Innovative Data Replication Techniques for Effective Latency Reduction in Mobile Edge Computing and IoT

Applications like smart cities, driverless vehicles, healthcare, and industrial automation are made possible by the revolutionary grouping of the IoT and MEC. Problems with data management, especially with lowering latency for real-time applications, plague these systems. System efficiency, operational dependability, and user experience can all take a hit when data transmission delays occur. Copying and distributing data over numerous servers or edge nodes enables faster access to information, and novel data replication techniques are being developed to meet this. Because of resource limitations, unpredictable user requests, and changing network circumstances, traditional data replication approaches struggle in the distributed and dynamic settings of MEC and the Internet of Things (IoT). Intelligent data placement algorithms, network-aware replication, and demand-based dynamic replication are examples of advanced approaches. By decreasing latency and avoiding the need for repeated calls to the central cloud, these strategies guarantee that data is always available at the proper location and time. Particularly for latency-sensitive applications like augmented reality, autonomous vehicles, and telemedicine, this paper assesses these methods using simulations and case studies from real-world MEC and IoT deployments, showing that they can considerably decrease latency, improve system responsiveness, and increase data availability. Results show that MEC and IoT data management may be enhanced in terms of performance, scalability, and user happiness, which advances the state of the art.