Performance Evaluation of Wireless Sensor Network with LPWA for Medical applications
著者 ププット ダニ プラセティヨ アディ
著者別表示 PUPUT DANI PRASETYO ADI journal or
publication title
博士論文本文Full 学位授与番号 13301甲第1919号
学位名 博士（工学）
学位授与年月日 2020‑09‑28
URL http://hdl.handle.net/2297/00061383
doi: https://doi.org/10.14569/IJACSA.2020.0110232
Performance Evaluation of Wireless Sensor Network with LPWA for Medical
applications
医療応用のための省電力広域無線センサネットワ ークの性能評価
Puput Dani Prasetyo Adi
SEPTEMBER 2020
DISSERTATION
Performance Evaluation of Wireless Sensor Network with LPWA for Medical applications
医療応用のための省電力広域無線センサネットワ ークの性能評価
Graduate School of
Natural Science & Technology Kanazawa University
Division of
Electrical Engineering and Computer Science
Student ID : 1824042014
Name : Puput Dani Prasetyo Adi
Chief Advisor : Prof. Akio Kitagawa
June 2020
Acknowledgements
Thanks to the Lord Jesus Christ for giving the wisdom to do this Dissertation, I also thank all parties without exception who helped me, especially Professor Akio Kitagawa, who always provided the best support for my personal development while I studied at MeRL (Micro Electronics Research Laboratory). Thank you for the assistance of Professor Akio Kitagawa in financing some international journals and equipment, Signal analyzers, Digital Microscope that I used during my research at MeRL, Professor Akio Kitagawa was like a father to me while I was at MeRL.
I express my deep gratitude, for my wife and daughter, which God has entrusted, while I studied at Kanazawa University, they always faithfully used video calls to communicate every day, they were in Indonesia during my studies at Kanazawa University.
for my wife, Juwita Ekalia Noviyanti, thank you for the love, trust, and prayer that you always pray for your husband. Also thanks to Fidelia Danita Joaquin who always provides motivation and enthusiasm for papa to complete papaʹs dissertation.
Thank you to my father and mother, Mr. Sukaryo and Mrs. Sri Zatmiati, for all their prayers, directions, affection, and endless advice. Thanks to Papa Daniel Alexander, for his help to me since I first met in Yogyakarta, I hardly have a future. Thank you for the scholarship that I gave during my undergraduate study at STMIK AKAKOM Yogyakarta.
Thanks to PESAT - Nabire who was a family during my teaching in Papua, Mr. Edo, Mr. Gunawan, Ms. Sambara, Ms. Yoke, and Mr. Samuel, and all of my fellow teachers and the PESAT Nabire boarding children without exception.
Thank you to Dr.Eng.Muhammad Niswar at Hasanuddin University who provided recommendations for continuing research to the Doctoral, Thank you also to all my brothers, Suhari Adi and Budi Sulistyo, the father and mother-in-law and all the family, Mr.Yuwono, Mrs. Endah, Vira, and Mbah jun who always gave me encouragement, prayer, and motivation so that I could complete my doctoral studies. thanks for Mr.Shiro Nakabayashi and Mrs.Bodil Nakabayashi, Stephen and Abigail melton munday and all Hope House family without exception, thank you for growing together in praise and worship while in Kanazawa-Ishikawa.
furthermore, Thank you For the MEXT Monbukagusho JAPAN scholarship, which has supported funding and living expenses while at Kanazawa University JAPAN, I am grateful.
Thank you to all Kanazawa University staff and lecturers who have assisted in all academic activities at Kanazawa University. Thank you to Professor Mambo Masahiro and Professor Junichi Akita for your help and guidance, good luck and always be successful in everything.
Thank you to the Merdeka University of Malang who has always followed the progress of my study in the Doctoral program at Kanazawa University-JAPAN. Thank you to all your fellow lecturers at University of Merdeka Malang, Electrical Engineering Department.
Thank you also to all Indonesian colleagues at Kanazawa University who are members of the Indonesian Student Association (PPI) in Kanazawa Ishikawa - JAPAN for being together, we hope success will always be with us.
Kanazawa, June 2020
Puput Dani Prasetyo AdiAbstract
Recently, Wireless Sensor Network (WSN) devices continue to be developed in Power consumption, size or dimension, and functionality (medical, Weather monitor, pet cow movement, Forest fire point monitoring, etc.), WSN devices must have 3 the following criteria, i.e., Low Power Consumption, High-Speed Data Transmission, Long Range. however when compared to devices such as Bluetooth, WiFi, ZigBee, and LoRa it has its specifications that canʹt be compared. The conclusion canʹt reach 3 WSN criteria perfectly, e.g., Bluetooth and WiFi have High-Speed Data Transmission (Mbps) capabilities, therefore, they are large in Power Consumption and Short Range or distance. Furthermore, LoRa, Low Power Consumption and Long Range (km), but, Low-Speed Data Rate or Data Transmission (bps). Therefore, a LoRa is not able to send large capacity Payload data such as video files, music, or Pictures. However, view from the functionality factor, LoRa is one of the Radio Frequency (RF) devices selected in this research for sending the sensor data with a low Bit Rate or Data Rate (bps) for monitoring Human health, patients with disabilities, prioritizing natural disaster vic- tims based on the concept of triage use pulse sensor, and other sensors e.g, Temperature data, SPO
2, and Blood Pressure, on this research focuses on heart rate (bpm), therefore, that a Low Power Wide Area (LPWA) device is realized with detailed analysis. In this dissertation, several Nircable devices are discussed to build Low Power WSN, e.g., Bluetooth Low Energy, ZigBee, and LoRa. These devices communicate with the Gateway or Internet Server and Application Server, therefore, that Uplink and Downlink communication occurs, in general, the WSN devices that communicate with each other on the Internet of Things (IoT). In the IoT there are thousands of sensor node devices that are connected to the Gateway or Internet server, therefore, the need for management to keep the Node or WSN sensor alive for a long time, this research also discusses about the topology approaches in the WSN to regulate the Power Consumption of Sensor nodes.
