1 Introduction
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Our existing work in [21] only considers on-body sensors/relays. Similarly, our work in [20] only considers the implanted pacemaker. In this paper, both works are combined, i.e., the patients are equipped with on-body sensors and an implanted pacemaker. This consideration makes the simulation scenarios (1 to 5) different from our work in [21].
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Unlike [21], the impact of mobility due to postural changes of the patient(s) arms, legs, and head is also considered.
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In this paper, an enhanced version of our previously proposed DARE protocol in [21] is presented, i.e., MI-DARE. The newly proposed MI-DARE protocol uses MI-based machine learning technique to prolong the network lifetime of sensors by minimizing the number of redundant transmissions.
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In this paper, the mathematical model for the non-invasive inductive link includes a fly back diode to prevent voltage surge(s) which was not the case in our previous work in [20]. Moreover, Section 6 related to the mathematical model has been strengthened with the addition of quality factor analysis.
2 Related work
2.1 Inductive link design
2.2 Energy-efficient routing
3 System model
3.1 Network topology
3.2 Types of data reporting
3.3 Functionality of the pacemaker
3.4 Induction link and its parameters
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Voltage gain: It is the ratio of the output voltage to the input voltage, i.e., V out/V in.×
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Link efficiency (η): The ability of transferring power from the primary side to the secondary side is known as the link efficiency.
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Quality factor: It is a widespread measure used to characterize resonators. The higher the quality factor, the higher is the resonating effect.
4 Mathematical model for induction
4.1 Equivalent circuits
4.1.1 Series tuned primary circuit (STPC)
Parameters | Values |
---|---|
Operating frequency |
f=13.56 MHZ |
Primary coil |
L
1=5.48μH |
Secondary coil |
L
2=1μH |
Parasitic resistance of the transmitter coil | R
L1≃2.12Ω
|
Parasitic resistance of the receiver coil | R
L2≃1.63Ω
|
Load resistance | Rload=320Ω
|
4.1.2 Series tuned primary and parallel tuned secondary circuit (STPPTSC)
5 The proposed routing protocols
5.1 Communication flow of DARE protocol
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First, the body sensor is checked whether it is alive or not. If yes, then the algorithm checks for the body relay to be alive. If the body sensor is found alive, then it checks whether it is a threshold measuring sensor or a continuous data monitoring sensor.
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If the body sensor is a threshold measuring sensor, it checks if the low or high threshold levels are reached. If yes, then it measures the distance between that body sensor and body relay.
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Afterwards, it calculates the energy consumption costs in the transmission process and in the reception process for the body sensor and the body relay, respectively.
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Then, it estimates the delay in propagating the data from the body sensor to the body relay.
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If the threshold is not reached, then the algorithm proceeds to check the other body sensors in the same manner and calculates the distance, remaining energy, and delay. Finally, the estimated parameters are separately stored in different variables.
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This whole process continues till all the body sensors are checked.
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When the body relay receives data from all the sensors, it aggregates the received data and transmits these data either directly to sink (static or mobile) or MS, depending upon the particular scenario.
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After the data aggregation phase, the transmission and remaining energy of the body relay is calculated. The remaining energy is given as$${} \text{Remaining energy} = \text{Initial energy} - \text{Transmission energy} $$(18)
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After checking all the sensors of the first patient, the protocol operation advances towards checking the next patient.
5.2 Mobility in DARE
5.3 The MI-DARE routing protocol
6 Experimental results and discussions
Sink | Position | |
---|---|---|
x (m) | y (m) | |
Sink1 | 0 | 3 |
Sink2 | 6 | 6 |
Sink3 | 12 | 3 |
Sink4 | 6 | 0 |
Parameter | Value |
---|---|
E
TXelec
| 16.7 nJ/bit |
E
RXelec
| 36.1 nJ/bit |
E
amp
| 1.97 nJ/bit |
E
amp
| 7.99 nJ/bit |
n for LOS | 3.38 |
n for non-LOS | 5.9 |
w
| 4000 bits |
6.1 The mathematical model
6.1.1 STPC
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Voltage gain: From the equation for voltage gain, V load/V s for STPC is directly proportional to R load. In Fig. 15, when the value of k increases from 0.2 to 0.8, V load/V s also increases. For every value of k, V load/V s shows almost a linear response as R load increases from 0 Ω to 100 Ω.×For k = 0.4 and R load=320 Ω, V load/V s =2.2. We have chosen the aforementioned values of k and R load because these make the circuitry harmless to human tissues.
