A Response-Aware Traffic Offloading Scheme Using Regression Machine Learning for User-Centric Large-Scale Internet of Things

Resource allocation and management in an Internet-of-Things (IoT) paradigm requires precise request and response processing irrespective of its scalability support. Unpredictable traffic patterns and user density demands reliable offloading for handling user request traffic and service response. Considering the need for large-scale IoT in an account of its interoperability and heterogeneous support, this manuscript introduces a response-aware traffic offloading scheme (RTOS) for delay-sensitive user requests. This offloading scheme is supported by a multivariate spline regression machine learning model for classifying traffic for reducing the failure rate. The splines are adaptive based on the classified traffic for performing independent and shared offloading. The computation process for determining the offloading model is inherited from the cyber-physical system (CPS) coupled with the IoT-Cloud architecture. The information from the knowledge base and event logs are exploited for decision making in employing the offloading method for the classified traffic. The simulation analysis of this scheme shows that it is effective in improving the request processing ratio and reducing processing, response time, and delay. The simulation is performed for the varying user density and traffic flows.