Keywords: wireless sensor network, power consumption, low power, small data rate,
long-range, medical, monitoring, internet of things
Table of Contents
Acknowledgements ………. ii
Abstract……….………….. iii
Table of Contents……….. iv
List of Figures ………... viii
List of Table ……….. xiii
Chapter 1. Introductions ………. 1
1.1.Background Overview………. 2
1.2.Research Objective……… 7
1.3.Contributions……… 8
1.4.Dissertation Organization………... 9
Chapter 2. Fundamental Literature Review ……….….. 11
2.1 Data Acquisition ………..…………... 11
2.2 Duty Cycle ……….….. 12
2.3 Time Cycle (Tcycle) ……….……… 12
2.4 Body Temperature Sensor ……….. 13
2.5 Pulse sensor ………..………..………….. 14
2.6 MH-ET Live Sensor ………... 17
2.7 Blood Pressure Sensor ………..……..……….. 18
2.8 BMP280 and BME280 Sensor ………..…………..….. 23
2.9 Basic of Sensor Node ………..……..….. 24
2.10 Medical Internet of Things (MIoT) ……….... 25
2.11 Raspberry Pi 4 Model B ………..……….…… 28
2.12 Bluetooth Radio Frequency (RF) ……….. 30
2.13 ZigBee or XBee Radio Frequency (RF) ………... 33
2.14 Zigbee and Blood Pressure Connectivity ……….. 37
2.15 LoRa (Long Range) Radio Frequency ………...….. 41
2.15.1 LoRa Regulations ……… 41
2.15.2 CHIRP LoRa ………... 43
2.15.3 E32 LoRa End Node ……… 47
2.15.4 Dragino LoRa 915 MHz ………... 50
2.15.5 ES920LR LoRa End Node ………. 53
2.15.6 Pairing the ES920LR LoRa ……….. 59
2.15.7 LoRa Gateway ………. 64
2.15.7.1 LoRa LG01 ………. 64
2.15.7.2 ES920LR GW………... 66
2.15.7.3 Dragino LoRa and RaspBerry Pi 3 Gateway……….……….. 67
2.16 Simulation for Low Power Consumption of Sensor node ………. 69
2.16.1 Energy Models ……….. 75
2.16.2 Powertrace on Wireless Sensor Networks Communication………. 78
2.17 Wireless Sensor Networks Parameter ……… 80
2.17.1 Power Receiver (Pr) (-dB) and RSSI (-dBm) with exponent value….. 80
2.17.2 Power Receiver (Pr) (-dB) and RSSI (-dBm) with Gain ……….… 81
2.17.3 Free Space Path Loss Condition (L)……….… 83
2.17.4 RSSI of LoRa ……… 84
2.17.5 Energy and Power Signals ……… 85
2.17.6 PathLoss (dB) and attenuation signal ……….. 86
2.17.7 LoRa Parameter ……… 86
2.17.8 Signal to Noise Ratio (SNR) ……… 88
2.17.9 LoRa Parameters Packet……….. 89
2.17.10 Spreading Factor……… 90
2.17.11 The Shannon-Hartley Theorem……… 93
2.17.12 Sensitivity (S) of LoRa ……….………. 94
2.17.13 Link Budget Of LoRa ……… 95
2.17.14 Bit Rate or Data Rate (Rb) ……… 95
2.17.15 Time on Air (ToA) ………... 95
2.17.16 LoRa Symbol Symbol Duration (Ts) or Tsym ……….. 96
2.17.17 Signal to Interference Ratio (SIR) ………..…….. 96
2.17.18 Bit ErrorRate (BER) ……….……..96
2.17.19 Packet ErrorRate (PER) ………..……….. 96
2.17.20 Eb/No and C/N ………. 97
2.17.21 SNR (-dB) ………...…… 97
2.17.22 Coding Rate (CR) ……… .97
2.17.23 Symbol Rate (Rs) ……….………..98
2.17.24 Bandwidth or Chip Rate (Rc) ……….………..98
2.17.25 RSSI (dBm) ………...………..99
2.17.26 Long Range Radio Propagation for LoRa FSPL………99
2.17.27 A Type of obstacle materials during radio propagation ……… 100
2.17.28 Two-Ray Ground Reflection (2-ray) model ……….… 100
2.17.29 Fresnel Zone ……….………103
2.17.30 Spesific Attenuation due to rain ………108
2.17.31 Spesific Attenuation due to Snow ………...………...….109
Chapter 3. The Methods used on the Wireless Sensor node ……….112
3.1 Block Diagram, Flowchart, and Design ……… 112
3.2 Adaptive Data Rate ……… 113
3.3 Energy WSN efficiency use Protocols Algorithm ……….116
3.4. Research Architecture of LoRa on Radio Propagation……….. 117
3.5 Sleep Mode ………...118
3.6 Power Consumption and Delay Strategy……… 120
3.6.1 Power Consumption of Lora node uses E32 and ES920LR…………... 120
3.6.1.1 Measurement of Battery Life (H) of Receiver node ………121
3.6.1.2 Measurement of Battery Life (H) of Transmitter node………121
3.7. Efficiency Energy with increase Tdelay to minimize the Current…....123
3.8 Development of Sensor Nodes using Leafony and LoRa ES920LR …..129
3.9 LoRa Micro Board (Leafony Board) design ………..130
3.10 ES920LR Radio Propagation ………..134
Chapter 4. Result and Analyze of Wireless Sensor Networks ……… 138
4.1 Bluetooth Sensor Node, Temperature Sensor, and RSS Analyzes …….138
4.1.1 Voltage and temperature Analysis ………...138
4.1.2 Receiver Signal Strength indicator (RSSI) of RN-42 Bluetooth ………..140
4.1.3 The Temperature data on MySQL database ………142
4.2 XBee Sensor Node, Pulse Sensor, and RSS Analyzes ………..143
4.3 Algorithma Protocol for WSN to Power Consumption efficiency : Furthermore ………149
4.3.1 Protocols Algorithm………...149
4.3.2 Protocols Algorithm Comparison Result………..151
4.4 Consumption node with ToA Analysis ……….. 153
4.5. Various Sensor Output……….…….. 155
4.5.1 Pulse Sensor use HTML JSON and MySQL dB……….. 155
4.5.2 Blood Pressure Output Graph on the Web Page ……….157
4.5.3 Sensor Node analysis uses LoRa 915 MHz and 920 MHz……… 158
4.5.3.1 BME280 Sensor Output ……….158
4.5.3.2 SPO
2Sensor Output ……….. 160
4.6 Output Sensor on Application Server………...165
4.7 Bit Rate, Bandwidth, SF, ToA, S, and LoRa Characteristic………..166
4.8 LoRa of certain circumstance approaches ………169
4.8.1 2-Ray Ground Reflection model……….. .169
4.8.2 Free Space Path Loss (-dB) of LoRa………...170
4.8.3 Materials causing attenuation in LoRa propagation………...171
4.9.1 RSSI Analysis Measurement Comparison on different situation…….. 172
4.9.2 RSSI LoRa measurement with signal analyzer……….172
4.9.3 RSSI LoRa at Indoor (realtime)………...173
4.9.4 RSSI LoRa type measurement with different obstacles and distance... 175
4.9.5 Area Measurement realtime………...176
4.10 Approach to PRR (%) ………...179
4.11 RSSI Analysis on the bad Wheater ……….180
4.12 RSSI with SF approach………... 181
4.14 Output Realtime Signal Analyzer ……….183
4.14.1 E32 LoRa Signal Analyze with Analyzer……….. 183
4.14.2 Realtime chirp sinyal ………..185
4.14.3 MH-ET LoRa Observations using the Signal Analyzer………...187
4.14.4 Radio frequency Dragino LoRa 915 MHz with signal analyzer……...188
4.14.5 ES920LR LoRa Signal Analyzer………190
Chapter 5. Conclusion 5.1 Conclusion ………...196
5.2 Future Work ………197
Publication List ………...……….………. 198
Bibliography ………...….……. 201
Appendix ………...……. 213
Appendix A. C++ Programming Transmitter LoRa ES920LR………...…. 214
Appendix B. C++ Programming on Application Server Thingspeak …….... 218
Appendix C. Realtime RSSI (-dBm) example………... 227
Appendix D. LoRa Regional Parameters ………...…………...……….230
Appendix E. C++ Program LoRa 915 MHz Sender & Receiver (RSSI & SNR) MH-ET………...…………...……….231
Appendix F. Pulse Sensor Schematic ...232
List of Figures
Figure 1.1. Disaster Condition ………. 3
Figure 1.2 Disaster victims priorities based on severity, infrastructure damaged include telecommunication and electricity ……… 3
