| Macquarie University - School of Computing | Smart Energy Monitoring | 2018 - 2020 | Smart energy monitoring is a system that allows individuals or businesses to track and manage their energy usage in real-time. This is typically done through the use of smart meters, which are devices that can measure and transmit information about energy usage to a central system. The data collected by these meters can then be analyzed to identify patterns and trends in energy usage, which can be used to optimize energy consumption and reduce costs. Smart energy monitoring systems can also be integrated with home automation systems and appliances, allowing users to control their energy usage remotely and set automated energy-saving routines. | Datasets |
| Macquarie University - School of Computing | Smart Irrigation | 2018 - 2020 | Smart irrigation for indoor plants refers to the use of technology to automatically and efficiently water indoor plants, such as those in greenhouses, offices, or homes. These systems can track the moisture levels of the soil and adjust the watering schedule accordingly. They can also take into account factors such as temperature, light levels, and plant species to determine the optimal amount of water and the best time to water.
Some examples of smart irrigation systems for indoor plants include self-watering pots, which have a built-in water reservoir and a wick that delivers water to the soil as needed. Another example is the use of sensors that monitor the moisture levels in the soil, and then sends a message to the user or to a control system to remind them to water the plants.
Smart irrigation systems for indoor plants can be controlled through a mobile app or a central computer, allowing users to monitor and adjust the watering schedule from anywhere. They can also be integrated with other smart home systems, such as voice assistants, to allow for voice control of the irrigation system.
Overall, the use of smart irrigation systems for indoor plants can help to conserve water, reduce the risk of overwatering or underwatering, and improve the health and growth of the plants. | Datasets |
| Macquarie University - School of Computing | Smart Parking for Disabled People | 2018 - 2020 | Smart parking for disabled parking refers to the use of technology to improve the accessibility, availability and management of disabled parking spaces in Macquarie University’s campus. These systems may include a combination of sensors, cameras, and software to detect the occupancy of a parking space and to reserve or direct the disabled driver to an available spot.
Examples of smart parking systems for disabled parking include:
● Smart parking meters: These meters can detect the presence of a vehicle and can reserve the parking space for a specific amount of time. They can also be controlled remotely, allowing for the reservation and payment of a parking space via a mobile app.
● Smart parking guidance systems: These systems use sensors and cameras to detect the occupancy of parking spaces and to guide the driver to an available spot. They can also display real-time information about the availability of disabled parking spaces on a digital sign or through a mobile app.
● Smart parking reservation systems: These systems allow disabled drivers to reserve a parking space in advance, using a mobile app or website. This can ensure that a space will be available when they arrive, reducing the need to search for a spot.
Overall, smart parking systems for disabled parking can improve the accessibility and availability of parking spaces for individuals with disabilities, making it easier for them to access buildings, businesses and other facilities. | Datasets |
| Macquarie University - School of Computing | Smart City Project | 2018 - 2020 | The Macquarie Park District is of considerable economic importance to New South Wales and to Australia. It forms a critical part of the Global Economic Corridor where Sydney’s knowledge jobs are concentrated. Over the past two decades, the Macquarie Park District has experienced considerable growth of office development and is expected to continuingly expand as a commercial office centre well into the future, with or without changes to planning controls. Without such joined-up policy and implementation frameworks, urban renewal projects can place enormous stress on the precinct, commuters and inhabitants whilst also leading to costly and ineffective solutions.
With a funding from Australian Government’s Smart Cities and Suburbs Program, and partner up with the City of Ryde Council, Macquarie University has developed a pedestrian counting system which can help visualise and test assumptions before and during such stress. The system compiles real-time data from Macquarie University’s Internet-of-Things pedestrian sensors to better understand pedestrian activity within the Macquarie Park District. n The resulting data acquired from the system is available as this visualisation tool. This online tool can be used to:
● Observe a representation of pedestrian traffic on any given day and time
● Compare averages of the same day and time over the preceding four weeks
● Ability to see impact of major events or extreme weather conditions on pedestrian traffic
● Download the data bank for analysis or for use in own visualisation tools
System Information
The system consists of Internet-of-Things sensors installed in various key locations across the District, a wireless data transmission system, a server and a visualisation tool. | Datasets |
| University of Queensland | e-Nose (smell detection for the Cancer of stomach) | 2018 - 2020 | The objective of this project is to Use E-nose devices and data processing units to create a platform for sharing
collected data over the web with Australian researchers. The dataset will be collected from anonymous non-
cancer individuals and positive cancer patients identified at the Royal Brisbane & Women's Hospital. The
collected data will be used to develop an Exhaled Breath Analysis System for non-invasive health care
monitoring. The system will combine a laboratory-based sensor module (Electronic Nose), pattern recognition
subsystem, and non-invasive sampling for rapid and low-cost lung cancer diagnostics. The specific targets of
this project are to:
Provide a dataset for machine learning on a low-cost and rapid intelligent lung cancer diagnostic system.
Make lung cancer diagnostic data freely available to researchers nationwide.
Encompass a diverse and large population of breath analysis records. | Datasets |
| University of Adelaide | EdgeBC Dataset | | The primary purpose of the Adelaide IoT Testbed is enabling researchers to deploy ad-hoc IoT testkits and operate their workload (e.g., ML, data processing, blockchain) in both lab-based and real-world settings. The Edge BC dataset is an exemplary result of such studies.
The Edge BC dataset contains a lab-based reproduction of a deployment of the IoT testbed hardware within a search and rescue exercise in 2019. The IoT testbed gathers observation data via wireless sensors and commits them to a blockchain that operates within the testbed infrastructure itself, providing information assurance and provenance to the sensor data. The goal of the research was to quantify the performance and resource consumption of such blockchain configuration. The performance metrics of the blockchain and the resource load of the underlying IoT infrastructure form the Edge BC dataset. | Datasets |
| University of Adelaide | Single Channel Edge Blockchain | | The Adelaide IoT testbed is an extensible collection of adaptable and configurable test kits, comprising fog and edge computing devices. These test kits resemble ad-hoc computing infrastructures that are rapidly assembled in the field for purposes such as emergency response after disasters that might have rendered the existing infrastructure inoperable. Such ad-hoc infrastructures can be used to operate and provide computing services to IoT sensors and autonomous systems.
Leveraging blockchain within and across such ad-hoc infrastructures is an emerging trend for securing the related IoT data and services. However, it is unknown the extent to which ad-hoc IoT-centric infrastructures can support a blockchain network.
This project aims to empirically evaluate the performance and resource consumption of operating a blockchain upon an ad-hoc infrastructure. We focus on single-channel blockchains, meaning all participants jointly maintain a single set of records. We benchmarked multiple topology, deployment structure, and protocol configurations. | Datasets |
| University of Adelaide | Multi-Channel Edge Blockchain | | Datasets of performance and resource consumption metrics of IoT infrastructure operating multiple blockchain channels | MultiChannelBC |
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