title: Semantic In-Network Complex Event Processing for an Energy Efficient Wireless Sensor Network

authors: Kasi, Mumraiz Khan

subjects: Wireless Sensor Networks

abstract: Wireless Sensor Networks (WSNs) consist of spatially distributed sensor nodes that perform monitoring tasks in a region and the gateway nodes that provide the acquired sensor data to the end user. With advances in the WSN technology, it has now become possible to have different types of sensor nodes within a region to monitor the environment. This provides the flexibility to monitor the environment in a more extensive manner than before.

Sensor nodes are severely constrained devices with very limited battery sources and their resource scarcity remains a challenge. In traditional WSNs, the sensor nodes are used only for capturing data that is analysed later in more powerful gateway nodes. This continuous communication of data between sensor nodes and gateway nodes wastes energy at the sensor nodes, and consequently, the overall network lifetime is greatly reduced. Existing approaches to reduce energy consumption by processing at the sensor node level only work for homogeneous networks.

This thesis presents a sensor node architecture for heterogeneous WSNs, called SEPSen, where data is processed locally at the sensor node level to reduce energy consumption. We use ontology fragments at the sensor nodes to enable data exchange between heterogeneous sensor nodes within the WSN. We employ a rule engine based on a pattern matching algorithm for filtering events at the sensor node level. The event routing towards the gateway nodes is performed using a context-aware routing scheme that takes both the energy consumption and the heterogeneity of the sensor nodes into account.

As a proof of concept, we present a prototypical implementation of the SEPSen design in a simulation environment. By providing semantic support, in-network data processing capabilities and context-aware routing in SEPSen, the sensor nodes (1) communicate with each other despite their different sensor types, (2) filter events at the their own level to conserve the limited sensor node energy resources and (3) share the nodes' knowledge bases for collaboration between the sensor nodes using node-centric context-awareness in changing conditions. The SEPSen prototype has been evaluated based on a test case for water quality management. The results from the experiments show that the energy saved in SEPSen reaches almost 50% by processing events at the sensor node level and the overall network lifetime is increased by at least a factor of two against the shortest-path-first (Min-Hop) routing approach.

text: Semantic In-Network Complex Event Processing for an Energy Efficient Wireless Sensor Network Wireless Sensor Networks (WSNs) consist of spatially distributed sensor nodes that perform monitoring tasks in a region and the gateway nodes that provide the acquired sensor data to the end user. With advances in the WSN technology, it has now become possible to have different types of sensor nodes within a region to monitor the environment. This provides the flexibility to monitor the environment in a more extensive manner than before.

Sensor nodes are severely constrained devices with very limited battery sources and their resource scarcity remains a challenge. In traditional WSNs, the sensor nodes are used only for capturing data that is analysed later in more powerful gateway nodes. This continuous communication of data between sensor nodes and gateway nodes wastes energy at the sensor nodes, and consequently, the overall network lifetime is greatly reduced. Existing approaches to reduce energy consumption by processing at the sensor node level only work for homogeneous networks.

This thesis presents a sensor node architecture for heterogeneous WSNs, called SEPSen, where data is processed locally at the sensor node level to reduce energy consumption. We use ontology fragments at the sensor nodes to enable data exchange between heterogeneous sensor nodes within the WSN. We employ a rule engine based on a pattern matching algorithm for filtering events at the sensor node level. The event routing towards the gateway nodes is performed using a context-aware routing scheme that takes both the energy consumption and the heterogeneity of the sensor nodes into account.

As a proof of concept, we present a prototypical implementation of the SEPSen design in a simulation environment. By providing semantic support, in-network data processing capabilities and context-aware routing in SEPSen, the sensor nodes (1) communicate with each other despite their different sensor types, (2) filter events at the their own level to conserve the limited sensor node energy resources and (3) share the nodes' knowledge bases for collaboration between the sensor nodes using node-centric context-awareness in changing conditions. The SEPSen prototype has been evaluated based on a test case for water quality management. The results from the experiments show that the energy saved in SEPSen reaches almost 50% by processing events at the sensor node level and the overall network lifetime is increased by at least a factor of two against the shortest-path-first (Min-Hop) routing approach.