Figure 1.3 Paper and Label Priority on Triage concept ……… 4
Figure 1.4 implementation the triage consept on the WSN………. 4
Figure 1.5. Disability Patients and victims of natural disasters as End Nodes…… 5
Figure 1.6. End Devices (ED) and Gateway (GW) position……… 6
Figure 1.7. Gateways (GWs) Communication with Internet and Application Server……… 6
Figure 1.8. Network Architecture on this research……… 8
Figure 2.1. Digital Data Acquisition System ………..………… 11
Figure 2.2. Duty Cycle ……… 12
Figure 2.3. LM35 Temperature sensor ………..……… 13
Figure 2.4. LM35 on the board ………..……… 13
Figure 2.5. Schematic of LM35 Temperature Sensor ……….. 14
Figure 2.6. MLX90614 Temperature sensor ……….… 14
Figure 2.7. Pulse Sensor ……….… 15
Figure 2.8. Pulse Sensor position on the finger ………..……… 15
Figure 2.9. Pulse Sensor position on the earlobe ……… 15
Figure 2.10. Pulse Sensor Visualizer ……….………… 16
Figure 2.11. Normal Heartbeat ………..……… 16
Figure 2.12. Tachycardia ………..……… 16
Figure 2.13. Bradycardia ………. 17
Figure 2.14. Pulse Oximetry and Heart-Rate Sensor pins ……….……… 17
Figure 2.15. Pressure Sensor ……….………….. 18
Figure 2.16. Pressure Sensor Pins……… 18
Figure 2.17. Dimensions of Pressure sensors ……… 19
Figure 2.18. Pins of LM358N Op-amp ……… 19
Figure 2.19. IC and Schematic of LM358N Op-amp ……… 19
Figure 2.20. Wiring of Blood Pressure Sensor ………..…… 20
Figure 2.21. Block Diagram of a Blood Pressure Sensor ……….… 20
Figure 2.22. Blood Pressure Classification for Adult ………..… 21
Figure 2.23. Uni body of Blood Pressure sensor MPX10 type ……….….. 21
Figure 2.24. Bandpass Filter Stage ……….… 22
Figure 2.25. BME280 Sensor from Sparkfun ……… 23
Figure 2.26. BME280 Sensor used on this research ……….……… 23
Figure 2.27. BME280 Sensor from Adafruid ……….………… 23
Figure 2.28. IC BME280 and Block Diagram BME280 Sensor ……….… 24
Figure 2.29. Block Diagram of Sensor Node ………..………… 24
Figure 2.31. LTE CAT M1 ………..… 26
Figure 2.32. e-health board ……… 28
Figure 2.33. RaspBerry Pi 4 Model B Board ……….……… 29
Figure 2.34 Bluetooth and RF devices comparison ……… 30
Figure 2.35. Bluetooth RN-42 module and Dimension ………..……… 31
Figure 2.36. Zigbee architecture ………. 33
Figure 2.37. Zigbee S2c ……… 34
Figure 2.38. Zigbee Star, Tree and Mesh Network Topologies………..… 34
Figure 2.39. Node Sensor Schematic use Fritzing ……… 35
Figure 2.40. Node Sensor ……… 36
Figure 2.41. ZigBee end Devices and ZigBee Coordinator Connectivity……….… 37
Figure 2.42. Blood Pressure Connectivity testing ……… 37
Figure 2.43. Raspberry Pi 3 and Zigbee RF module Connectivity ……… 38
Figure 2.44. Radio Frequency devices with data rate (bytes) and Range Comparison ……… 41
Figure 2.45. ISM Frequency Band Regulation in JAPAN ……….. 41
Figure 2.46. LoRa Layers ……… 42
Figure 2.47. Parts of Transverse and Longitudinal Waves……….… 43
Figure 2.48. Modulation Signal Types………..… 44
Figure 2.49. Chirp with t = 10……….… 44
Figure 2.50. Chirp with t = 40……….…… 45
Figure 2.51. Up and Down-Chirp Signal LoRa……… 46
Figure 2.52. LoRa Signal with Encoded (data) on……… 46
Figure 2.53. Chirp Signal from Signal Analyzer……….. 46
Figure 2.54. LoRa E32 915T20D……….. 47
Figure 2.55. Block Diagram of BME280 Sensor on MCU ATmega 328……….. 47
Figure 2.56. DIY Transmitter and Receiver LoRa E32 915 MHz Node Sensor……. 48
Figure 2.57. LoRa E32 915T20D and dimension………..………. 48
Figure 2.58. Circuit Diagram of LoRa E32 915T20D……… 48
Figure 2.59. Transmitter and receiver LoRa Dragino 915 MHz……….…… 50
Figure 2.60. Arduino Uno and BME/P 280 Sensor connectivity……… 51
Figure 2.61. Arduino MEGA and BME/P 280 Sensor connectivity……… 51
Figure 2.62. Realtime Receive data process on the field……….. 51
Figure 2.63. ES920LR Pins……… 53
Figure 2.64. Wiring of Sensor node Atmega 328 promini ES920LR LoRa based…. 54 Figure 2.65. LoRa Setting on Arduino UNO to Transmitter Test………..………… 54
Figure 2.66. Communication test ES920LR with MCU Arduino UNO……… 55
Figure 2.67. Transmitter and receiver LoRa ES920LR use MCU Pro mini……….. 55
Figure 2.68. SMA Male Antenna Port ES920LR LoRa………….……… 58
Figure 2.69. U-FL SMA antenna type……….… 58
Figure 2.70. ES920LR EASEL for end node and Gateway LoRa……….……… 58
Figure 2.71. ES920LR use a surface Mount technology (SMT) ……….…. 58
Figure 2.72. ES920LR use a surface Mount technology (SMT) pin…………..….…. 59
Figure 2.73. FTDI and ES920LR Connection………. 59
Figure 2.74. Pairing of ES920LR use FTDI XBee (first test) ……… 60
Figure 2.75. Pairing of ES920LR on Sensor node prototype with U.FL Antenna… 60 Figure 2.76. General Configuration Mode………. 61
Figure 2.77. ES920LR Coordinator Configuration………... 62
Figure 2.78. ES920LR End Device Configuration……… 62
Figure 2.79 Communication Between End Devices and Coordinator from keyboard Command……… 64
Figure 2.80. LoRa LG01 Module Block Diagram………. 64
Figure 2.81. Dragino LoRa LG01 ……….…….. 65
Figure 2.82. LG01 Gateway and End node Dragino LoRa interaction…….….…… 65
Figure 2.83. ES920LRGW……….……… 66
Figure 2.84. Dragino LoRa Gateway……….……….…… 67
Figure 2.85. Classes of LoRaWAN ……….……… 68
Figure 2.86. Energy Efficiency on Clustering of WSN …….….……… 69
Figure 2.87. LEACH Protocol……….… 71
Figure 2.88. HEED Protocol……… 72
Figure 2.89. PEGASIS Protocol……….. 72
Figure 2.90. LEACH-TLCH Protocol………. 73
Figure 2.91. Multi DAGs on DODAG……… 73
Figure 2.92. DAGs with a Division Router……… 74
Figure 2.93 IoT Desain with the clustering desain for energy efficiency…………. 75
Figure 2.94. Energy Dissipation WSN model………...… 77
Figure 2.95. IC of MSP430F247……… 78
Figure 2.96. Powertrace on WSN with 10 nodes……….. 78
Figure 2.97. Zigbee Transmitter at Free Space Propagation………...……… 83
Figure 2.98. Zigbee Receiver at Free Space Propagation……… 84
Figure 2.99. Receiver Sensitivity………. 87
Figure 2.100. Spread Spectrum Communications……… 88
Figure 2.101. Spreading Factor (SF) ……….. 91
Figure 2.102. Comparation Spreading Factor (SF) on 125 kHz Frequency……….. 91
Figure 2.103. Comparation Spreading Factor (SF) on 250 kHz Frequency……….. 92
Figure 2.104. Comparation Spreading Factor (SF) on 500 kHz Frequency……….. 92
Figure 2.105. RP-SMA Male type antenna……… 99
Figure 2.106 Conditions of Fresnel Zone theory……….……… 104
Figure 2.107. Fresnel Zone 70% clear………. 105
Figure 2.108. Comparation of Fresnel Zone Clear Percentage (%)……… 106
Figure 2.109 Comparation of 70% Clear (30% Blockage) Fresnel Zone with different LoRa Frequency (433, 868 and 915 MHz) ……… 107
Figure 2.110. Transmitting data on the Bad Wheater (rain) …………...……… 107
Figure 2.111. Receive and Reflected signal on the Bad Weather (rain) ……… 108
