Innovative Approaches to Reducing Data Traffic in IoT Networks Using Deep Learning and Compressive Sensing - Balajee Sharma; Hemant Rajoriya (International Journal of Satellite Remote Sensing, 2024)
The exponential growth of internet of things (IoT) devices has posed unprecedented challenges in managing the massive data generated by real-time monitoring, automation, and analytics. Existing network infrastructures lack scalability, bandwidth, and suffer from latency problems, further making data transmission less efficient. This study surveys innovative approaches using deep learning and compressive sensing to reduce IoT data traffic. Deep learning is able to upgrade data processing by means of very effective pattern extraction and predictive modeling, while compressive sensing reduces redundancy using sparsity in data. The number of IoT devices has been growing quickly, and data generated by these devices ranges from analytics to real-time monitoring and automation. Given that the world is largely dependent on the Internet, thanks to developments like 5G and fiber optics, which enable users to access and create IoT. Data generation has skyrocketed, and solutions are very inexpensive. Despite the fact that this expansion offers numerous benefits, it severely strains network infrastructures that were not built to handle the large volume of data traffic from IoT devices. All the three together are scalable, energy-efficient, and latency-aware solutions for the IoT ecosystems. These techniques together are studied across applications such as smart cities, healthcare, and industrial automation, underlining their transformative potential. Difficulties like resource constraints, heterogeneity, and security risks are also presented and future research directions along with technological progress are included.
Keywords: Data compression; Deep Learning; Edge Computing; Internet of things (IoT); Machine Learning; Signal Reconstruction; Smart cities; compressive sensing; data traffic reduction; energy efficiency