journal article·2025-02-27Open access

An Efficient Data Transmission Protocol Based on Embedded System Using Cellular Technology Infrastructure

Cesar Isaza, Jonny Paul Zavala De Paz, Ely Karina Anaya, Jose Amilcar Rizzo Sierra, Cristian Felipe Ramirez-Gutierrez, Pamela Rocio Ibarra Tapia

Applied Sciences· Volume 15· Issue 5

Abstract

Every time the proper functioning of the vehicles must be guaranteed, as well as safety and efficiency. To achieve this, some expensive solutions are used, with few connectivity options and that fail to meet consumer demand. This paper presents a low-cost hardware system for the design of a real-time communication protocol between the electronic control unit (ECU) of a vehicle and a remote server based in a embedded system. A dual tone multi-frequency (DTMF) approach is implemented, so error codes (DTCs) are always available on a unit equipped with this system. The vehicle-to-infrastructure (V2I) communication protocol through voice channels is provided by cellular technology infrastructure, in which primary information is shared to monitor vehicles. With real-time data transmission, communication is established through a voice phone call between the vehicle’s ECU and the destination server, communicating the DTC codes. The system shows that the communication protocol has an effectiveness of 78.23%, which means that with the use of 2G technology, which is active and operating in many regions, it allows the information with the data to be received by the receiving user. Through this implemented system, it is ensured that if a vehicle suffers an accident or stops due to a mechanical failure in a region where there is no cellular technology coverage, information or a message can be sent so that through communication the rescue can be carried out using an cellular technology coverage.
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Research context

OpenAlex topics: IoT Networks and Protocols · IoT-based Smart Home Systems · Bluetooth and Wireless Communication Technologies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Cuerpo Académico de Tecnologías de la Información y Comunicación Aplicada, Universidad Politécnica de Querétaro, El Marqués 76240, Querétaro, Mexico

References (54)