Figure 2.112. Receive and Reflected signal on the Bad Weather (snow) ……… 110
Figure 3.1. Design of Medical Monitoring Xbee and Raspberry based ……… 112
Figure 3.2. Design of Medical Monitoring (MH-ET live - SPO2) LoRa based….… 112 Figure 3.3 Simple TT Network Concept……… 113
Figure 3.4 Adaptive Data Rate (ADR) Algorithm TT Network Concept…………... 114
Figure 3.5 Flowchart on this research………. 115
Figure 3.6 Adaptive Data Rate (ADR) Flowchart………..…… 115
Figure 3.7 Flowchart Simulation of Protocol Algorithm………..…… 116
Figure 3.8 Architecture research about Radio Propagation ……… 118
Figure 3.9 Interrupt Process……….. 118
Figure 3.10 Battery 3.7 Volt 1000 mAh Lithium Polymer Li-Po Battery Rechargeable… ……… 120
Figure 3.11 Battery 3.7 Volt, 700 mAh Lithium Polymer Li-Po Battery Rechargeable Suitable for Drone ……… 120
Figure 3.12 Total Load measurement of Current (mA) on Rx dan Tx Sensor Node……… 121
Figure 3.13 Current of Tx sensor node 29.3 mA (no transmit or idle) and 56.4 mA (Transmit) ………. 122
Figure 3.14 Current Consumption of E32 and ES920LR LoRa……….… 122
Figure 3.15 Delay process with parameters I total, Iactive, and Iidle………... 124
Figure 3.16. Increase the Tdelay to get the small current Total average (mA) …... 124
Figure 3.17. Current Average from additional TIdle (s) ………. 126
Figure 3.18. Battery Life Comparison from reducing the Current (mA) ……..…… 127
Figure 3.19. EE ES920LR 293bps, SF12, BW 125 kHz……… 127
Figure 3.20. EE ES920LR 5469 bps, SF7, BW 125 kHz………... 128
Figure 3.21. Design of IoT use ES920LR LoRa ………. 129
Figure 3.22 Testing Phase (Step by step setting of ES920LR Communication) …… 129
Figure 3.23 Mesh Communication LoRa ES920LR (Design4 development) ……... 130
Figure 3.24. ATMEL M328P from MeRL Keyence Digital Microscope……….. 130
Figure 3.25. LoRa ES920LR and Leafony Board dimension……… 131
Figure 3.26. Dimension of Leafony Board AX02A……… 131
Figure 3.27. LeafBus Leafony Board……… 132
Figure 3.28. Pins of Leafony Board……….. 133
Figure 3.29. MeRL Kanazawa Univ. Desain to LoRa ES920LR Leafony……… 133
Figure 3.30. The Evolution of Sensor Node on this research……… 134
Figure 3.31 LoRa Drone on FSPL Test………..…… 135
Figure 3.32 Antenna Position on the Top of Building (~40 meter) ………. 135
Figure 3.33 ES920LR LoRa test area……….……… 136
Figure 3.34 ES920LR LoRa test on building and trees obstacles……… 136
Figure 4.1. Voltage and 1 Temperature sensor graph...138
Figure 4.2. Voltage and 2 Temperature sensor graph ...138
Figure 4.3. RSSI WPAN 802.15.1 (Bluetooth RN-42) Graph with n = 2 ...141
Figure 4.4. Comparison RSSI WPAN 802.15.1 (Bluetooth RN-42) with different environment ... 141
Figure 4.5 Phyton Code to sending the data temperature to MySQL data- base...142
Figure 4.6 Output data Temperature sensor in Data-base...143
Figure 4.7 ZigBee PathLoss (-dB) on Free Space Area ...143
Figure 4.8 Received Signal Strength of Zigbee (dBm) on Free Space Area...144
Figure 4.9 RSSI (dBm) of Zigbee RF module ...144
Figure 4.10 Power Consumption of 1 Router Node...145
Figure 4.11 Power Consumption of 2 Router Node...146
Figure 4.12 Average Power Consumption of 1 Router Node...146
Figure 4.13 Average Power Consumption of 2 Router Node...147
Figure 4.14 DAGs with a 4 Division Router Node...147
Figure 4.15 Average Power Consumption of 26 Nodes...148
Figure 4.16 Network Hops...148
Figure 4.17 Sensor node on LEACH Algorithm protocol...149
Figure 4.19 Sensor node on LEACH-TLCH Algorithm protocol...150
Figure 4.20 Sensor node on PEGASIS Algorithm protocol...150
Figure 4.21 The result of comparison of Algorithm type Protocols with the number of 20nodes ...151
Figure 4.22 The result of comparison of Algorithm type Protocols with the number of 50 nodes ...151
Figure 4.23 The result of comparison of Algorithm type Protocols with the number of 100 nodes ...152
Figure 4.24 The result of comparison of Algorithm type Protocols ...152
Figure 4.25 Periodic Consumption with 500 kHz, CR=1,2,3,4 and SF =7 to 12 ... 154
Figure 4.26 Periodic Consumption with 250 kHz, CR=1,2,3,4 and SF =7 to 12 ……. 154
Figure 4.27 Periodic Consumption with 125 kHz, CR=1,2,3,4 and SF =7 to 12 ...155
Figure 4.28 Realtime Data Pulse (BPM) at Web Page HTML JASON ...155
Figure 4.29 Blood pressure test consisting of a, b, c and d ...156
Figure 4.30 Graph of Systolic (mmHg) ...157
Figure 4.31 Graph of Diastolic (mmHg) ...157
Figure 4.32 Realtime Sensor data from end node ...158
Figure 4.33 Data Transmit on Serial Monitor with RSSI (-dBm) value ………...159
Figure 4.34 Data Receive on Serial Monitor with RSSI (-dBm) value ...159
Figure 4.35 LoRa Gateway, LoRa Tx (MH-ET) and LoRa Rx ...160
Figure 4.36 Output data From LoRa Tx No Finger...161
Figure 4.37 Output data From LoRa Tx with Finger detect-ed...161
Figure 4.39 Output SPO2 Sensor data (not Valid)...162
Figure 4.40 Output SPO2 Sensor data (Valid)...162
Figure 4.41 The Finger is not placed on the sensor, there is no detection of arterial blood ...163
Figure 4.42 The Finger is detected by the sensor precisely...163
Figure 4.43 using objects other than fingers,the Hearth rate data is not accurate….164 Figure 4.44 Pulse Sensor node LoRa 920MHz based...164
Figure 4.45 a, b and c, Application Server Thingspeak (MeRL Lab) ... 165
Figure 4.46 ToA, Bandwidth, and Spreading Factor (SF) Comparison ...166
Figure 4.47 Comparation of Bitrate (BW), SF and CR (4/CR+4) ...167
Figure 4.48 LoRa Receiver Sensitivity (dBm) NF=6 dB ...167
Figure 4.49 Comparation of Budget Link (dB), SF and CR (4/CR+4)...168
Figure 4.50 BW, SF and ToA Comparison of ESLR920………...168
Figure 4.51 Rb, SF and BW Comparison of ESLR920 ...169
Figure 4.52 LoRa 915 MHz Pathloss [dBm] of 2-Ray Ground Reflection model ...169
Figure 4.53 Power Receiver of LoRa module 915 MHz and 920 MHz………... 170
Figure 4.54 Power Receiver of LoRa (-dB) module with different Frequency...171
Figure 4.55 Comparation of FSPL with Materials Obstacle LoRa Propagation…...171
Figure 4.56 End Nodes and signal analyzer on the measurement process...172
Figure 4.57 RSSI measurement on signal analyzer (A)...173
Figure 4.58 RSSI and SNR output on the LoRa Rx...173
Figure 4.59 RSSI (-dBm) of RF96 LoRa on Indoor Obstacles...174
Figure 4.60 RSSI (-dBm) LoRa 915 MHz ...175
Figure 4.61 RSSI (-dBm) ES920LR Lora Comparison ...175
Figure 4.62 Data Transmission Area ...176
Figure 4.63 Obstacles in Transmission data ...176
Figure 4.64 RSSI realtime (-dBm) on Hill obstacle ...177
Figure 4.65 RSSI realtime (-dBm) on LoS Building ...177
Figure 4.66 RSSI (-dBm) ES920LR BW 125kHz 12 SF, 920.6MHz...178
Figure 4.67 Pulse Sensor node LoRa based ...179
