Breast Lump Assessment: An IoT-Integrated Framework with Advanced Localization Techniques
DOI:
https://doi.org/10.56294/dm2025849Keywords:
Breast Lumps, Internet of Things, Lump, Sensor, Wearable JacketAbstract
Internet of Things (IoT) influences many areas such as healthcare, transportation, agriculture, industry control, environment monitoring, and water management. Healthcare is a major area in which the IoT enables a more personalized form of healthcare through smart healthcare systems. Breast cancer is the second leading cause of death among women globally, and its incidence is increasing every year. Early-stage detection of breast cancer is an important research challenge in the medical field. The aim of this article is to design an IoT - Integrated framework with advanced localization techniques for breast lump assessment. Through the proposed framework, breast lumps are monitored periodically using sensor embedded wearable jacket. The lump position and its depth in the breast are evaluated using localization techniques in sensor organization. Model outcome is analysed for six periodic tests data. Results evidence that periodic monitoring of breast health using the designed framework is effective to fix abnormal lumps at the early stage.
References
1. Alemdar, H., & Ersoy, C. (2010). Wireless sensor networks for healthcare: A survey. Computer networks, 54(15), 2688-2710.
2. Mukhopadhyay, S. C. (2014). Wearable sensors for human activity monitoring: A review. IEEE sensors journal, 15(3), 1321-1330.
3. Mishra, S. S., & Rasool, A. (2019, April). IoT Health care Monitoring and Tracking: A Survey. In 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI) (pp. 1052-1057). IEEE.
4. Sarvazyan, A. (1998). Mechanical imaging:: A new technology for medical diagnostics. International journal of medical informatics, 49(2), 195-216.
5. Monta, S., Promwong, S., & Kingsakda, V. (2016, June). Evaluation of ultra wideband indoor localization with trilateration and min-max techniques. In 2016 13th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) (pp. 1-4). IEEE.
6. Al-Ammar, M. A., Alhadhrami, S., Al-Salman, A., Alarifi, A., Al-Khalifa, H. S., Alnafessah, A., & Alsaleh, M. (2014, October). Comparative survey of indoor positioning technologies, techniques, and algorithms. In 2014 International Conference on Cyberworlds (pp. 245-252). IEEE.
7. Gu, Y., Lo, A., & Niemegeers, I. (2009). A survey of indoor positioning systems for wireless personal networks. IEEE Communications surveys & tutorials, 11(1), 13-32.
8. Boontrai, D., Jingwangsa, T., & Cherntanomwong, P. (2009, September). Indoor localization technique using passive RFID tags. In 2009 9th International Symposium on Communications and Information Technology (pp. 922-926). IEEE.
9. Valverde, L., de Lera, E., & Fernàndez, C. (2010, February). Inferencing emotions through the triangulation of pupil size data, facial heuristics and self-assessment techniques. In 2010 Second International Conference on Mobile, Hybrid, and On-Line Learning (pp. 147-150). IEEE.
10. Hyder, R., Kamel, N., & Tang, T. B. (2014, December). Brain source localization techniques: Evaluation study using simulated EEG data. In 2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES) (pp. 942-947). IEEE.
11. Balaji, M., & Chaudhry, S. A. (2018, February). A cooperative trilateration technique for object localization. In 2018 20th International Conference on Advanced Communication Technology (ICACT) (pp. 758-763). IEEE.
12. Paolini, G., Masotti, D., Antoniazzi, F., Cinotti, T. S., & Costanzo, A. (2019). Fall Detection and 3-D Indoor Localization by a Custom RFID Reader Embedded in a Smart e-Health Platform. IEEE Transactions on Microwave Theory and Techniques, 67(12), 5329-5339.
13. Liu, H., Darabi, H., Banerjee, P., & Liu, J. (2007). Survey of wireless indoor positioning techniques and systems. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 37(6), 1067-1080.
14. Chalvatzaras, Athanasios, Ioannis Pratikakis, and Angelos A. Amanatiadis. "A survey on map-based localization techniques for autonomous vehicles." IEEE Transactions on Intelligent Vehicles 8.2 (2022): 1574-1596.
15. Alam, M. S., Mohamed, F. B., Selamat, A., & Hossain, A. B. (2023). A review of recurrent neural network based camera localization for indoor environments. IEEE Access.
16. Li, Z., Wang, P., Tian, Z., & Liu, K. (2022). TriLoc: Toward Accurate Indoor Localization With Assistance of Microwave Reflections. IEEE Transactions on Microwave Theory and Techniques.
17. Zou, Y., Wu, L., Fan, J., & Liu, H. (2023). A convergent iteration method for 3-D AOA localization. IEEE Transactions on Vehicular Technology.
18. Marquez, L. E., & Calle, M. (2023). Understanding LoRa-based Localization: Foundations and Challenges. IEEE Internet of Things Journal.
19. Ren, Y., Liu, B., Cheng, R., & Agia, C. (2021). Lightweight semantic-aided localization with spinning LiDAR sensor. IEEE Transactions on Intelligent Vehicles.
20. Panwar, K., Fatima, G., & Babu, P. (2022). Optimal sensor placement for hybrid source localization using fused TOA-RSS-AOA measurements. IEEE Transactions on Aerospace and Electronic Systems.
21. Zaidi, Monji M., Ahmed Abdullah Asiri, and Imen R. Bouazzi. "Simple Digital Design to Optimize TDOA Algorithm Reducing Energy Consumption: WSN for Forest Fire Localization." 2024 5th International Conference on Mobile Computing and Sustainable Informatics (ICMCSI). IEEE, 2024.
22. Alnakkash, A. H., Kurji, A. S., Mahdi, S. Q., & Fanoos, Z. Q. (2024, January). A pargmatic Implementation of Outdoor Localization Scheme for Real Time IoT Applications. In 2024 Fourth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) (pp. 1-5). IEEE.
23. Abba, A. M., Sanusi, J., Oshiga, O., & Mikail, S. A. (2023, November). A Review of Localization Techniques in Wireless Sensor Networks. In 2023 2nd International Conference on Multidisciplinary Engineering and Applied Science (ICMEAS) (pp. 1-5). IEEE.
24. Attal, F., Mohammed, S., Dedabrishvili, M., Chamroukhi, F., Oukhellou, L., & Amirat, Y. (2015). Physical human activity recognition using wearable sensors. Sensors, 15(12), 31314-31338.
25. Akay, M. F. (2009). Support vector machines combined with feature selection for breast cancer diagnosis. Expert systems with applications, 36(2), 3240-3247.
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Copyright (c) 2025 M Kavitha, Singaraju Srinivasulu, P S Latha Kalyampudi, N. Sunanda, M Kalyani, D Mythrayee (Author)

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