Open Access
·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667
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The Journal of Information and Optimization Sciences (JIOS) is a world leading journal publishing high quality, rigorously peer-reviewed original research in all mathematically-oriented theoretical and applied topics in information sciences, optimization sciences and related areas since 1980. Subjects include but are not limited to:
• Information Sciences
• Optimization Sciences
• Control Theory
• Operational Research
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• Mathematical Programming
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Issues up to 2022 co-published with and available at:
Securing OBD II sensor technology using cryptographic method in safety-based driver behavior analysis
Siddhanta Kumar SinghSiddhanta.singh@jaipur.manipal.eduDepartment of Computer and Communication Engineering Manipal University JaipurJaipur, Rajasthan, 303007, IndiaView full profile →
, *Vijay Shankar SharmaCorresponding authorvijayshankar.sharma@jaipur.manipal.eduDepartment of Computer and Communication Engineering Manipal University JaipurJaipur, Rajasthan, 303007, IndiaView full profile →
, Sandeep Kumar Sharmasandeep.sharma@jaipur.manipal.eduDepartment of Computer and Communication Engineering Manipal University JaipurJaipur, Rajasthan, 303007, IndiaView full profile →
, Atul Srivastavaatul.nd2@gmail.comDepartment of Computer Science and Engineering Amity School of Engineering and TechnologyDepartment of Computer Science and Engineering Amity School of Engineering and Technology Amity UniversityLucknow, Uttar Pradesh, 226028, IndiaView full profile →
, Anuradha Pillaianuradha.pillai@sitpune.edu.inDepartment of Computer Science and Engineering Symbiosis Institute of TechnologyPune, Maharashtra, 412115, IndiaView full profile →
* Corresponding author · click or hover a name for details
Transportation plays a important role in our everyday life, but increasing vehicle numbers worsen traffic congestion and hence requires to focus the security issues. Safety is a key concern, especially in India, which faces significant road safety challenges. Analyzing driving styles can aid authorities in identifying errors and enhancing security measures. This study investigates integrated sensor and computing systems for assessing driving behaviors and provide security to the system. It evaluates driver performance across speed, acceleration, deceleration, and rpm scenarios using OBD II sensors linked to the Electronic Control Unit (ECU). Torque Pro analyzes raw data from OBD II readers to assess driving styles, categorizing results as safe, normal, aggressive, or dangerous. A t-test, conducted using IBM SPSS, assesses differences between GPS and OBD II speeds to select appropriate datasets. The driving dataset is stored in Firebase cloud for analysis. Securing OBD II dataset in Firebase Cloud involves ensuring data integrity, confidentiality, and availability.
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