Integrated Multimedia Wireless Sensor Node for Comprehensive Lifelogging and Beyond: Design, Development and Applications

Abstract

This paper introduces the Integrated Multimedia Wireless Sensor Node (IMWSN), a significant advancement in environmental monitoring and lifelogging within Multimedia Wireless Sensor Networks (MWSN). MWSNs, equipped with wearable sensors, are crucial for documenting personal life experiences. However, current MWSNs often lack the ability to fully integrate data across various sensor types, including environmental, visual, and medical sensors. The IMWSN addresses this gap by providing a comprehensive view of an individual's interactions and environment. The IMWSN is composed of multiple modules: a processor module that manages the overall system efficiently, a visual module designed to capture video footage of the surroundings, an environmental module that allows for real-time monitoring of environmental conditions, and a medical module dedicated to recording health-related data of individuals, a process often known as lifelogging. These components are encased in a custom-designed 3D-printed enclosure and powered by a durable 4500mAh mobile battery. System programming and monitoring are facilitated through the user-friendly Arduino IDE, making the experience accessible and customizable. Beyond its primary function in lifelogging, the IMWSN is remarkably versatile and suited for a range of applications. It can function as an action camera, assist in forest fire monitoring, support ambient assisted living environments, and monitor patients' health and daily activities rigorously. This adaptability makes the IMWSN a valuable and essential tool in fields that require extensive data collection and sophisticated analytical capabilities, highlighting its broad potential impact.

Keywords

• Integrated MWSN
• Environmental Monitoring
• Lifelogging
• Customizable Experiences

References

1. M. S. Obaidat and S. Misra, Principles of Wireless Sensor Networks, 1st ed. Cambridge University Press, 2014. doi: 10.1017/CBO9781139030960.
2. M. Majid et al., “Applications of Wireless Sensor Networks and Internet of Things Frameworks in the Industry Revolution 4.0: A Systematic Literature Review,” Sensors, vol. 22, no. 6, Art. no. 6, Jan. 2022, doi: 10.3390/s22062087.
3. M. Mohankumar, Kirthana, P. Banu, M. Shree, and M. Mylsamy, “MULTI-PARAMETER SMART HEALTH MONITORING SYSTEM USING ARDUINO-UNO,” pp. 2582–5208, Feb. 2022.
4. Y. Yang, H. Wang, R. Jiang, X. Guo, J. Cheng, and Y. Chen, “A Review of IoT-Enabled Mobile Healthcare: Technologies, Challenges, and Future Trends,” IEEE Internet of Things Journal, vol. 9, no. 12, pp. 9478–9502, Jun. 2022, doi: 10.1109/JIOT.2022.3144400.
5. M. Sarkar, T.-H. Lee, and P. K. Sahoo, “Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence,” Electronics, vol. 13, no. 12, Art. no. 12, Jan. 2024, doi: 10.3390/electronics13122309.
6. T. Jabeen et al., “Smart Wireless Sensor Technology for Healthcare Monitoring System Using Cognitive Radio Networks,” Sensors, vol. 23, no. 13, Art. no. 13, Jan. 2023, doi: 10.3390/s23136104.
7. J. A. Castro Correa, S. B. Sepúlveda Mora, B. Medina Delgado, C. D. Escobar Amado, and D. Guevara Ibarra, “A forest fire monitoring and detection system based on wireless sensor networks,” Sci. tech, vol. 27, no. 2, pp. 89–96, Jun. 2022, doi: 10.22517/23447214.24784.
8. U. Dampage, L. Bandaranayake, R. Wanasinghe, K. Kottahachchi, and B. Jayasanka, “Forest fire detection system using wireless sensor networks and machine learning,” Sci Rep, vol. 12, no. 1, Art. no. 1, Jan. 2022, doi: 10.1038/s41598-021-03882-9.

