A Response-Aware Traffic Offloading Scheme Using Regression Machine Learning for User-Centric Large-Scale Internet of Things
نویسندگان
چکیده
Resource allocation and management in an Internet-of-Things (IoT) paradigm requires precise request response processing irrespective of its scalability support. Unpredictable traffic patterns user density demands reliable offloading for handling service response. Considering the need large-scale IoT account interoperability heterogeneous support, this manuscript introduces a response-aware scheme (RTOS) delay-sensitive requests. This is supported by multivariate spline regression machine learning model classifying reducing failure rate. The splines are adaptive based on classified performing independent shared offloading. computation process determining inherited from cyber-physical system (CPS) coupled with IoT-Cloud architecture. information knowledge base event logs exploited decision making employing method traffic. simulation analysis shows that it effective improving ratio processing, time, delay. performed varying flows.
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ژورنال
عنوان ژورنال: IEEE Internet of Things Journal
سال: 2021
ISSN: ['2372-2541', '2327-4662']
DOI: https://doi.org/10.1109/jiot.2020.3022322