A smart campus design: data-driven and evidence-based decision support solution design

The growth and the availability of the smart devices is becoming ubiquitous today and inter-networking of these devices make up what is commonly called the Internet of Things (IoT). IoT is being used to update, enhance, simplify and automate individual lives and communities. Most of the cities in general and universities in special are adopting IoT technologies in order to create a smart sustainable living and working environments. Based on the existing literature of smart campus domain, it can be observed that there is only a small number of models as such. This study attempts to bridge the following knowledge gaps of smart campus domain.

This project falls into the concept of Smart Campus and aims to design a Smart Campus solution for Staffordshire University. The primarily goal is to design a solution architecture able to collect data from remote sensor networks and analyse them with the support of data analytics and machine learning techniques for sound business decision making. The project has two stages. The first stage is the business side of the project where a business requirement study has been done to extract the exact business requirements and once this complete the second stage was the technical implementation of one or many requirements and evaluation of the solution. The scope of this paper limits to the first stage of the project.

A quantitative approach was chosen by considering the nature of this study. A self-administered online questionnaire was developed around several key challenges and directed especially to the staff members, in order to identify what are the expectations of university staff in relation to thematic topics. Subsequently, business requirements under each key challenge were ranked based on MoSCoW prioritisation method. Energy management, space utilisation and occupancy, cleanliness recognition, smarter car parking, internet enabled café, network and physical security and environment (temperature) control are the key business challenges identified. Moreover, intended system qualities and specific project benefits were also identified to scope the project well.