9. F. M. Talaat and H. ZainEldin, “An improved fire detection approach based on YOLO-v8 for smart cities,” Neural Comput & Applic, vol. 35, no. 28, pp. 20939–20954, Oct. 2023, doi: 10.1007/s00521-023-08809-1.
10. V. Noel Jeygar Robert, P. Ragupathy, K. Chandraprabha, A. S. Nandhini, and M. Gnanasekaran, “Multi-Parameter Smart Health Monitoring System using Internet of Things,” in 2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS), Feb. 2022, pp. 1326–1334. doi: 10.1109/ICAIS53314.2022.9742828.
11. S. Mohapatra and P. M. Khilar, “Forest fire monitoring and detection of faulty nodes using wireless sensor network,” in 2016 IEEE Region 10 Conference (TENCON), Nov. 2016, pp. 3232–3236. doi: 10.1109/TENCON.2016.7848647.
12. M. Srbinovska, C. Gavrovski, V. Dimcev, A. Krkoleva, and V. Borozan, “Environmental parameters monitoring in precision agriculture using wireless sensor networks,” Journal of Cleaner Production, vol. 88, pp. 297–307, Feb. 2015, doi: 10.1016/j.jclepro.2014.04.036.
13. I. T. Almalkawi, M. Guerrero Zapata, J. N. Al-Karaki, and J. Morillo-Pozo, “Wireless Multimedia Sensor Networks: Current Trends and Future Directions,” Sensors, vol. 10, no. 7, Art. no. 7, Jul. 2010, doi: 10.3390/s100706662.
14. S. R. Jino Ramson and D. J. Moni, “Applications of wireless sensor networks — A survey,” in 2017 International Conference on Innovations in Electrical, Electronics, Instrumentation and Media Technology (ICEEIMT), Feb. 2017, pp. 325–329. doi: 10.1109/ICIEEIMT.2017.8116858.
15. D. Kandris, C. Nakas, D. Vomvas, and G. Koulouras, “Applications of Wireless Sensor Networks: An Up-to-Date Survey,” Applied System Innovation, vol. 3, no. 1, Art. no. 1, Mar. 2020, doi: 10.3390/asi3010014.
16. C. C. Y. Poon, B. P. L. Lo, M. R. Yuce, A. Alomainy, and Y. Hao, “Body Sensor Networks: In the Era of Big Data and Beyond,” IEEE Reviews in Biomedical Engineering, vol. 8, pp. 4–16, 2015, doi: 10.1109/RBME.2015.2427254.
17. V.-T. Ninh, “Stress Detection in Lifelog Data for Improved Personalized Lifelog Retrieval System”.
18. A. Bruun and M. L. Stentoft, “Lifelogging in the Wild: Participant Experiences of Using Lifelogging as a Research Tool,” in Human-Computer Interaction – INTERACT 2019, D. Lamas, F. Loizides, L. Nacke, H. Petrie, M. Winckler, and P. Zaphiris, Eds., in Lecture Notes in Computer Science. Cham: Springer International Publishing, 2019, pp. 431–451. doi: 10.1007/978-3-030-29387-1_24.
19. Y. Cho et al., “Platform design for lifelog-based smart lighting control,” Building and Environment, vol. 185, p. 107267, Nov. 2020, doi: 10.1016/j.buildenv.2020.107267.
20. S. Sathyanarayana, R. K. Satzoda, S. Sathyanarayana, and S. Thambipillai, “Vision-based patient monitoring: a comprehensive review of algorithms and technologies,” J Ambient Intell Human Comput, vol. 9, no. 2, pp. 225–251, Apr. 2018, doi: 10.1007/s12652-015-0328-1.
21. D. Tang, Y. Yoshihara, T. Takeda, J. Botzheim, and N. Kubota, “Informationally Structured Space for Life Log Monitoring in Elderly Care,” in 2015 IEEE International Conference on Systems, Man, and Cybernetics, Oct. 2015, pp. 1421–1426. doi: 10.1109/SMC.2015.252.
22. N. N. Malik, W. Alosaimi, M. I. Uddin, B. Alouffi, and H. Alyami, “Wireless Sensor Network Applications in Healthcare and Precision Agriculture,” Journal of Healthcare Engineering, vol. 2020, p. e8836613, Nov. 2020, doi: 10.1155/2020/8836613.
23. P. Rajak, A. Ganguly, S. Adhikary, and S. Bhattacharya, “Internet of Things and smart sensors in agriculture: Scopes and challenges,” Journal of Agriculture and Food Research, vol. 14, p. 100776, Dec. 2023, doi: 10.1016/j.jafr.2023.100776.
