Abstract
The demand for improving productivity in manufacturing systems makes the industrial Internet of things (IIoT) an important research area spawned by the Internet of things (IoT). In IIoT systems, there is an increasing demand for different types of industrial equipment to exchange stream data with different delays. Communications between massive heterogeneous industrial devices and clouds will cause high latency and require high network bandwidth. The introduction of edge computing in the IIoT can address unacceptable processing latency and reduce the heavy link burden. However, the limited resources in edge computing servers are one of the difficulties in formulating communication scheduling and resource allocation strategies. In this article, we use deep reinforcement learning (DRL) to solve the scheduling problem in edge computing to improve the quality of services provided to users in IIoT applications. First, we propose a hierarchical scheduling model considering the central-edge computing heterogeneous architecture. Then, according to the model characteristics, a deep intelligent scheduling algorithm (DISA) based on a double deep Q network (DDQN) framework is proposed to make scheduling decisions for communication. We compare DISA with other baseline solutions using various performance metrics. Simulation results show that the proposed algorithm is more effective than other baseline algorithms.
Highlights
With the rapid development of the Internet of things (IoT), an increasing number of daily services can obtain seamless network connectivity everywhere
With the explosive growth in industrial Internet of things (IIoT) applications, the network environment becomes increasingly complex, which leads to unprecedented challenges, e.g., intermittent wireless connections, scarce spectrum resources, and high propagation delay
We propose a deep intelligent scheduling algorithm (DISA), an intelligence-driven experiential network architecture that exploits edge computing and deep reinforcement learning (DRL) for scheduling
Summary
With the rapid development of the Internet of things (IoT), an increasing number of daily services can obtain seamless network connectivity everywhere. In this case, these sensors and devices generate large amounts of data that need further processing, which provides intelligence to both continuous environmental monitoring and data analysis [4]. We propose a deep intelligent scheduling algorithm (DISA), an intelligence-driven experiential network architecture that exploits edge computing and DRL for scheduling. (1) We use the idea of DRL to describe the scheduling problem in edge computing-based IIoT and define the corresponding state space, action space, reward function, and value function (2) We propose DISA, an edge computing-based network architecture for communication scheduling.
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