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

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.

💡 Research Summary

The paper presents a comprehensive design, implementation, and real‑world evaluation of an intelligent community information system that integrates thin‑client terminals with cloud‑computing back‑ends. Recognizing that modern smartphones and tablets—often treated as thin clients—are constrained by limited CPU, memory, battery life, and network bandwidth, the authors propose a architecture that offloads the bulk of computation, storage, and application logic to a pool of resource‑rich virtual machines in the cloud. The thin clients themselves are built on low‑power ARM boards running a lightweight Yocto‑based Linux distribution; they handle only essential I/O, display rendering, and user interaction, thereby achieving high energy efficiency and robustness.

A high‑speed broadband backbone (FTTH, LTE/5G) connects the clients to the cloud, with QoS‑aware traffic management to prioritize latency‑sensitive services. On the server side, a hybrid virtualization strategy combines VM provisioning with containerized micro‑services, enabling dynamic scaling and fine‑grained resource allocation. Multi‑tenant isolation is enforced through namespace segregation and role‑based access control, while data persistence uses a blend of distributed NoSQL stores for high‑throughput reads and relational databases for transactional consistency. An API gateway and service catalog expose cloud functionalities to developers, facilitating seamless information sharing across residents.

Security is addressed through TLS 1.3 encryption, OAuth 2.0 authentication, and TPM‑based key storage on the client devices. Remote display is realized via a dual‑protocol approach (RDP and HTML5‑based VNC) to ensure compatibility with various browsers and devices.

The system was deployed in a residential community of over 2,000 households. Performance measurements under peak concurrent usage showed average response times below 120 ms, server CPU utilization around 45 %, and a 30 % reduction in overall power consumption compared with a conventional PC‑based management platform. Fault‑recovery times were halved, and the centralized cloud management console allowed administrators to monitor and control all services from a single interface.

The authors conclude that the thin‑client/cloud paradigm delivers a cost‑effective, scalable, and robust solution for smart community deployments. Future work will explore AI‑driven service automation, integration of edge‑computing nodes to further reduce latency, and advanced security mechanisms such as zero‑trust networking.