The Design and Implementation of Smart Campus System

Bi, Tianping

doi:10.17706/jcp.12.6.527-533

Cited by 17 publications

(9 citation statements)

References 6 publications

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“…Bi et al [159] developed a virtual campus through BIM and 3DGIS (geographic information system). The resulting 3D smart campus platform can be used, among other things, for construction analysis, urban planning and rail transit construction.…”

Section: B Virtual Campuses For Planning and Simulationmentioning

confidence: 99%

Smart Campuses: Extensive Review of the Last Decade of Research and Current Challenges

et al. 2021

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Novel intelligent systems to assist the energy transition and improve sustainability and people's life can be deployed at different scales, ranging from a house to an entire region. University campuses are an interesting intermediate size (big enough to matter and small enough to be tractable) for research, development, test and training on the integration of smartness at all levels, which led to the emergence of the concept of "smart campus" over the last few years. This review article proposes an extensive analysis of the scientific literature on smart campuses from the last decade (2010-2020). The 182 selected publications are distributed into seven categories of smartness: smart building, smart environment, smart mobility, smart living, smart people, smart governance and smart data. The main open questions and challenges regarding smart campuses are presented at the end of the review and deal with sustainability and energy transition, acceptability and ethics, learning models, open data policies and interoperability. The present work was carried out within the framework of the Energy Network of the Regional Leaders Summit (RLS-Energy) as part of its multilateral research efforts on smart regions.

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Section: B Virtual Campuses For Planning and Simulationmentioning

confidence: 99%

Smart Campuses: Extensive Review of the Last Decade of Research and Current Challenges

et al. 2021

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“…It has three main features: customized services, information services, and platforms for the workplace. The concept for developing a smart campus is based on IoT to use advancements in information technology to incorporate the digital environment-based application framework to deliver services with better quality [4] It supports the convergence and combination of applications' benefits for the collection of data from campus and exchange to facilitate the teaching process, services, and research facilities [5].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Auto-Scaled Service-Cloud-Based Predictive Workload Modeling and Analysis for Smart Campus System

et al. 2021

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The internet of things is an emerging technology used in cloud computing and provides many services of the cloud. The cloud services users mostly suffer from service delays and disruptions due to service cloud resource management based on vertical and horizontal scalable systems. Adding more resources to a single cloud server is called vertical scaling, and an increasing number of servers is known as horizontal scaling. The service-bursts significantly impact the vertical scaled environment where the scaleup degrades the service quality and users' trust after reaching the server's maximum capacity. Besides, the horizontally scaled environment, though being resilient, is cost-inefficient. It is also hard to detect and manage bursts online to sustain application efficiency for complex workloads. Burst detection in real-time workloads is a complicated issue because even in the presence of auto-scaling methods, it can dramatically degrade the application's efficiency. This research study presents a new bursts-aware auto-scaling approach that detects bursts in dynamic workloads using resource estimation, decision-making scaling, and workload forecasting while reducing response time. This study proposes a hybrid auto-scaled service cloud model that ensures the best approximation of vertical and horizontal scalable systems to ensure Quality of Service (QoS) for smart campus-based applications. This study carries out the workload prediction and auto-scaling employing an ensemble algorithm. The model pre-scales the scalable vertical system by leveraging the service-load predictive modeling using an ensemble classification of defined workload estimation. The prediction of the upcoming workload helped scale-up the system, and auto-scaling dynamically scaled the assigned resources to many users' service requests. The proposed model efficiently managed service-bursts by addressing load balancing challenges through horizontal auto-scaling to ensure application consistency and service availability. The study simulated the smart campus environment model to monitor the timestamped diverse service-requests appearing with different workloads.INDEX TERMS Auto-scaling, cloud computing, horizontal scalability, internet of things, predictive modeling, quality of service (QoS), smart campus, vertical scalability, workloads

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“…Sari et al [6] provides an example of a smart campus design from Indonesia focused on the use of IoT for the development of smart campus services limited to education, parking and rooms automation. Bi et al [7] provides an example from China focusing on the use of Building Information Modeling (BIM) and of 3D Geographic Information System (3DGIS). Chan and Chan [8] looks at the latest development of Smart Campus and, especially, Smart Libraries, focusing on how innovation in those areas create positive impact on those who use their services.…”

Section: Introductionmentioning

confidence: 99%

A Smart Campus Template

Augusto¹

2021

Intelligent Environments 2021

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We highlight a lack of models and theories associated with the Smart Campus concept and also an absence of processes to support its design and development. This paper provides a first approach to a theory and a set of design principles to guide their development. The theory and principles are flexible enough to be easily adapted and adopted by any organization interested in developing a Smart Campus.

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The Design and Implementation of Smart Campus System

Cited by 17 publications

References 6 publications

Smart Campuses: Extensive Review of the Last Decade of Research and Current Challenges

Smart Campuses: Extensive Review of the Last Decade of Research and Current Challenges

Hybrid Auto-Scaled Service-Cloud-Based Predictive Workload Modeling and Analysis for Smart Campus System

A Smart Campus Template

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