IoT-Enabled Accident Response System with Hazard Detection and Vital Sign Monitoring, Vehicle to Vehicle (V2V) Communication
DOI:
https://doi.org/10.15662/IJEETR.2026.0802217Keywords:
Internet of Things, Intelligent Transportation Systems (ITS), Vehicle-to-Vehicle (V2V) Communication, Hazard Monitoring systems., Real-Time Accident DetectionAbstract
Road accidents remain a significant cause of fatalities, primarily due to delayed detection and lack of immediate emergency response. This paper presents an IoT-enabled accident response system that integrates hazard detection and vital sign monitoring using Vehicle-to-Vehicle (V2V) communication. The proposed system utilizes multiple sensors, including an accelerometer, gas sensor, temperature sensor, and heartbeat sensor, to continuously monitor vehicle conditions and driver health. A microcontroller processes real-time sensor data using predefined threshold-based logic to detect accidents and hazardous situations. Upon detection, the system retrieves location information through a GPS module and transmits alerts containing accident details, hazard status, and driver health data via IoT platforms to emergency contacts. Additionally, V2V communication enables real-time warning dissemination to nearby vehicles, reducing the risk of secondary collisions. The system is designed to minimize false alarms through multicondition validation and ensure fast response time. Experimental results demonstrate reliable detection performance and efficient communication. The proposed solution enhances road safety and contributes to the development of intelligent transportation systems
References
1. L. Liu, J. Zhang, Y. Qu, S. Zhang, and W. Xiao, ‘‘MmRH: Noncontact vital sign detection with an FMCW mmwave radar,’’ IEEE Sensors J., vol. 23, no. 8, pp. 8856–8866, Apr. 2023.
2. M. Weenk, M. Koeneman, T. H. van de Belt, L. J. L. P. G. Engelen, H. van Goor, and S. J. H. Bredie, ‘‘Wireless and continuous monitoring of vital signs in patients at the general ward,’’ Resuscitation, vol. 136, pp. 47–53, Mar. 2019.
3. M. Ali, A. Elsayed, A. Mendez, Y. Savaria, and M. Sawan, ‘‘Contact and remote breathing rate monitoring techniques: A review,’’ IEEE Sensors J., vol. 21, no. 13, pp. 14569–14586, Jul. 2021.
4. T.Hao,C.Bi,G.Xing,R. Chan,andL.Tu,‘‘MindfulWat ch:Asmartwatchbased system for real-time respiration monitoring during meditation,’’ Proc. ACM Interact., Mobile, Wearable Ubiquitous Technol., vol. 1, no. 3, pp. 1–19, Sep. 2017.
5. C.Nagarajan and M.Madheswaran - ‘Stability Analysis of Series Parallel Resonant Converter with Fuzzy Logic Controller Using State Space Techniques’- Taylor &Francis, Electric Power Components and Systems, Vol.39 (8), pp.780-793, May 2011. DOI: 10.1080/15325008.2010.541746
6. C.Nagarajan and M.Madheswaran - ‘Experimental verification and stability state space analysis of CLL-T Series Parallel Resonant Converter’ - Journal of Electrical Engineering, Vol.63 (6), pp.365-372, Dec.2012. DOI: 10.2478/v10187-012-0054-2
7. C.Nagarajan and M.Madheswaran - ‘Performance Analysis of LCL-T Resonant Converter with Fuzzy/PID Using State Space Analysis’- Springer, Electrical Engineering, Vol.93 (3), pp.167-178, September 2011. DOI 10.1007/s00202-011-0203-9
8. S.Tamilselvi, R.Prakash, C.Nagarajan,“Solar System Integrated Smart Grid Utilizing Hybrid Coot-Genetic Algorithm Optimized ANN Controller” Iranian Journal Of Science And Technology-Transactions Of Electrical Engineering, DOI10.1007/s40998-025-00917-z,2025
9. S.Tamilselvi, R.Prakash, C.Nagarajan,“ Adaptive sliding mode control of multilevel grid-connected inverters using reinforcement learning for enhanced LVRT performance” Electric Power Systems Research 253 (2026) 112428, doi.org/10.1016/j.epsr.2025.112428
10. S.Thirunavukkarasu, C. Nagarajan, 2024, “Performance Investigation on OCF and SCF study in BLDC machine using FTANN Controller," Journal of Electrical Engineering And Technology, Volume 20, pages 2675–2688, (2025), doi.org/10.1007/s42835-024-02126-w
11. C. Nagarajan, M.Madheswaran and D.Ramasubramanian- ‘Development of DSP based Robust Control Method for General Resonant Converter Topologies using Transfer Function Model’- Acta Electrotechnica et Informatica Journal , Vol.13 (2), pp.18-31,April-June.2013, DOI: 10.2478/aeei-2013-0025.
