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Real-time traffic monitoring is essential to the development of smart cities as well as its potential for energy savings. However, real-time traffic monitoring is a task that requires sophisticated and expensive hardware. Owing to the prohibitive cost of specialised sensors, accurate traffic counts are typically limited to intersections where traffic information is used for signalling purposes. The sparse arrangement of traffic detection points does not provide adequate information for intelligent lighting applications, such as adaptive dimming. This paper investigates the low-cost and off-the-shelf sensors to be installed inside street lighting luminaires for traffic sensing. A luminaire-mounted sensor test-bed installed on a moderately-busy road trialled three non-invasive presence-detection sensors: Passive Infrared (PIR), Sonar (UVD) and lidar. The proof-of-concept study revealed that a HC-SR501 PIR motion detector could count traffic with 73% accuracy at a low cost and may be suitable for intelligent lighting applications if accuracy can be further improved.

A set of reusable ontologies has been developed as separate components within a knowledge representation (KR) system, to generically model a reef system. The ontology design, ranging from light to heavyweight, aims to leverage from the scalable characteristics of semantic technologies to allow for flexibility when posing domain and locality-specific hypotheses, such as predicting coral bleaching. The Semantic Reef Project is an eco-informatics application designed to test ecological hypotheses to derive information about environmental systems. The intention is to develop an automated data processing, problem-solving, and knowledge discovery system that will assist in developing our understanding and management of coral reef ecosystems. Remote environmental monitoring (including sensor networks) is being widely developed and used for collecting real-time data across widely distributed locations. As the volume of raw data increases, it is envisaged that bottlenecks will develop in the data analysis phases because current data processing procedures still involve manual manipulation and will soon become unfeasible to manage. Ontologies provide a new approach and methodology for modulating this data overflow while also improving our ability to extract knowledge from the data collected.

Eco-informatics is the combination of multiple environmental datasets and modeling tools used to test ecological hypotheses and derive information. The Semantic Reef project is developing automated data processing, problem-solving and knowledge discovery systems to better understand and manage reef ecosystems. Three core tools are utilized; Semantic Web, Grid Computing and workflow based e-Research approaches, creating a platform designed to evaluate complex hypothesis queries and provide alerting for unusual events (e.g. spawning, bleaching). Remote environmental monitoring is being widely deployed to gather data in real-time. As the volume of raw data increases, bottlenecks are expected to develop in the data analysis phases - current data processing still involves human intervention and manual manipulation. Semantic Web technologies address this data deluge through using explicit descriptions, called ontologies, of the datasets and structures, making the data machine-understandable, therefore automating data integration and processing. The Semantic Reef project is focused on developing reef ontologies, which, when coupled to datasets, derives inferences from data to 'ask' the system questions for semantic correlation and analysis. Currently, the model is being extended to map dynamic data from reef-based sensor-networks into the ontology in real-time, offering a new approach to solving problems of scale across reefs.

A semantically driven Virtual Organisation (VO) model for predicting important events for coral reefs is presented – the Semantic Reef. The model is an application of a Semantic Grid that encapsulates services and resources to produce a complex higher-level knowledge base in order to predict coral bleaching on the Great Barrier Reef. The goal is to enable the synergistic interplay between different e-Research tools to produce a model that can evaluate complex hypotheses. Built on a Grid infrastructure and invoking an ontology to map to currently non-communicative datasets, the Semantic Reef model is designed to address specific scientific problems such as the prediction of coral bleaching events. The outcome is not only a model for exploring a diverse range of e-Research challenges, but also potentially a re-usable interoperable knowledge base can then be accessed as a resource in other VOs.