24. “ABX00042-ABX00045-ABX00046-datasheet.pdf.” Accessed: Mar. 11, 2024. [Online]. Available: https://docs.arduino.cc/resources/datasheets/ABX00042-ABX00045-ABX00046-datasheet.pdf
25. “Portenta Vision Shield | Arduino Documentation.” Accessed: Mar. 11, 2024. [Online]. Available: https://docs.arduino.cc/hardware/portenta-vision-shield/
26. “Arduino MKR ENV Shield rev2,” Arduino Online Shop. Accessed: Jun. 10, 2023. [Online]. Available: https://store-usa.arduino.cc/products/arduino-mkr-env-shield-rev2
27. “Gravity: PPG Heart Rate Monitor Sensor for Arduino (Analog/Digital).” Accessed: Nov. 13, 2024. [Online]. Available: https://www.dfrobot.com/product-1540.html
28. “DS3231 RTC (Real Time Clock) Interfacing with Arduino to build DIY Digital Clock.” Accessed: Aug. 19, 2023. [Online]. Available: https://circuitdigest.com/microcontroller-projects/interfacing-ds3231-rtc-with-arduino-and-diy-digital-clock
29. “Getting Started with Arduino IDE 2 | Arduino Documentation.” Accessed: Mar. 11, 2024. [Online]. Available: https://docs.arduino.cc/software/ide-v2/tutorials/getting-started-ide-v2/
30. G. Cicirelli, R. Marani, A. Petitti, A. Milella, and T. D’Orazio, “Ambient Assisted Living: A Review of Technologies, Methodologies and Future Perspectives for Healthy Aging of Population,” Sensors, vol. 21, no. 10, Art. no. 10, Jan. 2021, doi: 10.3390/s21103549.
31. G. Marques and R. Pitarma, “Promoting Health and Well-Being Using Wearable and Smartphone Technologies for Ambient Assisted Living Through Internet of Things,” in Big Data and Networks Technologies, Y. Farhaoui, Ed., Cham: Springer International Publishing, 2020, pp. 12–22. doi: 10.1007/978-3-030-23672-4_2.
32. R. Maskeliūnas, R. Damaševičius, and S. Segal, “A Review of Internet of Things Technologies for Ambient Assisted Living Environments,” Future Internet, vol. 11, no. 12, Art. no. 12, Dec. 2019, doi: 10.3390/fi11120259.
33. G. Menculini et al., “Insights into the Effect of Light Pollution on Mental Health: Focus on Affective Disorders—A Narrative Review,” Brain Sciences, vol. 14, no. 8, Art. no. 8, Aug. 2024, doi: 10.3390/brainsci14080802.
34. N. U. Huda, I. Ahmed, M. Adnan, M. Ali, and F. Naeem, “Experts and intelligent systems for smart homes’ Transformation to Sustainable Smart Cities: A comprehensive review,” Expert Systems with Applications, vol. 238, p. 122380, Mar. 2024, doi: 10.1016/j.eswa.2023.122380.
35. K. Bhui et al., “Air quality and mental health: evidence, challenges and future directions,” BJPsych Open, vol. 9, no. 4, p. e120, Jul. 2023, doi: 10.1192/bjo.2023.507.
36. G. Facchinetti, G. Petrucci, B. Albanesi, M. G. D. Marinis, and M. Piredda, “Can Smart Home Technologies Help Older Adults Manage Their Chronic Condition? A Systematic Literature Review,” International Journal of Environmental Research and Public Health, vol. 20, no. 2, p. 1205, Jan. 2023, doi: 10.3390/ijerph20021205.
37. J. Radua, M. De Prisco, V. Oliva, G. Fico, E. Vieta, and P. Fusar-Poli, “Impact of air pollution and climate change on mental health outcomes: an umbrella review of global evidence,” World Psychiatry, vol. 23, pp. 244–256, May 2024, doi: 10.1002/wps.21219.
38. A. A. Abba Ari et al., “Data collection in IoT networks: Architecture, solutions, protocols and challenges,” IET Wireless Sensor Systems, vol. 14, no. 4, pp. 85–110, Aug. 2024, doi: 10.1049/wss2.12080.
39. M. Mohammed, M. N. Mahmud, M. F. Mohd Salleh, and A. Alnoor, “Wireless sensor network security: A recent review based on state-of-the-art works,” International Journal of Engineering Business Management, vol. 15, Feb. 2023, doi: 10.1177/18479790231157220.