Figure 4.68 PRR (%) with a time and distance approach ...180
Figure 4.69 LoRa sig-nal (-dBm) attenuation on rain ...181
Figure 4.70 SF and RSSI (dBm) Comparison ...181
Figure 4.71 Data transmitter and receiver ES920LR on Coolterm ...182
Figure 4.72 Data on the end node and the Gateway (ES920GW) ...183
Figure 4.73 LoRa Signal Analyze with SDR v.1.0.0.1700 RTL-SDR ...183
Figure 4.74 LoRa Signal Analyze with Textronix RSA 3408B Analyzer ……….184
Figure 4.75 Inphase and Quadrature (IQ) ...185
Figure 4.76 Transmit Power LoRa Signal (dB) ...185
Figure 4.77 (a) Start Chirp signal (preamble) (b) realtime chirp up-down chirp and encoded signal(c) Up and Down Chirp (d) Encoded message signal 186
Figure 4.78 LoRa signal use a Signal Analyzer ...187
Figure 4.79 LoRa signal use a Signal Analyzer with modulation signal ....187
Figure 4.80 LoRa Chirp signal ... 188
Figure 4.81 Signal LoRa 915 MHz with peak setting ... 188
Figure 4.82 LoRa 915 MHz signal with trace bitmap ...189
Figure 4.83 Signal LoRa with a normal setting ...189
Figure 4.84 Signal LoRa with intermediate surges ...190
Figure 4.85 Signal ES920LR on End node measurement with Textronix Signal Analyzer ...190
Figure 4.86 Signal ES920LR on End node on hand position, measurement with Textronix Signal Analyzer………... 191
Figure 4.87 Chirps LoRa... 191
Figure 4.88 Channel Power of LoRa 920.55 MHz Freq...192
Figure 4.89 Frequency Carrier of LoRa 920.592 MHz... 192
Figure 4.90 Channel Power on Signal Analyzer with Spectogram ...193
Figure 4.91 (a,b,c,d,e,f,g,h) LoRa Signal movement on Signal Analyzer ...194
List of Tables
Table 2.1. RFM95W Spesification……… 25
Table 2.2. Comparative of LPWAN technologies……….… 26
Table 2.3. The range, power and transmission speed of wireless technology….… 31 Table 2.4. Pulse sensor node spesification……….… 35
Table 2.5. ISM Frequency band regulation……… 42
Table 2.6. LORA E32915T20d spesification……… 49
Table 2.7. E32915T20D pins……… 50
Table 2.8. Connection pin on transmitter……… 56
Table 2.9. Connection pin on receiver………. 56
Table 2.10. Time on air es920lr LORA……….. 63
Table 2.11. Connection of es920lr with arduino ……… 63
Table 2.12. Spesification of es920lr LORA………... 63
Table 2.13. Time on air ES920GW LORA gateway spesification ……… 66
Table 2.14. ES920GW LORA gateway setting……… 67
Table 2.15. Variable of this simulation research……….……… 75
Table 2.16. Pathloss exponent value from different environment……… 81
Table 2.17. The general parameter of LORA………..… 86
Table 2.18. LORA spreading factor comparation with SNR limit……….…. 94
Table 2.19. Configuration of spreading factor, bandwidth and bitrate in japan….. 95
Table 2.20. Signal strength classification……… 99
Table 2.21. LORA antenna spesification………. 99
Table 2.22. Material and thickness and nilai pathloss……….. 100
Table 2.23. Result of fresnel zone approach with h consideration……… 104
Table 2.24. Parameter used for the estimation of the spesific attenuation due to rain……….. 108
Table 2.25. Rain intensity measurement scale……… 109
Table 2.26. Rain intensity, calculated rain attenuations and its precipitations…… 109
Table 2.27. Parameter used for the estimation of the spesific attenuation due to snow……… 110
Table 2.28. Rain intensity, calculated snow attenuations and its precipitations…… 110
Table 3.1. The impact of Additional TDelay (s) on Current (mA) ……… 125
Table 3.2. Leafony board and ES920LR pin connections……… 132
Table 4.1. Comparison of algorithm protocol types and several nodes……… 153
Table 4.2. Different types of material causes Signal Attenuation at 3 km………172
Chapter 1. Introductions
Health is one of the prominent factors, it should be maintained and monitored by everyone every day, the health is expensive when someone checks his health, they come to the nearest hospital or clinic to receive the health services. the Health services are divided into several types, including minor health services (pulse or heartbeat examination, SPO
2, and blood pressure), moderate health services (outpatients) and severe health services (patients must stay in the hospital). some conditions of the patient are not possible to come to the hospital, this is due to several factors including the condition of the patient himself (disability) or not having a close family member, or the factor of the distance between the place of residence with the clinic or hospital, or also possible factors the other.
furthermore, the main purpose of this dissertation is how to help these patients healthy in the category of mild examination i.e., pulse, blood pressure, SpO
2dynamically and flexibly, so that they can check their health without having to come to the hospital or clinic (Ala Al-Fuqaha, et al.(2015)). in this case, if the condition of outpatients is not possible to come to the hospital or clinic as in the conditions mentioned earlier and the medical party (doctor) or the hospital requires pulse data and blood pressure as an indication of the data for initial examination and examination continues, therefore the solution taken is to build a sensor node device or device used to transmit the pulse and blood pressure data.
the next goal in this research is to analyze the condition of the network that has been built if there are much sensor nodes or in this case, substantial patients are scattered in various places and send to several Internet Gateways so that it can be analyzed multi-node network types formed i.e. mesh networking, cluster tree networking or star networking.furthermore, a multi-node network was built using the Wireless Technology module as a medium for sending data between nodes or sending sensor data to the Gateway (Chun-hao liao, et al. (2017)).
In previous authorsʹ research, the authors examined the Performance Evaluation of Zigbee based on monitoring the patientʹs Pulse status. but this research is limited to the radio frequency device used. Zigbee or can be called XBee or IEEE 802.15.4 has several types of XBee S1 is a type of radiofrequency that has the ability of up to 120 meters with the type of point to point communication and star communication. XBee S2 is a type of Radio Frequency that can transmit sensor data as far as 120 meters with the type of communication Mesh, Tree, Star and point to point.
furthermore, The XBee Pro can send data up to 1 Km in the condition of Free Space Path Loss (FSPL) without the obstruction of buildings or trees. in this case XBee is a device that is still limited by distance (Hristos T. Anastassiu, et al.(2014)), Tajudeen O.
