Open Access

ARTICLE

A Task-Oriented Hybrid Cloud Architecture with Deep Cognition Mechanism for Intelligent Space

Yongcheng Cui¹, Guohui Tian^1,*, Xiaochun Cheng^2,*

1 School of Control Science and Engineering, Shandong University, Jinan, China
2 Computer Science Department, Swansea University, Wales, UK

* Corresponding Authors: Guohui Tian. Email: ; Xiaochun Cheng. Email:

(This article belongs to the Special Issue: AI Powered Human-centric Computing with Cloud and Edge)

Computers, Materials & Continua 2023, 76(2), 1385-1408. https://doi.org/10.32604/cmc.2023.040246

Received 10 March 2023; Accepted 24 May 2023; Issue published 30 August 2023

Abstract

Intelligent Space (IS) is widely regarded as a promising paradigm for improving quality of life through using service task processing. As the field matures, various state-of-the-art IS architectures have been proposed. Most of the IS architectures designed for service robots face the problems of fixed-function modules and low scalability when performing service tasks. To this end, we propose a hybrid cloud service robot architecture based on a Service-Oriented Architecture (SOA). Specifically, we first use the distributed deployment of functional modules to solve the problem of high computing resource occupancy. Then, the Socket communication interface layer is designed to improve the calling efficiency of the function module. Next, the private cloud service knowledge base and the dataset for the home environment are used to improve the robustness and success rate of the robot when performing tasks. Finally, we design and deploy an interactive system based on Browser/Server (B/S) architecture, which aims to display the status of the robot in real-time as well as to expand and call the robot service. This system is integrated into the private cloud framework, which provides a feasible solution for improving the quality of life. Besides, it also fully reveals how to actively discover and provide the robot service mechanism of service tasks in the right way. The results of extensive experiments show that our cloud system provides sufficient prior knowledge that can assist the robot in completing service tasks. It is an efficient way to transmit data and reduce the computational burden on the robot. By using our cloud detection module, the robot system can save approximately 25% of the average CPU usage and reduce the average detection time by 0.1 s compared to the locally deployed system, demonstrating the reliability and practicality of our proposed architecture.

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