Show source-provided references
1. Exploring the implications of autonomous vehicles: A comprehensive review10.1007/s41062-022-00763-6 ↗
2. Understanding the long-term emergence of autonomous vehicles technologies10.1016/j.techfore.2021.120852 ↗
3. Cummings, M., and Britton, D. (2020). Chapter 6—Regulating safety-critical autonomous systems: Past, present, and future perspectives. Living with Robots, Academic Press.10.1016/B978-0-12-815367-3.00006-2 ↗
4. Advances in smart roads for future smart cities
5. Bibliometric review of telematics-based automobile insurance: Mapping the landscape of research and knowledge10.1016/j.aap.2023.107428 ↗
6. Ovidiu, V., John, R., Patrick, P., Gerardo, D., Kai, K., Gerhard, M., Vincent, L., Roy, B., Hans, E., and Steffen, B. (2021). Automotive Intelligence Embedded in Electric Connected Autonomous and Shared Vehicles Technology for Sustainable Green Mobility. Front. Future Transp., 2.10.3389/ffutr.2021.688482 ↗
7. A Stochastic Bi-Level Optimal Allocation Approach of Intelligent Buildings Considering Energy Storage Sharing Services10.1109/TCE.2024.3412803 ↗
8. A comprehensive overview of vehicle to everything (V2X) technology for sustainable EV adoption10.1016/j.est.2023.109304 ↗
9. Enhancing control disorder and implementing V2X-Based suppression methods for electric vehicle CO2 thermal management systems10.1016/j.etran.2024.100336 ↗
10. Aissaoui, R., Menouar, H., Dhraief, A., Filali, F., Belghith, A., and Abu-Dayya, A. (2014, January 10–14). Advanced real-time traffic monitoring system based on V2X communications. Proceedings of the 2014 IEEE International Conference on Communications (ICC), Sydney, Australia.10.1109/ICC.2014.6883734 ↗
11. Vehicle tracking for an evasive manoeuvres assistant using low-cost ultrasonic sensors10.3390/s141222689 ↗
12. Arpad, T., and Tamas, H. (2024). A Method for Mapping V2X Communication Requirements to Highly Automated and Autonomous Vehicle Functions. Future Internet, 16.10.3390/fi16040108 ↗
13. Schindelhauer, C. (2006, January 21–27). Mobility in Wireless Networks. Proceedings of the SOFSEM 2006: Theory and Practice of Computer Science, Merin, Czech Republic.10.1007/11611257_9 ↗
14. Investigation on cellular LTE C-V2X network servingvehicular data traffic in realistic urban scenarios10.1016/j.future.2024.07.002 ↗
15. Nawaz, G., Nazmus, M., Suruz, M., Sameer, A., and Yun, W. (2024). A Comprehensive Study and Analysis of the Third Generation Partnership Project’s 5G New Radio for Vehicle-to-Everything Communication. Future Internet, 16.10.3390/fi16010021 ↗
16. Muhammad, N., Pekka, L., and Marcos, K. (2022). Connected Vehicles: V2V and V2I Road Weather and Traffic Communication Using Cellular Technologies. Sensors, 2.
17. Muhammad, J., Manzoor, A., Sumbal, M., Parag, K., Najla, A., Obaid, U., Hesham, E., Amir, A., and Sherzod, T. (2023). Advancing C-V2X for Level 5 Autonomous Driving from the Perspective of 3GPP Standards. Sensors, 23.10.3390/s23042261 ↗
18. Saleh, A. (2021). Evaluation of Development Level and Technical Contribution of Recent Technologies Adopted to Meet the Challenges of 5G Wireless Cellular Networks. Sensors, 13.10.3390/sym13040635 ↗
19. Guzmán Leguel, A.K., Nguyen, H.-H., Gómez Gutiérrez, D., Yoo, J., and Jeong, H.-Y. (2024). Enhancing the Minimum Awareness Failure Distance in V2X Communications: A Deep Reinforcement Learning Approach. Sensors, 24.10.3390/s24186086 ↗
20. Hojeong, L., Seungmo, K., and Hyogon, K. (2024). Causality-Sensitive Scheduling to Reduce Latency in Vehicle-to-Vehicle Interactions. Sensors, 24.10.3390/s24227142 ↗
21. Enhancing Security in Vehicle-to-Vehicle Communication: A Comprehensive Review of Protocols and Techniques10.3390/vehicles6010020 ↗
22. Vicente, M., Cesar, A., Azurdia, M., Céspedes, S., Pablo, O., Samuel, M., Rojas, J., and Ismael, S. (2018, January 18–20). Implementation of a Low-Cost Vehicular VLC System and CAN Bus Interface. Proceedings of the 2018 11th International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP), Budapest, Hungary.
23. Sumeet, S., Joel, M., Chintan, P., and Nishant, D. (2019, January 4–7). Voice-Controlled Autonomous Vehicle Using IoT. Proceedings of the 3rd International Workshop on Recent Advances on Internet of Things: Technology and Application Approaches (IoT-T & A 2019), Coimbra, Portugal.
24. Jeong, P., and Hyeong, N. (2020). Front-End of Vehicle-Embedded Speech Recognition for Voice-Driven Multi-UAVs Control. Appl. Sci., 10.10.3390/app10196876 ↗
