Cloud-Based Real-Time Chat Application for Scalable and Secure Communication

Abstract

Traditional chat applications continue to suffer from high latency, poor scalability, and limited This system will allow the message to be delivered instantly and synchronized in real-time while allowing users to connect and connect dynamically and securely exchange messaging data. Experimental results show that our system reduces message delivery delay by 65% and increases scalability by allowing more concurrent connections without slowing down system performance. security as they are based on HTTP polling architectures with relational databases and monolithic structures. In this paper, we propose a new cloud-based real-time chat methodology that uses distributed storage, token-based user authentication, and low-latency, bidirectional protocols.

Introduction

Real-time messaging applications are essential for digital communication, but traditional systems face delays, synchronization issues, and reliability problems due to limitations in infrastructure, REST APIs, HTTP polling, and relational databases. To address these challenges, the paper proposes a cloud-based chat system leveraging distributed databases, persistent communication protocols, modular design, and AI-driven mechanisms.

The methodology introduces an enhanced routing mechanism for Wireless Sensor Networks (WSNs) with four stages: network initialization, cluster formation, intelligent cluster head (CH) selection using Q-learning, and adaptive route discovery. This approach optimizes energy usage, reduces latency, increases message delivery reliability, and ensures secure communication via encryption.

Performance results show significant improvements over traditional systems, including a 65% reduction in message delay, support for five times more concurrent users, higher message delivery success, and energy-efficient operations, demonstrating scalability, reliability, and enhanced user experience.

Conclusion

This work has incorporated intelligent CH selection due to Q-learning along with our improved routing discovery experience it will help eliminate the restrictions of those existing WSN routing protocols. The proposed model provided an effective means of managing clusters and adaptive routing while leveraging three significant properties: residual energy, remaining distance to base station, link quality, in addition it will perform in a reinforcement learning model at the same time, which reducing delay and improves system throughput, reliability, and ultimately total network lifetime - presenting suitable applications for WSN such as disaster management, smart agriculture and environmental monitoring.

References

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Copyright

Copyright © 2025 D. Suganthi, Shanmathi A, Adhulya D, Previna G, Vaibhavi M. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.