Integrating Edge and Cloud Processing for Dynamic IoT Data Analytics in Wireless Networks

Management of dynamic data analytics on wireless networks has become very challenging because of the limitations such as computational resources, latency requirements, and bandwidth that have arisen due to the proliferation of IoT devices. To address these issues, this paper propose a Distributed Edge-Cloud Collaborative Framework (DECCF) that makes it possible to flexibly partition the workload between the cloud and the edge, thus improving the processing of IoT data. The system's efficient consumption of bandwidth and reduced latency are supported by real-time monitoring of resources and prioritization of tasks. DECCF's value was demonstrated by a 96% task success rate and a 95% bandwidth utilization efficiency in extensive testing conducted on a publicly available IoT dataset. The framework improved performance in dynamic scenarios by reducing latency by 25% compared to traditional hybrid systems. Results obtained through this study have thus established that DECCF could serve to make Internet of Things data analytics scalable, efficient, and reliable in the future-generation wireless network, especially targeting various domains including smart cities, health monitoring systems, and industrial automation among others.