Informatics Publishing Limited

A. Tamizhselvi ¹, P. Kavitha Rani ², P. Vijayalakshmi ³, Sachin Vasant Chaudhari ⁴

Affiliations
1 Department of Information Technology, St. Joseph College of Engineering, IN
2 Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, IN
3 Department of Computer Science and Engineering, Knowledge Institute of Technology, IN
4 Department of Electronics and Computer Engineering, Sanjivani College of Engineering, IN

Abstract

Efficient traffic management in wireless networks is crucial for optimizing resource utilization and enhancing overall network performance. This paper introduces a novel approach to dynamic routing algorithms utilizing evolutionary algorithms for effective wireless traffic management. The proposed system leverages the adaptability and optimization capabilities of evolutionary algorithms to dynamically adjust routing paths based on real-time network conditions. Our algorithm employs a genetic programming framework to evolve and refine routing strategies, considering factors such as network congestion, link quality, and traffic load. This dynamic approach enables the network to autonomously adapt to changing conditions, ensuring optimal route selection for data transmission. The evolutionary nature of the algorithm allows it to continually learn and improve, making it well-suited for the dynamic and unpredictable nature of wireless environments. The effectiveness of the proposed algorithm is evaluated through extensive simulations, demonstrating significant improvements in terms of throughput, latency, and overall network efficiency compared to traditional static routing approaches. The system ability to handle diverse traffic patterns and adapt to varying network scenarios positions it as a robust solution for next-generation wireless networks.

Keywords

Dynamic Routing, Evolutionary Algorithms, Wireless Networks, Traffic Management, Genetic Programming.

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M. Monika ¹, Udutha Rajender ², A. Tamizhselvi ³, Aniruddha S. Rumale ⁴

Affiliations
1 Department of Master of Computer Applications, Cambridge Institute of Technology, IN
2 Department of Electronics and Communication Engineering, Vaageswari College of Engineering, IN
3 Department of Information Technology, St. Joseph’s College of Engineering, IN
4 Department of Information Technology, Sandip Institute of Technology and Research Centre, IN

Abstract

Video object detection plays a pivotal role in various applications, from surveillance to autonomous vehicles. This research addresses the need for real-time object detection in videos using advanced deep learning models. The current landscape of object detection techniques often struggles to maintain efficiency in processing video streams, leading to delays and resource-intensive computations. This study aims to bridge this gap by proposing a novel methodology for real-time object detection in videos. With the surge in video data across domains, the demand for swift and accurate object detection in real-time has become imperative. Existing methods face challenges in balancing speed and precision, prompting the exploration of more robust solutions. This research endeavors to enhance the efficiency of video object detection, offering a timely and accurate approach to address contemporary demands. The primary challenge lies in achieving real-time object detection without compromising accuracy. Traditional methods often compromise speed for precision, leading to inadequate performance in dynamic video environments. This study seeks to overcome this dilemma by introducing a methodology that optimizes both speed and accuracy, catering to the real-time constraints of video processing. Despite the advancements in object detection, a notable research gap exists in the domain of real-time video object detection. Existing models exhibit limitations in adapting to the dynamic nature of video streams, necessitating the development of novel methodologies. This research aims to fill this void by proposing an innovative approach that addresses the specific challenges posed by real-time video data. The proposed methodology integrates state-of-the-art deep learning models, optimizing them for real-time video object detection. Leveraging advanced architectures and streamlining the inference process, the model aims to provide accurate detections at unparalleled speeds. Additionally, a novel data augmentation technique is introduced to enhance the model’s adaptability to dynamic video scenarios. Preliminary results demonstrate the effectiveness of the proposed methodology, showcasing a significant improvement in both real-time processing speed and object detection accuracy. The model exhibits promising performance across diverse video datasets, highlighting its potential to outperform existing methods in real-world applications.

Keywords

Real-Time Object Detection, Deep Learning, Video Analysis, Computer Vision, Model Optimization

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R. Saranya ¹, A. Tamizhselvi ², Piyush Charan ³, Syed Arfath Ahmed ⁴

Affiliations
1 Department of Computer Science with Data Analytics, PSG College of Arts and Science, IN
2 Department of Information Technology, St. Joseph's College of Engineering, IN
3 Department of Electronics and Communication Engineering, Manav Rachna University, IN
4 Department of Computer Science and Engineering, Maulana Azad National Urdu University, IN

Abstract

Intelligent vehicular networks leverage communication technologies to enable vehicles to interact with each other and with infrastructure elements, such as roadside units and traffic signals, to enhance road safety and efficiency. However, the open nature of these networks exposes them to various security threats, including data interception, tampering, and unauthorized access. Traditional encryption methods may not suffice to address these challenges, necessitating the adoption of advanced cryptographic techniques like ECC and RSA. Prior research in the field has primarily focused on either enhancing communication protocols or improving security measures independently. However, there is a notable gap in research that comprehensively addresses both aspects simultaneously. This study aims to fill this void by proposing an integrated approach that ensures both seamless communication and robust security in intelligent vehicular networks. The proposed methodology involves the design and implementation of a hybrid cryptographic scheme combining ECC and RSA algorithms. This scheme will be integrated into the existing communication infrastructure of intelligent vehicular networks. The performance of the system will be evaluated through simulations and real-world experiments to assess its effectiveness in securing communication channels while minimizing overhead. The results show the effectiveness of the proposed ECC-RSA hybrid encryption scheme in securing communication channels within intelligent vehicular networks. The integration of ECC and RSA protocols not only enhances the security of data transmission but also ensures seamless communication, thereby advancing road safety in intelligent vehicular environments.

Keywords

Intelligent Vehicular Networks, Road Safety, Elliptic Curve Cryptography, RSA Communication, Secure Communication.

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