An Adaptive Framework for Optimizing Transactions in Mobile Distributed Real-Time Database Systems

Abstract

Mobile Distributed Real-Time Database Systems (MDRTDBS) are pivotal for applications requiring real-time data processing under constrained resources. This paper introduces a novel framework integrating three adaptive algorithms: Piggybacking for Communication Optimization, Dynamic Transaction Shipping, and Real-Time Priority Scheduling. The proposed framework addresses the critical challenges of high communication overhead, limited energy resources, and dynamic real-time variations. Through extensive simulations, the framework demonstrates a 75% reduction in communication overhead, 30% lower energy consumption, and 25% reduced transaction latency while achieving a 40% higher success rate compared to existing methods. These advancements position the framework as a robust solution for enhancing the efficiency and reliability of MDRTDBS across diverse applications, such as telemedicine and IoT infrastructures.