Edge-Learning-Based Hierarchical Prefetching for Collaborative Information Streaming in Social IoT Systems

For smart cities, ubiquitous user connectivity and collaborative computation offloading are significant for the ever-increasing information requirements to promote the quality of citizens' life. In this article, we design an information prefetching architecture, which investigates a hierarchical data storage and selection strategy, including local to edge and edge to cloud. Building on collected data in the social media system or sensor networks, we specifically focus on analyzing mobile terminals' behaviors to assure the precision of our prefetching strategy in different kinds of information streaming. To assemble edge agents (EAs) prefetching, we also consider the characteristics of wireless backhaul. This scheme is carried out to optimize the EAs prefetching framework by the independent and joint action modules that are based on the theory of deep reinforcement learning (DRL). It paves a better way of collaborative edge computing (CEC) that can be built by using an independent/joint edge-learning model to help and promote the algorithm efficiency and cost-effectiveness. Furthermore, for hiding the information of data transmission between the cloud and the edge servers during data prefetching, this hierarchical scheme is designed as an implicit index maintained by edge servers. Our results show rationales on the obtainable performance of EAs architectures and their reciprocity with the dynamic change of mobile terminals' requirements.

Automated summary

For smart cities, ubiquitous user connectivity and collaborative computation offloading are significant. This article proposes an information prefetching architecture that optimizes collaborative edge computing using a hierarchical data storage and selection strategy. It also utilizes an independent/joint edge-learning model to improve algorithm efficiency and cost-effectiveness.