Optimizing Data Transmission in IoT Networks through Enhanced Compression and Edge Computing Techniques

As the Internet of Things (IoT) continues to evolve, ensuring efficient data compression and transmission emerges as a pivotal challenge, especially considering the energy constraints and data volume transmitted across the network. This work introduces an innovative approach for enhancing data compression in IoT systems, leveraging algorithms that efficiently utilize edge technologies for data distribution and transmission, coupled with data compression via a proficiently trained neural network. The primary aim of this methodology is to curtail the data transmission volume within the IoT network, which consequentially amplifies data transmission speeds and minimizes transmission costs. Furthermore, the proposed distributed system, operative on edge devices, ensures system scalability while mitigating the probability of data transmission errors, concurrently augmenting data security within the IoT network. Through meticulous analysis and application of IoT data compression methods, we have refined the data transmission process, utilizing a distinctive blend of classification and distribution. The ensuing software, derived from the proposed method, renders specialized data compression for the IoT network. Preliminary results indicate that amalgamating the neural network method of data compression with classical methods on edge devices markedly elevates data transmission efficiency, offering a viable pathway to balancing energy efficiency, transmission costs, and security in IoT networks.