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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Kundu, Mayuri
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Computer Science & IT Engineering

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A compact study on methodological insights on navigational systems in vehicular traffic system Thimmappa, Prathibha; Kundu, Mayuri
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp585-591

Abstract

Navigation system has witnessed a significant inclusion of potential technological advancement in the area of vehicular traffic system. Since the last decade, there are various evolution of innovative techniques that has identified and addressed some serious problem towards vehicular navigation system. With a progress of time, artificial intelligence (AI) has evolved as contributory role model towards optimizing the performance of navigation system. However, still it is quite challenging to acquire a quick snapshot of overall stand of all such methodologies and its effectiveness. Hence, this paper presents a precise, compact, and highly crisp discussion of core taxonomies of methods towards improving navigation system. The paper also contributes towards highlighting their strength and weakness followed by updated research trend to understand the true picture. Finally, the paper contributes to highlight the critical trade-off and gaps.
Efficient data streaming in dynamic vehicular networks: a hybrid controller for seamless connectivity Thimmappa, Prathibha; Kundu, Mayuri
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp229-236

Abstract

The demand for highly efficient data transmission is being increasingly demanded for dynamic vehicular networks, especially in the case of internet-of-vehicle (IoV). The current data transmission methods are known to encounter inefficiencies in terms of unreliable routing and restricted scalability. Evolving studies have found artificial intelligence (AI) based schemes more suitable to address these issues; however, there are no significant innovations towards developing a potential framework that can not only increase data transmission performance but also minimize the analytical overheads of AI. Hence, this paper presents a novel baseline framework by introducing an optimized controller structure at anchor points with the inclusion of novel ideologies of orientation degree and selection of mediating node. The proposed model witnesses 32.3 dB of signal quality, 857 kbps throughput, 81 ms delay, and 171 ms of response time, exhibiting much better performance in contrast to the frequently used data transmission method. The proposed model contributes to a solid foundation for any futuristic AI model for efficient and reliable data transmission in IoV.
Co-Authors Thimmappa, Prathibha