Abstract
Nowadays, more and more sensors, devices and applications are connected in Industrial Internet of Things (IIoT), producing massive real-time flows which need to be scheduled for Quality-of-Service provision. To realize application-aware and adaptive flow scheduling, the problem of traffic classification must be addressed at first. When edge computing paradigm is introduced into IIoT, the traffic classification service can be deployed on edge node in the near-end. Recently, deep-learning-based IIoT traffic classification methods show better performance, but the computational cost of deep learning model is too high to be deployed on edge node. Moreover, increasingly unknown flows generated by new devices and emerging industrial APPs lead to frequent training of traffic classifiers. It is difficult to migrate the complex process of classifier training from cloud server to edge nodes with limited resources. To address these issues, we take the benefits of hash mechanism and consensus mechanism in blockchain to design a lightweight IIoT traffic classification service, which is more applicable for edge computing paradigm. First, inspired by the hash mechanism in blockchain and the learning to hash for big data, we propose a new learning-to-hash method named extension hashing. By this method, we can build the set of binary coding tress (BCT set), then generating hash table for more efficient k-nearest neighbor-based classification without complex classifier training. Then, we design a new voting-based consensus algorithm to synchronize the BCT sets and the hash tables across edge nodes, thereby providing the traffic classification service. Finally, we conduct data-driven simulations to evaluate the proposed service. By comparing traffic classification results on public data set, we can see that the proposed service achieves the highest classification accuracy with the minimal time cost and memory usage.