12. C.Nagarajan and M.Madheswaran - ‘DSP Based Fuzzy Controller for Series Parallel Resonant converter’- Springer, Frontiers of Electrical and Electronic Engineering, Vol. 7(4), pp. 438-446, Dec.12. DOI 10.1007/s11460-012-0212-0.
13. C.Nagarajan and M.Madheswaran - ‘Experimental Study and steady state stability analysis of CLL-T Series Parallel Resonant Converter with Fuzzy controller using State Space Analysis’- Iranian Journal of Electrical & Electronic Engineering, Vol.8 (3), pp.259-267, September 2012.
14. C.Nagarajan and M.Madheswaran, “Analysis and Simulation of LCL Series Resonant Full Bridge Converter Using PWM Technique with Load Independent Operation” has been presented in ICTES’08, a IEEE / IET International Conference organized by M.G.R.University, Chennai.Vol.no.1, pp.190-195, Dec.2007
15. Suganthi Mullainathan, Ramesh Natarajan, “An SPSS and CNN modelling based quality assessment using ceramic materials and membrane filtration techniques”, Revista Materia (Rio J.) Vol. 30, 2025, DOI: https://doi.org/10.1590/1517-7076-RMAT-2024-0721
16. M Suganthi, N Ramesh, “Treatment of water using natural zeolite as membrane filter”, Journal of Environmental Protection and Ecology, Volume 23, Issue 2, pp: 520-530,2022
17. NeuLog. (2017). Respiration Monitor Belt Logger Sensor Nul-236.
18. L. Chioukh, H. Boutayeb, D. Deslandes, and K. Wu, ‘‘Noise and sensitivity of harmonic radar architecture for remote sensing and detection of vital signs,’’ IEEE Trans. Microw. Theory Techn., vol. 62, no. 9, pp. 1847–1855, Sep. 2014.
19. L. Qiao, E. Dong, H. Yin, H. Li, and J. Yang, ‘‘Intelligent network device identification based on active TCP/IP stack probing,’’ IEEE Netw., early access, 2024.
20. J. Wu, H. Lu, Y. Xiang, F. Wang, and H. Li, ‘‘SATMAC: Self-adaptive TDMA-based MAC protocol for VANETs,’’ IEEE Trans. Intell. Transp. Syst., vol. 23, no. 11, pp. 21712–21728, Nov. 2022.
21. J. Wu, X. Chen, L. Zhang, S. Zhou, and D. Wang, ‘‘A multi-secret reputation adjustment method in the secret sharing for Internet of Vehicles,’’ Secur. Commun. Netw., vol. 2022, pp. 1–13, May 2022.
22. R. Song, S. Zhang, C. Li, Y. Zhang, J. Cheng, and X. Chen, ‘‘Heart rate estimation from facial videos using a spatiotemporal representation with convolutional neural networks,’’ IEEE Trans. Instrum. Meas., vol. 69, no. 10, pp. 7411–7421, Oct. 2020.
23. A. M. Rodríguez and J. Ramos-Castro, ‘‘Video pulse rate variability analysis in stationary and motion conditions,’’ Biomed. Eng. OnLine, vol. 17, no. 1, pp. 1– 26, Dec. 2018.
24. X. Shen et al., ‘‘PupilHeart: Heart rate variability monitoring via pupillary fluctuations on mobile devices,’’ IEEE Internet Things J., vol. 10, no. 20, pp. 18042–18053, Oct. 2023.
25. Murugeshwari, B., Sudharson, K., Panimalar, S. P., Shanmugapriya, M., & Abinaya, M. (2020). SAFE–Secure Authentication in Federated Environment using CEG Key code.
26. Anand, L., & Neelanarayanan, V. (2019). Liver disease classification using deep learning algorithm. BEIESP, 8(12), 5105-5111.