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Link efficiency: It is clear from Fig. 16 that the value of the link efficiency is highly dependent on k. For k=0.4 and R load, the value of η is 0.75.×
6.1.2 STPC and STPPTC
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Voltage gain: In Fig. 17, changes in the voltage gain V load/V s , by varying R load for STPC and STPPTC are shown. It is clear from the figure that changes in R load do not produce any significant change in V load/V s . The only noticeable increase in V load/V s is produced by increasing k. However, due to the fact that this link is used on human body to induce voltage to the implanted device, the value of k changes only from 0 to 0.45. From R load=0Ω to 100 Ω, the behavior of V load/V s is nearly constant. After reaching 100 Ω, it increases almost linearly. For k = 0.4 and R load = 320 Ω, the voltage gain is about 3.7. Hence, the value of V load/V s is significantly higher, when the secondary circuit is tuned in parallel as compared to the tuning of the primary circuit. Moreover, the output voltage is nearly four times higher than the driving input voltage.×
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Link efficiency: The link efficiency graph for STPPTSC is shown in Fig. 18. For every value of k, this figure depicts the steepness of the link efficiency till R load= 100 Ω. After this value, the link efficiency of the circuit becomes constant. For k = 0.4 and R load = 320 Ω, the link efficiency is about 0.9, i.e., 90 %. In comparison to the STPC, the link efficiency of the parallel tuned secondary circuit increases by 15 %. In other words, 90 % of the input power is efficiently transferred to the secondary side.×
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Quality factor: Fig. 19 shows the comparison plot of quality factor for both the equivalent circuits. It is clear from the figure that quality factor increases as the operating frequency is increased. For the safety of body tissues, the frequency is set to be 13.56 MHz.×The quality factor of STPPTSC is higher than the other circuit which means that the second equivalent circuit achieves good tuning under resonant conditions.
6.2 DARE and MI-DARE protocols
Parameter | DARE | M-ATTEMPT |
---|---|---|
Types of devices | Body sensors | Sensors |
Body relay | Sink | |
Main sensor (MS) | ||
Sink | ||
Deployment | Body sensors, body relays, and MS are fixed | Sensors and sink both are fixed |
Sink can either be static or mobile | ||
Topology per patient | 7 body sensors | 7 sensors |
1 body relay on chest | 1 sink on chest | |
Communication flow | Scenario 1: body sensors to body relays to sink | Sensors to sink or sensors to other sensors to sink |
Scenario 2: body sensors to body relay to nearest sink | ||
Scenario 3: body sensors to body relay to MS to sink | ||
Scenario 4: body sensors to body relay to moving sink | ||
Scenario 5: body sensors to body relay to nearest moving sink | ||
Energy parameters |
E0BSs=0.3J
|
E0sensors= 0.3 J |
E0BR=1J
|
E
Sink= infinite | |
E
MS= infinite | ||
E
Sink= infinite | ||
Network type | Heterogeneous in terms of energy of body sensors and body relays | Homogeneous in terms of energy of sensors |
Communication type | Multi-hop | Single-hop |
Multi-hop | ||
Types of data reporting | Event-driven | Event-driven |
Time-driven | Time-driven |
6.2.1 Stability period and network lifetime
6.2.2 Throughput
6.2.3 End-to-end delay
Metric | MI-DARE | DARE | M-ATTEMPT | |
---|---|---|---|---|
Stability period | Highest | Moderate | Least | |
Network lifetime | Highest | Moderate | Least | |
Energy consumption | Least | Moderate | Highest | |
Packet delivery ratio | High | High | Moderate | |
End-to-end delay | Highest | High | Least |