Olasupo, et al. (2016), Xiaoqing Yu, et al.(2017). so in this dissertation, Radio
Frequency is used with remote capabilities and is supported by an Internet Gateway
that is capable of accommodating multi-sensor data. Lora (Long Range) Radio
Frequency is a type of radio that works on certain frequencies, i.e., 433 MHz, 868 MHz,
915 MHz, and 923 MHz. this frequency is different if it is used by different parts of the world, the frequency usage regulation is called the Industrial, Scientific, and Medical (ISM) band and this has been protected by the Telecommunications Law if violated it will be subject to sanctions by the user. e.g., a LoRa 902.6 MHz network researcher is in Japan, then, the researcher must follow the ISM Band which is permitted in Japan at a Frequency Range between 915.9 - 929.7 MHz.
1.1 . Background Overview
The initial idea of this research is to help elderly patients, victims of natural disasters, and the general public, and medical staff (doctors) in the identification and examination of health conditions quickly, accurately, and realtime, by sending a data or information from body condition measurements human or realtime patient, at the same time, and from different points or places. This issue or theme was raised as a dissertation topic due to the difficulty of a person or patient in conducting a physical examination at a hospital or doctor, due to physical problems. And this system can be used for natural disaster emergency response to send pulse sensor data from natural disaster victims to find out the latest conditions. And also this system can be used by doctors or the hospital or among themselves or personally in checking, recording in a real-time day by day conditions in real-time body conditions e.g., SPO
2, Blood Pressure, or Pulse Sensor, mental disaster monitoring (Hayati,nur, et al. (2017)), Pa- tient’s Electrocardiogram (ECG) (Moh. Khalid Hasan, et al (2019)), Patient’s Physio- logical Signal (Ying Zhang, (2009)), Urinary tract infection (Philip A.Catherwood, et al.(2018)). Used 3 sensors i.e., ECG, Body temperature, and SPO
2sensors (Mohammad Shahidul Islam, et al. (2019)).
Therefore, in previous studies the concept used was triage, namely priority on the severity of human victims of natural disasters, i.e, Priority 1, Priority 2, and Prior- ity 3 (Figure 1.3). The priority of this level is based on the heart rate found i.e, Tachy- cardia, Bradycardia, or normal. From here the pulse is used to determine the speed of the human heartbeat.Besides the heartbeat or pulse parameters, the triage concept also uses SPO
2.
Not only is it used by the SAR (Search and Rescue) Team to search for victims of nat- ural disasters and to label every victim found and collected (Figure 1.1 and Figure 1.2), however it can also be used by a medical team or doctor to access the hospital. mild, considering the heartbeat here is the doctorʹs initial step in examining the patient be- fore examining a part or condition of the body or other.
When disasters occur, it is possible to damage infrastructure including telecommuni-
cations and electrical systems (Figure 1.2), therefore, that sensor nodes with low-
power, long-range in outreach, and low data rate systems are needed.
Figure 1.1. Disaster Condition
Figure 1.2 Disaster victims priorities based on severity, infrastructure damaged in-
clude telecommunication and electricity
Figure 1.3 Paper and Label Priority on Triage concept
Figure 1.4 implementation the triage consept on the WSN (Puput Dani Prasetyo Adi)
Triage is a fast and focused assessment concept in a way that allows the most efficient use of human resources, equipment, and facilities with the aim of se- lecting and classifying all patients who need help and setting priorities for han- dling.
This triage concept is used by the SAR (Search and Rescue) Team in finding victims of natural disasters, this is the beginning of the idea to change the paper mode to sensor nodes and implement Wireless Sensor Network technology us- ing ZigBee to quickly send heart rate data by prioritizing 3 condition, normal (60-100 bpm), mayor / bradycardia (<60 bpm) and minor (> 100bpm) / Tachy- cardia. (Figure 1.4).
Zigbee distance is limited (120 meters), therefore, it cannot reach patients within a distance >1 km so that sensor nodes are made with pulse sensors and LoRa technology
Furthermore, the data is processed and received by the Gateway and forwarded to the
Internet server and displayed on the Application Server. Figure 1. An example of
sending data from various points to the Gateway that has been selected by End node
through the pairing process using Personal Address Network Identifier (PAN-ID) and Destination ID. The process of transmitting this data will be followed by an analysis of the Power Consumption Sensor Node, Time on Air (ToA), and optimizing data Rate (Rb). Because it can be predicted that the packet or payload data sent from the end node to the Gateway will experience an error (%), and packet loss (bytes) occurs, if there is an obstacle, this happens in residential and building areas. This idea originated from research at the masterʹs degree, using ZigBee S1 and S2 to transmit Pulse sensor data, using star and mesh topology (The Aloÿs Augustin, et al.(2016)).
The results of the research are the process of sending experiencing Packet Loss (%) using 3 End nodes, detailed analysis of transmitting data, packet loss, delay, and throughput. So, in this case, a wide range is needed to build a Low Power Wide Area (LPWA) network architecture. This dissertation will be tested and analyzed on all Wireless Sensor Network (WSNs), e.g., Bluetooth, ZigBee, WiFi, and LoRa devices (Kazem Sohraby, et al. (2007)). From the specifications of these Radio Frequency (RF) devices, they have their respective advantages ranging from Data rate (R
b), Range, and Power Consumption.
Figure 1.5. Disability Patients and victims of natural disasters as End Nodes Figure 1.5 is an important object description for the WSN application for medical monitoring, e.g., disability patients and victims of natural disasters as End Nodes, moreover, Figure 1.6 will be developed in Figure 1.7, it is a communication between gateways that will send data acquisition to the Application server or the internet server. The analysis in this section is on Uplink (UL) (Jean Michel de Souza Sant’Ana, et al.(2020)), Sergio Barrachina-Muñoz, et al. (2017) and Downlink (DL) data from the Gateway to the Internet Server and Application Server. WSN for another application e.g, smart bin (Dimitris Ziouzios. (2019)),Smart City (Gopika Premsankar, et al. (2020)), agriculture (Rice Field) Zhenran Gao, et al.(2018), street light monitoring system (Rudy Susanto, et al. (2018)), weather station (Kristoffer Olsson and Sveinn Finnsson.
(2017)). Smart vehicles LoRa and UNIX based (José Santa, et al. 2019)).
HeartBeat data as end node
HeartBeat Data as end node
Figure 1.6. End Devices (ED) and Gateway (GW) position
Figure 1.7. Gateways (GWs) Communication with Internet and Application Server
1.2 Research Objectives
One of the objectives of this research is to conduct Internet-based medical monitoring of Things (IoT), by looking at several factors or parameters including Power Consumption (p), Data Rate (Rb), Time on Air (ToA). Furthermore, optimizing Data Rate (Rb), Long Life of sensor nodes or Power Consumption, and Time on Air (ToA) on Low Power Wide Area Networks (LPWA) or (LPWAN) on network architectures built to monitor a patient or human health general, the parameters monitored include heartbeat, SPO
2, and Blood Pressure (Moh. Khalid Hasan, et al (2019)). Figure 1.8 is a the network architecture on this research. There are 4 main components i.e., End node (Sensor node), Gateway, Network Server, and application server. accordingly, at the end node, the sensors used are Heart-beat or pulse sensors, blood pressure sensors, and SPO
2sensors, on this research deep explained on Pulse sensor node-based.
