2017 transformation projects
Smart Parks: Making public parks work effectively for communities and the environment
To assist planners and local authorities in optimising the design and use of public parks, this project was focussed on the development of a preliminary index for assessing park design and functionality on several dimensions, including environmental sustainability, amenity, quality, accessibility, levels of interaction and social cohesion. The Smart Park Index will rate how well a park’s design meets the needs of its local physical environment and neighbourhood context.
Self-powered Monash IoT Sensor Network for Air Quality Measurement in Buildings and Outdoor
This project was directed to develop a Monash IoT (Internet-of-Things) sensor platform for environmental monitoring. The platform will be able to monitor environmental and air quality parameters such as temperature, UV, humidity, PM (Particulate matter), CO2 and smoke, which are key variables that need to be collected to support the sustainable design and critical infrastructure in urban areas. The long-term goal of this project is to provide essential information on the micro-climate of urban environments for a sustainable and smart development of cities.
The Advanced Electric Bus System
Current battery electric buses replicate diesel characteristics and are thus flawed; not capitalising on the opportunities of an Advanced Electric Bus System. This project explores far beyond the vehicle into a complete, advanced, system.
Design of next generation green desalination infrastructure
This project was aimed to achieve the development of rigorous decision support tools for design, integration, planning, and scheduling of desalination infrastructure to produce low cost desalinated water.
Supply chain optimization: hierarchal sales time series clustering-assisted forecasting in a real-world big data environment
Sales forecasting is an important task in supply chain optimisation across many industries. There are several challenges associated to the current method used in this field. This project combines hierarchical time series forecasting with state-of-the-art clustering techniques to address these issues to enable better sales forecasting.