year: 2015

title: A novel approach to investigate depression symptoms in the ageing population using Generalizability theory

authors: Saiji Kumar, Sruthy

subjects: Depression

abstract: As depression is common in older people and confers a significant risk for dementia, its accurate assessment is essential to monitor and treat the condition. The 15-item Geriatric Depression Scale (GDS-15) is a widely used assessment tool for measuring depression in aged populations, and its psychometric properties have been recently improved using Rasch analysis. However, its temporal reliability and ability to distinguish between dynamic and enduring symptoms of depression have not been examined using the appropriate methodology. Distinguishing enduring aspects of depression helps to estimate the risks of depression and the long-lasting effects of an intervention while identifying and targeting dynamic symptoms may enhance the efficiency of a treatment. Generalizability theory (G-theory) is a suitable method to distinguish between enduring and dynamic symptoms of depression, evaluate the overall reliability of the GDS-15, and identify sources of measurement error. This study applied G-theory to the longitudinal GDS-15 data of 354 participants aged 70 years and older from the Sydney Memory and Ageing Study, collected over ten years with 2-4 years intervals. The GDS-15 demonstrated strong reliability and generalizability of its scores across the sample population and assessment occasions in measuring enduring symptoms of depression (Ga= 0.82, Gr=0.90). In addition, three dynamic symptoms of depression were identified, namely helplessness, hopelessness, and loss of interest in activities, which did not affect the overall strong reliability of the GDS-15. This study demonstrated that the GDS-15 is a reliable measure for assessing enduring symptoms of depression and can be used to evaluate the efficacy of depression treatments and monitor depression levels in older adults. Dynamic symptoms identified by this study are more amendable and can be targeted in the first place to enhance effectiveness of a treatment.

text: A novel approach to investigate depression symptoms in the ageing population using Generalizability theory As depression is common in older people and confers a significant risk for dementia, its accurate assessment is essential to monitor and treat the condition. The 15-item Geriatric Depression Scale (GDS-15) is a widely used assessment tool for measuring depression in aged populations, and its psychometric properties have been recently improved using Rasch analysis. However, its temporal reliability and ability to distinguish between dynamic and enduring symptoms of depression have not been examined using the appropriate methodology. Distinguishing enduring aspects of depression helps to estimate the risks of depression and the long-lasting effects of an intervention while identifying and targeting dynamic symptoms may enhance the efficiency of a treatment. Generalizability theory (G-theory) is a suitable method to distinguish between enduring and dynamic symptoms of depression, evaluate the overall reliability of the GDS-15, and identify sources of measurement error. This study applied G-theory to the longitudinal GDS-15 data of 354 participants aged 70 years and older from the Sydney Memory and Ageing Study, collected over ten years with 2-4 years intervals. The GDS-15 demonstrated strong reliability and generalizability of its scores across the sample population and assessment occasions in measuring enduring symptoms of depression (Ga= 0.82, Gr=0.90). In addition, three dynamic symptoms of depression were identified, namely helplessness, hopelessness, and loss of interest in activities, which did not affect the overall strong reliability of the GDS-15. This study demonstrated that the GDS-15 is a reliable measure for assessing enduring symptoms of depression and can be used to evaluate the efficacy of depression treatments and monitor depression levels in older adults. Dynamic symptoms identified by this study are more amendable and can be targeted in the first place to enhance effectiveness of a treatment.

year: 2022

title: Tāwharautia Aotearoa

authors: Maxwell, Te Kahautu

abstract: Tāwharautia Aotearoa was composed in opposition to the National Governments attempt to remove Section 9 of the Principles of the Treaty of Waitangi which would give the Government power to sell State assets. Performed by Ōpōtiki Mai Tawhiti at the Te Matatini World Championship Kapa Haa Competition 2013.

text: Tāwharautia Aotearoa Tāwharautia Aotearoa was composed in opposition to the National Governments attempt to remove Section 9 of the Principles of the Treaty of Waitangi which would give the Government power to sell State assets. Performed by Ōpōtiki Mai Tawhiti at the Te Matatini World Championship Kapa Haa Competition 2013.

year: 2013