In this research, a sensor node will be built which functions to send pulse, SPO
2and blood pressure data to the Internet Gateway and then the data is sent back to i.e platforms or devices. Laptop computers with Internet connections, Tablets, iPad, smartphones, and other platforms. so the data will be easily received quickly, by the Medical Parties in this case the Hospital and the doctor or the patient itself. LoRa supports the main objective of flexibility in sending patient health monitoring data using this Internet of Things (IoT) based technology (Fan Wu, et al. (2019)).
Furthermore, LoRa has qualified specifications in terms of handling distance. the maximum distance of LoRa can reach 15 km in the condition of Free Space Path Loss (FSPL) and of course, this needs consideration from the Fresnel Zone calculation sector.
Accordingly, Fresnel Zone itself is the condition of the earthʹs arch shape which
will have an impact on reflection, diffraction, and scattering on the LoRa propagation
signal. in this case, the sensor data transmission experiment can be used using LoRa
Radio Frequency in Free Space Path Loss (FSPL) conditions and conditions with
Obstacle (Buildings, Trees), and Trial using Multi-node LoRa with LoRa Internet
Gateway (LoRaWAN) conditions (Dina Hussein, Dina M. Ibrahim. (2020)), (Dmitry
Bankov,et al. (2016)), (Dina M. Ibrahim, Dina Hussein. (2019), LoRaWAN (LoRa Wide
Area Network) is the Internet Access Control (MAC) Internet Protocol for Wide Area
Network. Figure 1.8 explains the functionality of LoRa on the IoT Network
Architecture (Aloÿs Augustin,et al.(2016)). End Nodes in this dissertation are Sensor
Node Devices which are equipped with Microcontroller, Indicator or Output, LoRa,
Sensor and integrated battery.
(a)
Network Architecture
(b)
(b) applicative system on this research
This sensor node is made as minimal power so that the Sensor Node is formed with the ability of Low Power Consumption as expected in this Research. while the development of the network system under study is multiple nodes or multi-node LoRa, so it is necessary to regulate the energy in the LoRa sensor node in the network topology. In the research on Quality of Service and Energy Consumption IEEE 802.15.4 using the ZigBee Tool, the conclusion was that the network topology was determining the power consumption of the sensor node. furthermore, the Gateway is compatible with LoRa and holds all data from the LoRa End Node using a database technique so that the sensor data on each node is stored on the LoRa Gateway.
furthermore, this sensor data can be analyzed when sending Uplink and Downlink
data, at the Uplink and Downlink process stage can be analyzed Percentage, time of Packet Data and concluded what percentage packet loss (%), packet data throughput and other parameters.
Figure 1.8.(a), (b), (c) Worldwide Pulse sensor LoRa and IoT Based
1.3 Contributions
This research contributes to the development of Low Power Devices Internet of Things (IoT) technology to support the achievement of a Low Power Wide Area (LPWA) network to address a large number of end-node or end-device devices, and its main purpose is to monitor human health or patient. Specifically, the contributions of this dissertation are as follows:
- Creating sensor nodes instead of the concept of triage to prioritize patients based on the severity seen from the number of heartbeats (bpm).
- Building a Wireless Sensor Network (WSN) network based on Low Power Wide Area Network (LPWA or LPWAN) that has a wide range, Small data rate, and Long life sensor node for medical monitoring.
- Developing knowledge in the field of Internet of Things (IoT) with end devices that are Low Power, light type, and robust, as well as Application Servers that are compatible with the latest and high novelty Radio Frequency (RF) devices.
- Analyzing Low Power Wide Area (LPWA) networks using Low Power i.e, Long Range (LoRa) Radio Frequency 915 MHz and 920 MHz Radio Frequency devices, and other Radio Frequency devices i.e, Bluetooth, and ZigBee or XBee for medical monitoring.
- In multi nodes, the development of using Adaptive Data Rate on LoRaWAN is
Air time (ToA) and energy consumption of sensor nodes
- Leafony Board (Trillion node) has not yet released LoRa Leafony until early 2020, as a development team, MeRL Kanazawa University created a special board and tested LoRa Leafony, the LoRa used are ES920LR and ES920GW with 920 MHz Frequency according to the Frequency band allowed in Japan
1.4 Dissertation Organization
In this research is divided into 5 chapters, the point is there are 2 important parts in this dissertation, i.e., signal Radio Frequency (RF) Analyzes, and analyze on the Gateway communication, internet and Application server, RF Signal analysis, among others, the performance of Radio Frequency (RF) using LPWA and LPWAN Bluetooth, ZigBee or XBee, and LoRa at 915 MHz and 920 MHz frequencies, analysis of Quality of Services ie, Signal Noise Ratio (SNR), Receive Signal Strength Indicator (RSSI), Bit Rate or Data Rate (bytes), and Time on Air (ToA) of several causative factors such as attenuation Signal (dB), Obstacles, Free Space Path Loss, Fresnel Zone, etc.
moreover, and the second part is the edge node performance when communicating with Gateway, i.e., Uplink and Downlink, and how it is analyzed on the Application Server as output. The chapter division is explained in the section below:
- Chapter 2; this section describes the performance of Bluetooth to build the Internet of Things (IoT) using Raspberry Pi 3 Model B as a Gateway, then ZigBee or XBee Performance in conducting medical monitoring using pulse sensors and Blood Pressure sensors, and analysis of the Quality of Service (QoS) of communication between sensor nodes, then analysis of LoRa (Long Range) performance using a Microsensor. This chapter also analyzes how to optimize the power consumption of end nodes or Low Power and Lossy Network on the Internet of Things (IoT) using protocol algorithm comparison from the Contiki cooja simulator. The point in this chapter is analyzed 3 devices of Radio Frequency (RF) i.e., Bluetooth, ZigBee or XBee, and LoRa.
- Chapter 3; This section describes the Block Diagram of strategies undertaken to optimize data rate or bit rate, Time on Air, and energy or Power Consumption of Sensor nodes, including the Adaptive Data Rate mechanism.
- Chapter 4; This section describes the entire graph, data results from measurements, design, transmitting data transmitter (Tx) to the receiver (Rx), Gateway analysis, Power Consumption, signal analysis using Textronix signal Analyzer, simulation results, etc from all Radio Frequency performance analysis.
- Chapter 5; This section describes the conclusions of the entire Radio Frequency
(RF) analysis, which are appropriate to be applied according to the Low Power
Wide Area (LPWA) criteria, and Future work for the achievement of better
research results.
Chapter 2. Fundamental Literature Review
To build an end node device requires a Microcontroller (MCU) that is compatible with the Radio Frequency (RF) device used. In this case, the MCU supports the programming created for the successful process of transmitting LoRa data from the Transmitter (Tx) to the Receiver (Rx). One of the powerful MCUs used from initial to final research is the ATmega 328p which is found in various types of Arduino variants. e.g., Arduino Uno, and Arduino pro mini. furthermore, the end node development results using ATmega m328p is the micro type ATmega used by the Leafony board, its board which is specifically designed for this dissertation is discussed.
Besides ATmega 328p, a powerful device is the Raspberry Pi, recently, the Raspberry Pi 4 is a new type. type, in this research, Raspberry Pi 3 Model B is used.
Raspberry Pi is a board that has multi-port multi-function, including USB for input (keyboard, mouse, USB Drive), HDMI for output or monitor, RJ45 Port or Local Area Network (LAN) port, Adapters, SD and Micro SD Ports, Micro USB for adapters, and ports that have different functions which are integrated into one board, Raspberry Pi uses the Python language to operate the programming used in this research.
