Using Smart Management Frameworks to Reduce Memory Fragmentation in IoT based Embedded System

The aim of study, IoT embedded systems work in contexts with limited resources, they suffer greatly in terms of performance, energy economy, and dependability from memory fragmentation. As IoT devices become more ubiquitous, effective memory management methods that satisfy their unique needs are even more in demand. This paper provides a Smart Management Framework (SMF) managing memory fragmentation by means of compaction tactics, dynamic garbage collecting approaches, and memory-aware allocation algorithms. The SMF ensures superior performance in dynamic and real-time IoT based embedded system systems by means of memory use optimisation, reduction of fragmentation, and enhancement of energy economy. The SMF performs better than more conventional methods on fragmentation reduction (30%), allocation time (20%), and energy consumption (14% lower). These findings show that in many additional applications the SMF can simultaneously boost their efficiency and scalability while extending the running lifetime of IoT devices in many different uses. This work lays long-term, effective resource management in IoT based embedded systems.