Distributed Fog Computing Based on Improved LT codes for Deep Learning in Web of Things

Abstract

With the rapid development of the Web of Things, there have been a lot of sensors deployed. Advanced knowledge can be achieved by deep learning method and easier integration with open Web standards. A large number of the data generated by sensors required extra processing resources due to the limited resources of the sensors. Due to the limitation of bandwidth or requirement of low latency, it is impossible to transfer such large amounts of data to cloud servers for processing. Thus, the concept of distributed fog computing has been proposed to process such big data into knowledge in real-time. Large scale fog computing system is built using cheap devices, denotes as fog nodes. Therefore, the resiliency to fog node failures should be considered in design of distributed fog computing. LT codes (LTC) have important applications in the design of modern distributed computing, which can reduce the latency of the computing tasks, such as matrix multiplication in deep learning methods. In this paper, we consider that fog nodes may be failure, and an improved LT codes are applied to matrix multiplication of distributed fog computing process to reduce latency. Numerical results show that the improved LTC based scheme can reduce average overhead and degree simultaneously, which reduce the latency and computation complexity of distributed fog computing.