title: Measurement and Applications: Exploring the Challenges and Opportunities of Hierarchical Federated Learning in Sensor Applications

authors: Ooi, Melanie Po-Leen

abstract: Sensor applications have become ubiquitous in modern society as the digital age continues to advance. AI-based techniques (e.g., machine learning) are effective at extracting actionable information from large amounts of data. An example would be an automated water irrigation system that uses AI-based techniques on soil quality data to decide how to best distribute water. However, these AI-based techniques are costly in terms of hardware resources, and Internet-of-Things (IoT) sensors are resource-constrained with respect to processing power, energy, and storage capacity. These limitations can compromise the security, performance, and reliability of sensor-driven applications. To address these concerns, cloud computing services can be used by sensor applications for data storage and processing. Unfortunately, cloud-based sensor applications that require real-time processing, such as medical applications (e.g., fall detection and stroke prediction), are vulnerable to issues such as network latency due to the sparse and unreliable networks between the sensor nodes and the cloud server [1]. As users approach the edge of the communications network, latency issues become more severe and frequent. A promising alternative is edge computing, which provides cloud-like capabilities at the edge of the network by pushing storage and processing capabilities from centralized nodes to edge devices that are closer to where the data are gathered, resulting in reduced network delays [2], [3].

text: Measurement and Applications: Exploring the Challenges and Opportunities of Hierarchical Federated Learning in Sensor Applications Sensor applications have become ubiquitous in modern society as the digital age continues to advance. AI-based techniques (e.g., machine learning) are effective at extracting actionable information from large amounts of data. An example would be an automated water irrigation system that uses AI-based techniques on soil quality data to decide how to best distribute water. However, these AI-based techniques are costly in terms of hardware resources, and Internet-of-Things (IoT) sensors are resource-constrained with respect to processing power, energy, and storage capacity. These limitations can compromise the security, performance, and reliability of sensor-driven applications. To address these concerns, cloud computing services can be used by sensor applications for data storage and processing. Unfortunately, cloud-based sensor applications that require real-time processing, such as medical applications (e.g., fall detection and stroke prediction), are vulnerable to issues such as network latency due to the sparse and unreliable networks between the sensor nodes and the cloud server [1]. As users approach the edge of the communications network, latency issues become more severe and frequent. A promising alternative is edge computing, which provides cloud-like capabilities at the edge of the network by pushing storage and processing capabilities from centralized nodes to edge devices that are closer to where the data are gathered, resulting in reduced network delays [2], [3].

year: 2023-12-01

title: Urban water metabolism information for planning water sensitive city-regions

authors: Serrao-Neumann, Silvia

subjects: Science & Technology

abstract: Climate change and growing populations will stretch water resources in many city-regions globally, and urbanisation will continue to degrade water quality and upset natural hydrological flows. These pressures call for alternative urban water management approaches with improved connection with land use planning. Evaluating the water metabolism of urban areas gives a holistic picture of how water flows through and is transformed by urban settlements, to inform land use planning for sustainably managing urban water. Previous research has conceptualised how metabolism science may inform urban land use planning. In this work, we build on to identify how urban water metabolism evaluations can inform urban planning practice. We ask, ‘how can urban water metabolism evaluations support urban and water planning towards water sensitive city-regions?’ Focusing on three Australian capital city-regions, we empirically identify the knowledge needs of practitioners and compare this against the knowledge known to be generated from past urban water metabolism evaluations. This was done within a framework of urban water resource management objectives for water sensitive cities - that is, protection of water resources and hydrological flows, recognition of the diverse functions of water, and resource efficiency and supply internalisation. Based on the findings, the paper discusses five key strategic initiatives for planning for water sensitive city-regions: resource efficiency and hydrological performance benchmarks and targets for urban developments, tailoring programmes for resource efficiency, making case for regional blue-green space networks for improved hydrological performance, small and large-scale infrastructure innovation, and social and institutional innovation in urban water management.

text: Urban water metabolism information for planning water sensitive city-regions Climate change and growing populations will stretch water resources in many city-regions globally, and urbanisation will continue to degrade water quality and upset natural hydrological flows. These pressures call for alternative urban water management approaches with improved connection with land use planning. Evaluating the water metabolism of urban areas gives a holistic picture of how water flows through and is transformed by urban settlements, to inform land use planning for sustainably managing urban water. Previous research has conceptualised how metabolism science may inform urban land use planning. In this work, we build on to identify how urban water metabolism evaluations can inform urban planning practice. We ask, ‘how can urban water metabolism evaluations support urban and water planning towards water sensitive city-regions?’ Focusing on three Australian capital city-regions, we empirically identify the knowledge needs of practitioners and compare this against the knowledge known to be generated from past urban water metabolism evaluations. This was done within a framework of urban water resource management objectives for water sensitive cities - that is, protection of water resources and hydrological flows, recognition of the diverse functions of water, and resource efficiency and supply internalisation. Based on the findings, the paper discusses five key strategic initiatives for planning for water sensitive city-regions: resource efficiency and hydrological performance benchmarks and targets for urban developments, tailoring programmes for resource efficiency, making case for regional blue-green space networks for improved hydrological performance, small and large-scale infrastructure innovation, and social and institutional innovation in urban water management.

year: 2019

title: Waitangi Soda Springs alum dosing – Bioavailability of aluminium 2011-2020

authors: Ling, Nicholas

abstract: Continuous alum dosing of the Waitangi Soda Springs at Lake Rotoehu commenced in 2011 to reduce inflows of dissolved reactive phosphorus to Lake Rotoehu. Analyses of bioaccumulation in the tissues of kōura and goldfish from Lake Rotoehu were undertaken on animals collected annually since 2013 to determine the bioavailability and bioaccumulation of aluminium. Lake Rotoehu kōura and goldfish collected in 2013 showed slightly elevated aluminium concentrations in the gills indicating enhanced exposure to aluminium but little evidence for significant bioaccumulation in tissues. Concentrations in goldfish liver and gills were highest in 2016, but not significantly different to goldfish collected from Lake Rotorua in 2013 and flesh concentrations were low. Analyses of animals collected in 2020 confirm these earlier findings with no evidence of enhanced bioaccumulation of aluminium in Lake Rotoehu biota.

text: Waitangi Soda Springs alum dosing – Bioavailability of aluminium 2011-2020 Continuous alum dosing of the Waitangi Soda Springs at Lake Rotoehu commenced in 2011 to reduce inflows of dissolved reactive phosphorus to Lake Rotoehu. Analyses of bioaccumulation in the tissues of kōura and goldfish from Lake Rotoehu were undertaken on animals collected annually since 2013 to determine the bioavailability and bioaccumulation of aluminium. Lake Rotoehu kōura and goldfish collected in 2013 showed slightly elevated aluminium concentrations in the gills indicating enhanced exposure to aluminium but little evidence for significant bioaccumulation in tissues. Concentrations in goldfish liver and gills were highest in 2016, but not significantly different to goldfish collected from Lake Rotorua in 2013 and flesh concentrations were low. Analyses of animals collected in 2020 confirm these earlier findings with no evidence of enhanced bioaccumulation of aluminium in Lake Rotoehu biota.

year: 2021