Design and Implementation of Intelligent Community System Based on Thin Client and Cloud Computing

TL;DR

This paper presents an intelligent community system integrating thin clients, broadband networks, and cloud computing to enhance resource sharing, efficiency, and robustness in community information services.

Contribution

It introduces a novel system architecture combining thin clients and cloud computing for community management, addressing limitations of mobile devices and resource sharing barriers.

Findings

System is robust and efficient in real community deployment

Thin clients improve energy efficiency and computing capacity

Cloud servers facilitate resource sharing among users

Abstract

With the continuous development of science and technology, the intelligent development of community system becomes a trend. Meanwhile, smart mobile devices and cloud computing technology are increasingly used in intelligent information systems; however, smart mobile devices such as smartphone and smart pad, also known as thin clients, limited by either their capacities (CPU, memory or battery) or their network resources, do not always meet users' satisfaction in using mobile services. Mobile cloud computing, in which resource-rich virtual machines of smart mobile device are provided to a customer as a service, can be terrific solution for expanding the limitation of real smart mobile device, but the resources utilization rate is low and the information cannot be shared easily. To address the problems above, this paper proposes an information system for intelligent community, which is composed of thin clients, wide band network and cloud computing servers. On one hand, the thin clients with the characteristics of energy efficiency, high robustness and high computing capacity can efficiently avoid the problems encountered in the PC architecture and mobile devices. On the other hand, the cloud computing servers in the proposed information system solve the problems of resource sharing barriers. Finally, the system is built in real environments to evaluate the performance. We deploy the proposed system in a community with more than 2000 residents, and it is demonstrated that the proposed system is robust and efficient.