Collaborate Edge and Cloud Computing With Distributed Deep Learning for Smart City Internet of Things

City Internet-of-Things (IoT) applications are becoming increasingly complicated and thus require large amounts of computational resources and strict latency requirements. Mobile cloud computing (MCC) is an effective way to alleviate the limitation of computation capacity by offloading complex tasks from mobile devices (MDs) to central clouds. Besides, mobile-edge computing (MEC) is a promising technology to reduce latency during data transmission and save energy by providing services in a timely manner. However, it is still difficult to solve the task offloading challenges in heterogeneous cloud computing environments, where edge clouds and central clouds work collaboratively to satisfy the requirements of city IoT applications. In this article, we consider the heterogeneity of edge and central cloud servers in the offloading destination selection. To jointly optimize the system utility and the bandwidth allocation for each MD, we establish a hybrid offloading model, including the collaboration of MCC and MEC. A distributed deep learning-driven task offloading (DDTO) algorithm is proposed to generate near-optimal offloading decisions over the MDs, edge cloud server, and central cloud server. Experimental results demonstrate the accuracy of the DDTO algorithm, which can effectively and efficiently generate near-optimal offloading decisions in the edge and cloud computing environments. Furthermore, it achieves high performance and greatly reduces the computational complexity when compared with other offloading schemes that neglect the collaboration of heterogeneous clouds. More precisely, the DDTO scheme can improve computational performance by 63%, compared with the local-only scheme.

I. Introduction

With the fast development of mobile networks and the widespread application of city Internet of Things (IoT) in various fields (e.g., smart transportation, smart home, and smart manufacturing), the demand for mobile devices (MDs) is increasing drastically. However, MDs, such as smartphones, tablet computers, unmanned aerial vehicles (UAVs), and wearable devices, are usually constrained by limited resources, e.g., CPU computing power, storage space, energy capacity, and environmental awareness. Complex computing tasks, e.g., optical character recognition (OCR), face recognition (FR), and augmented reality (AR), are inefficient to be handled locally. Furthermore, a diversity of city IoT applications, such as delay-sensitive and delay-tolerant applications can cause a variety of different computation and communication costs.