25. Abubakar, U., Mohammed, A., Abduljalal, Y., Muhammad, U., Ibrahim, A., Ezekiel, E., and Matthew, T. (2023, January 15–30). Development of an Android-Based, Voice-Controlled Autonomous Robotic Vehicle. Proceedings of the 10th International Electronic Conference on Sensors and Applications, Virtual.
26. Jeong, P., and Hyeong, N. (2023). In-Vehicle Speech Recognition for Voice-Driven UAV Control in a Collaborative Environment of MAV and UAV. Aerospace, 10.10.3390/aerospace10100841 ↗
27. Security assessment of in-vehicle communication protocols
28. Bi, Z., Xu, G., Xu, G., Wang, C., and Zhang, S. (2022). Bit-Level Automotive Controller Area Network Message Reverse Framework Based on Linear Regression. Sensors, 22.10.3390/s22030981 ↗
29. Ji, C., Ko, T., and Hong, M. (2021). CA-CRE: Classification Algorithm-Based Controller Area Network Payload Format Reverse-Engineering Method. Electronics, 10.10.3390/electronics10192442 ↗
30. An intelligent embedded diagnostic system on CAN protocol to avoid rear-end collision of vehicles
31. Accident avoidance system using CAN10.17485/ijst/2016/v9i30/99027 ↗
32. Architecture and Potential of Connected and Autonomous Vehicles10.3390/vehicles6010012 ↗
33. An intelligent system to avoid collision in vehicle based on can protocol
34. Implementation and performance measurement of a V2X communication system for vehicle and pedestrian safety
35. Jimenez, F., Naranjo, J.E., Serradilla, F., Perez, E., Hernandez, M., Ruiz, T., Anaya, J., and Diaz, A. (2016). Intravehicular, short and long-range communication information fusion for providing safe speed warnings. Sensors, 16.10.3390/s16010131 ↗
36. Foysal, K., Abdelgawad, A., Prasanth, V., and Yelamarthi, K. (2020). LoRa architecture for V2X communication: An experimental evaluation with vehicles on the move. Sensors, 20.10.3390/s20236876 ↗
37. Dedicated networks for IoT: PHY/MAC state of the art and challenges10.4108/eai.26-10-2015.150597 ↗
38. An empirical study on ad hoc performance of DSRC andWi-Fi vehicular communications10.1155/2013/482695 ↗
39. IEEE 802.11 bd & 5G NR V2X: Evolution of radio access technologies for V2X communications10.1109/ACCESS.2019.2919489 ↗
40. Bluetooth 5: A concrete step forward toward the IoT10.1109/MCOM.2018.1700053 ↗
41. A Survey of Various Schedulers Used for Fair Bandwidth Allocation in WiMAX: IEEE 802.1610.1080/13614576.2020.1742766 ↗
42. Narang, G., Lonavala, B.V., Dadhich, R., and Yadav, D. (2016, January 18–19). Survey of Mobile WiMAX and its threats. Proceedings of the 2016 International Conference on ICT in Business Industry & Government (ICTBIG), Indore, India.10.1109/ICTBIG.2016.7892709 ↗
43. Fitriawan, H., Susanto, M., Arifin, A., Mausa, D., and Trisanto, A. (2017, January 24–27). ZigBee based wireless sensor networks and performance analysis in various environments. Proceedings of the 2017 15th International Conference on Quality in Research (QiR): International Symposium on Electrical and Computer Engineering, Bali, Indonesia.10.1109/QIR.2017.8168495 ↗
44. Edge-Assisted V2X Motion Planning and Power Control Under Channel Uncertainty10.1109/TVT.2023.3250654 ↗
45. Collision Avoidance Predictive Motion Planning Based on Integrated Perception and V2V Communication10.1109/TITS.2022.3173674 ↗
46. On the 5G-based V2V broadcast communications: A security perspective10.1016/j.array.2021.100084 ↗
47. On the potential of V2X message compression for vehicular networks10.1109/ACCESS.2020.3041688 ↗
48. Vladyko, A., Elagin, V., Spirkina, A., Multhanna, A., and Ateya, A. (2022). Distributed edge computing with blockchain technology to enable ultra-realiable low-latency V2X communications. Electronics, 11.10.3390/electronics11020173 ↗
49. Talavera, E., Anaya, J., Gomez, O., Jimenez, F., and Naranjo, J. (2018). Performance comparison of geobrodcast strategies for winding roads. Electronics, 7.10.3390/electronics7030032 ↗
50. Eduard, Z., and Stefan, H. (2024). Enhancing Scalability of C-V2X and DSRC Vehicular Communication Protocols with LoRa 2.4 GHz in the Scenario of Urban Traffic Systems. Electronics, 13.10.3390/electronics13142845 ↗
51. Survey on cooperatively V2X downloading for intelligent transport systems10.1049/iet-its.2018.5104 ↗
52. Arena, F., and Pau, G. (2019). An overview of vehicular communications. Future Internet, 11.10.3390/fi11020027 ↗
53. ITU (International Telecommunication Union) (1990). Q.24. Multi-Frequency Push-Button Signal Reception, ITU (International Telecommunication Union).
54. Yeh, C., and Hwang, S. (2019). Efficient Detection Approach for DTMF Signal Detection. Appl. Sci., 9.10.3390/app9030422 ↗