Differentiated Content Acquisition Integrating Network Coding and Edge Computing for Industrial Internet

Abstract

The construction of the Industrial Internet has become a concrete implementation and an essential starting point in accelerating the digital transformation and intelligent upgrading of industrial manufacturing enterprises. The problem of addressing and forwarding for resource-constrained devices restricts data acquisition and dissemination on the Industrial Internet. In order to retrieve content reasonably, we propose IDEANE, an identity-differentiated content acquisition and multipath forwarding scheme with network coding and edge computing. In IDEANE, content requests are disseminated based on the identity and location information carried in a multi-identifier network, which can improve the efficiency of interest message requests and reduce idle links in the network. Moreover, the content acquisition computation is offloaded to multiple edge nodes, and the encoded data are transmitted to the edge nodes for recovery. IDEANE offloads the less demanding computing tasks to the edge nodes close to the content requesters, to relieve the pressure on providers. In addition, a collaboration method among multiple edge nodes is also studied. Multiple edge nodes collaborate to support the mobility of content requesters, save energy consumption in terminal devices, reduce transmission latency, and ensure data security. The experimental results show that IDEANE can avoid duplicated transmission, reduce the network link overhead, improve network throughput, and enhance network robustness and reliability.