2.1 Data Acquisition
Acquisition is the process of processing data samples taken from real data (pulse sensors or arterial pulses to the heart) that are read by a pulse sensor or transducer, then processed by the computer into digital data, e.g., data bits per minute (bpm), generally can be seen in the figure 2.1, figure 2.1 is a block diagram of data acquisition.
Figure 2.1. Digital Data Acquisition System
In this research using data acquisition process, analog signal data taken from Pulse sensor, processed by Analog to Digital (ADC) on Microcontroller Arduino or MCU ATmega 328p then transmitting (Tx) and Receiving (Rx) using Transceiver, furthermore, pulse sensor data is sent through Radio Frequency LoRa 915 MHz or LoRa 920 MHz through the transmitter node or end-node to the receiver node (Rx) or Gateway using the same LoRa frequency and pairing on PAN ID, Destination ID, Source ID.
2.2 Duty Cycle
The important thing in calculating signal or wave propagation is the duty cycle, on radio Frequency (RF) devices, e.g, LoRa, the duty cycle is the percentage of time during the transmitting data process. There are several parameters to represent the value of the duty cycle, in the calculation of Energy in LoRa known as Ecycle (mJ), and the time required for sending data at a certain time unit (ms) is Tcycle, therefore, it is important to understand the duty cycle, the parameters include frequency (f), Period (T), Pulse Width (Tw). Furthermore, to understand more deeply about duty cycles, consider the example in figure 2.2.
Figure 2.2. Duty Cycle 2.3 Time Cycle (Tcycle)
Tcycle is the time used for the LoRa Radio Frequency (RF) device parameter when sending data packages (bytes), the formula represented in equation 2.1.
Tcycle = 100 x
_(2.1)
2.4 Body Temperature Sensor
To detect temperature or body temperature, this dissertation uses LM35 Temperature sensor as in Figure 5 DFRobot, there is embedded in the board design, there is also a separate, furthermore, in Figure 2.3 and Figure 2.4, LM35 Temperature sensor consists of 3 pins, i.e., GND, Output Voltage and Supply Voltage, LM35 Temperature sensor uses Pin A0 or Analog pin Arduino or MCU, LM35 was tested using Bluetooth RN-42 and Raspberry Pi 3 Model B. Moreover, Figure 2.5 is a Schematic of LM35 Temperature sensor. (Manuel Ramos, (2017)).
Figure 2.3. LM35 Temperature sensor
Figure 2.4 . LM35 on the board (DFrobot.com)
Figure 2.5. Schematic of LM35 Temperature Sensor
Furthermore, besides LM35 and BME280, the sensor for temperature reading is the MLX90614 Temperature sensor (Figure 2.6), this sensor is a type of sensor for reading temperature using infra-Red. This sensor is also called the infrared thermometer because its function is to measure the temperature of the human body or healthcare. Some important information about the MLX90614 sensor specifications are as follows:
o Range for ambient temperature: -40 to 125 ˚C (-40 to 257 °F)
o Range for object temperature (non contact): -70 to 380 ˚C (-94 to 716 °F) o Resolution: 0.02 °C
o Accuracy: 0.5°C for (0-50 °C) both ambient and object
Figure 2.6. MLX90614 Temperature sensor (arduino.cc) 2.5 Pulse sensor
This pulse sensor is a sensor that is used to detect pulses found on the fingertips
or earlobe, the way it works is the LED or infrared LED reads the signal changes
obtained from the pulse, Figure 2.7 shows Enlarged from the Pulse sensor there are 2
parts i.e, the LED and Sensor Chip. Figure 10 is the position of the Pulse sensor at the
fingertips to get the right pulse from the arteries. Figure 2.8 and Figure 2.9 is the
position of the pulse sensor on the earlobe, this position is also the same as the pulse
at the fingertips, the unit of measurement of the pulse sensor is ʺbeat per minuteʺ or
bpm. The Pulse sensor circuit can be operated with 4 mA Current and 5v Voltage.
Figure 2.7. Pulse Sensor (Pulsesensor.com)
Figure 2.8. Pulse Sensor position on the finger (pulsesensor.com)
Figure 2.9. Pulse Sensor position on the earlobe (pulsesensor.com)
Figure 2.10 is the display of the Pulse sensor on the serial monitor plotting. In other features, the pulse sensor can be displayed using a visualizer called Processing Vis.
Moreover, the bpm value on the pulse sensor is calibrated so that it matches the
human pulse rate, accordingly, the human heart rate consists of 3 classifications based
on heart rate conditions (bpm), ie,> 100 bpm Tachycardia, 60-100 bpm normal, and <60
bpm, Bradycardia. These values will be entered into the C ++ program using the
Arduino IDE. Furthermore, in the C ++ Pulse sensor program with the Arduino IDE,
there are 2 parts namely the pulse sensor and the interrupt, as shown in the program
Figure 2.10. Pulse Sensor Visualizer (MeRL Kanazawa Univ.).
An illustration of the heartbeat condition is represented in Figure 2.11, Figure 2.12, and Figure 2.13. moreover, 3 The conditions described are normal heartbeat conditions (60-100 beats per minute), low heartbeat (<60 beats per minute) or Bradycardia, and too fast heartbeat conditions (> 100 beats per minute) or Tachycardia.
Figure 2.11. Normal Heartbeat (Mayo Foundation For Medical Education &
Research, 2020)
Figure 2.12. Tachycardia (Mayo Foundation For Medical Education & Research,2020)
Figure 2.13. Bradycardia (Mayo Foundation For Medical Education & Research,2020)
Figure 2.13 b. Heart rate measurement Method
Based on human age, Heart Beat (bpm) is classified as in the table 2.1a. (Neramitr Chirakanphaisarn, et.al (2018)).
Table 2.1a. Heart beat classification (bpm) based on age Age Span Heart rate (bpm)
Less than 1 month 120-160
1-12 month 80-140
12 month – 2 years 80-130
2-6 years 75-120 6-12 years 75-110 More than 12 years 60 – 100
The case study in this dissertation is devoted to adult and elderly patients (More than 12 years), as in research purposes, so that the C++ language program used in sensor nodes only reads 60-100 bpm as a normal heartbeat, <60 bpm (BradyCardia), and> 60 bpm (TachyCardia).
2.6 MH-ET Live Sensor
To detect SPO
2or Pulse Oximetry and heart rate in this dissertation using the MH-ET Live MAX30102 sensor, this sensor has dimensions x = 11,964 mm, and y = 10.16 mm, SPO
2Wireless data are analyzed in the form of SPO
2sensors which are in Wrist-worn. Figure 2.14 is the display form of the MH-ET Live MAX 30102 sensor using Keyence VHX Digital Microscope. This sensor consists of Infrared LEDs, Photoelectric Detectors, optical devices, and Low-noise electronic circuits with ambient light suppression.
Figure 2.14. Pulse Oximetry and Heart-Rate Sensor pins (MeRL Kanazawa Univ.)
The Principle method or description of MH-ET Live sensor
Photodissolation method: The measurement of pulse and blood oxygen saturation is performed by using human tissue to cause different light transmittance when the blood vessel beats;
Light source: a specific wavelength of light-emitting diode selective for oxyhemoglobin ( HbO
!) and hemoglobin ( Hb ) in arterial blood;
Light transmittance is converted into an electrical signal: the change in the
volume of the arterial pulsation causes the light transmittance of the light to
change. At this time the light reflected by the human tissues is received by the photoelectric transducer, converted into an electrical signal, and amplified and output.
Oxygen absorption rate oxygen saturation (SaO
2), formulated in equation 2.2.
The normal SaO
2value is 95-100%.
SaO
!=
$$%&'(%&'()$%
