etc. If there is no detection in the sensor or any fault occurs in the relay circuit or in the transformer an alert mail will be sent to the server. Through that alert mail, the fault can be recognized and rectified.
- STEP 10: STOP.
Using Wireless Sensor Network, the patient details are stored in baste station using WSN and continuously monitored using Artificial Intelligence which is represented in the flow chart figure no. 5.
- STEP 1: START
- STEP 2: The patient details were sent to the nurse.
- STEP 3: The nurse collectively sends all the patient details in the particular ward to the base station using Wireless Sensor Network.
- STEP 4: The data are stored in the database which has separate files for the ward which is known as a cluster.
- STEP 5: The database is continuously monitored using Artificial Intelligence.
- STEP 6: If any abnormal conditions like sudden increase or decrease in the IV fluid flow rate, temperature rate, pressure rate, or oxygen level an indication will send for the doctor.
- STEP 7: STOP.
Figure 6 shows the prototype of P2N IC KIT. It contains 3 separable parts which include sensor connections with power supply, an air generator for external pressure, and a saline bottle setup.
A. ARDUINO AT MEGA 2560:
Arduino at mega 2560 is a microcontroller board. It has 54 digital input/output pins from which 15 can be used as PWM output, 16 analog inputs and 4 hardware serial ports, 16MHz quartz crystal, USB connection, a power jack, an ICSP header, RESET button, 256KB memory of which 8KB used by the boot loader, 8KB RAM, 4KB EEPROM. It has a large amount of memory space. The programming of this system is done in the board with the help of Arduino IDE software using a USB cable. The C language is used for programming. The specifications of Arduino at mega are listed below in Table 1.
Specification of Arduino at mega:
Table 1 : specification of Arduino at mega 2560
At mega 2560
Current rating per I/O pin
Current drawn from chip
Yes/attached with digital pin 13
Various types of sensors are used in medical field. Here we use certain sensors that are relatable to the research of health care monitoring. They are
- IR sensor
- SpO2 sensor
- Temperature sensor
- Pressure sensor
B.1. IR sensor:
IR sensor is used to detect the IV fluid flow. An infrared sensor (IR) is an electronic monies that amplitude light radiating from point of an object or a thing. By using IR rays, the IV fluid drops can be measured. IR sensor which is used as the frequency of 38 kHz. If the solution crosses the fixed limit, there is no detection in the IR sensor. Fig 7 shows the working model of IR sensor detecting the completion of saline. We can also a critical level detector in the fig 7 given below.
B.2. SpO2 sensor:
The SpO2 sensor is an optical sensor that cans vestige oxygen saturation using red and infrared light. SpO2 sensor is used to measure the heartbeat rate and oxygen level in the brain. It gives results in percentage. As shown in the fig the sensor is placed in the fingertip which measures the oxygen and heartbeat rate from the blood circulation. The pulse rate ranges from 30-254bpm.
B.3. Temperature sensor:
This sensor node is used to measure the temperature of the member node. It requires a thermocouple and Resistance Temperature Detector. It collects the data about temperature from a particular source and converts the data into an understandable form to the observer. This is a type of Negative Temperature Coefficient 5kOhm resistance. If any rise in the temperature the value of sensor voltage changes. By changes in the voltage, there may be a rise in a patient’s pressure value.
The pressure sensor is a device that is used to measure the pressure of gas or liquid. Pascal is the unit of pressure and its measurable quantity is 5kg. Pressure is defined in terms of force/unit area. The pressure sensor acts as a transducer that generates a signal in terms of the function of the pressure given. Its temperature ranges from − 73 to + 135 degrees Celsius. Its measures in the range 0 to 344 bar.
C. DRIVER CIRCUIT:
In electronics, a driver circuit is an electrical circuit or it is an electronic component that is used to control other circuits and provides a high-power to a transistor. Here two transformers are used which convert the 230V into 5V with 1amps and 12V with 4amps using a rectifier and regulator circuit. The 5V can be given constantly to the Arduino board, IR sensor, temperature sensor, pressure sensor, SpO2 sensor Wi-Fi module. And the 12V is given to the relay circuit, IV fluid control DC motor. Using a relay circuit the DC motor rotates front and back, which increases or decreases the IV fluid flow rate. The Metal Oxide Semiconductor Field Effect Transistor (MOSFET) is used for switching and transmitting the voltage to the relay circuits.
The Wi-Fi module ESP8266 is a low-cost microchip with a TCP/IP stack. This module will give any microcontroller to access the internet connection. It acts as access points that provide a network for the system. So, several systems can be connected to Wi-Fi. Therefore 10 patients in a ward can be monitored by a single nurse using a Wi-Fi connection. The status of the patient like temperature rate, pressure rate, and IV fluid flow rate, and oxygen level, heartbeat rate can be updated to the application using a Wi-Fi connection.
This system handles Artificial Intelligence, Wireless Sensor Network, Wi-Fi, Android application, Arduino IDE. The sensor senses the patient’s parameters. The pulse is gained from the sensor and the signal from the pulse gets counted. The counted pulse rate is stored in the random access memory. The values obtained are associated with former values which are already programmed in Arduino IDE with the help of embedded C language. Through this, the system decides the IV fluid flow rate whether it is very low or low or high. The Arduino sends a signal to the android application using a Wi-Fi connection. If the flow declines, then the corresponding abnormal message will be sent to the android application. The application is developed in the android studio with the help of the java language. Likewise, the pressure and temperature sensors send data. The SpO2 sensor continuously senses the blood circulation which sends the signal to the application. The signal has the data of oxygen level and heartbeat rate. Through the application, the nurse can monitor the oxygen level and heartbeat rate. The status of the patient is updated to the nurse who acts as CH. The patients in the ward are considered as nodes and the nurse is considered as CH. The signal from each CH is carried to the base station using WSN. In the base station, the status of the patient is stored in the Database, using which the doctor can monitor all the patients in the hospital.
The database collects the data in the form of (i) the patient’s file stores the data of each patient. (ii) The nurse file contains the data about each patient. (iii) The cluster stores the information about the nurse file (iv)The ward file stores the patient’s details from the various clusters. (v) The doctor file stores the details about the number of wards files. This mechanism is based on My SQL with the help of the java language. As shown in Fig. (9) the database is monitored by the booming technology Artificial Intelligence. It continuously monitors the data, from BS if any difference in the collected data it will indicate in the doctor’s monitor in the form of a datasheet. The data sheet contains the details of ward number, nurse, and patient. Figure 9 shows the block diagram of processing software