Dian Zhang

A low-cost autonomous optical sensor for water quality monitoring.

A low-cost optical sensor for monitoring the aquatic environment is presented, with the construction and design described in detail. The autonomous optical sensor is devised to be environmentally robust, easily deployable and simple to operate. It consists of a multi-wavelength light source with two photodiode detectors capable of measuring the transmission and side-scattering of the light in the detector head. This enables the sensor to give qualitative data on the changes in the optical opacity of the water. Laboratory tests to confirm colour and turbidity-related responses are described and the results given. The autonomous sensor underwent field deployments in an estuarine environment, and the results presented here show the sensors capacity to detect changes in opacity and colour relating to potential pollution events. The application of this low-cost optical sensor is in the area of environmental pollution alerts to support a water monitoring programme, where multiple such sensors could be deployed as part of a network.

ColiSense, today's sample today: A rapid on-site detection of β-D-Glucuronidase activity in surface water as a surrogate for E. coli.

A sensitive field-portable fluorimeter with incubating capability and triplicate sample chambers was designed and built. The system was optimised for the on-site analysis of E. coli in recreational waters using fluorescent based enzyme assays. The target analyte was β-D-Glucuronidase (GUS) which hydrolyses a synthetic substrate 6-Chloro-4-Methyl-Umbelliferyl-β-D-Glucuronide (6-CMUG) to release the fluorescent molecule 6-Chloro-4-Methyl-Umbelliferyl (6-CMU). The system was calibrated with 6-CMU standards. A LOD of 5 nM and a resolution of less than 1 nM was determined while enzyme kinetic tests showed detection of activities below 1 pmol min(-1) mL(-1) of sample. A field portable sample preparation, enzyme extraction protocol and continuous assay were applied with the system to analyse freshwater and marine samples. Results from a one day field trial are shown which demonstrated the ability of the system to deliver results on-site within a 75 min period.

Smart multi-modal marine monitoring via visual analysis and data fusion

Estuaries and coastal areas contain increasingly exploited resources that need to be monitored, managed and protected efficiently and effectively. This requires access to reliable and timely data and management decisions must be based on analysis of collected data to avoid or limit negative impacts. Visually supported multi-modal sensing and data fusion offer attractive possibilities for such arduous tasks. In this paper, we demonstrate how an in-situ sensor network can be enhanced with the use of contextual image data. We assimilate and alter a state-of-the-art background modelling technique from the image processing domain in order to detect turbidity spikes in water quality sensor measurements automatically. We then combine this with visual sensing to identify abnormal events that are not caused by local activities. The system can potentially assist those charged with monitoring large scale ecosystems, combining real-time analytics with improved efficiency and effectiveness.

Multi-modal sensor networks for more effective sensing in Irish coastal and freshwater environments

The worlds oceans represent a vital resource to global economies and there exists huge economic opportunity that remains unexploited. However along with this huge potential there rests a responsibility into understanding the effects various developments may have on our natural ecosystem. This along with a variety of other issues necessitates a need for continuous and reliable monitoring of the marine and freshwater environment. The potential for innovative technology development for marine and freshwater monitoring and knowledge generation is huge and recent years have seen huge leaps forward in relation to the development of sensor technology for such purposes. However despite the advancements there are still a number of issues. In our research we advocate a multi-modal approach to create smarter more efficient monitoring networks, while enhancing the use of in-situ wireless sensor networks (WSNs). In particular we focus on the use of visual sensors, modelled outputs and context information to support a conventional in-situ wireless sensor network creating a multi-modal environmental monitoring network. Here we provide an overview of a selection of our work in relation to the use of visual sensing through networked cameras or satellite imagers in three very diverse test sites - a river catchment, a busy port and a coastal environment.

Understanding packet loss for sound monitoring in a smart stadium IoT testbed

The Smart Stadium for Smarter Living project provides an end-to-end testbed for IoT innovation through a collaboration between Croke Park Stadium in Dublin, Ireland and Dublin City University, Intel and Microsoft. This enables practical evaluations of IoT solutions in a controlled environment that is small enough to conduct trials but large enough to prove and challenge the technologies. An evaluation of sound monitoring capabilities during the 2016 sporting finals was used to test the capture, transfer, storage and analysis of decibel level sound monitoring. The purpose of the evaluation was to use existing sound level microphones to measure crowd response to pre-determined events for display on big screens at half-time and to test the end-to-end performance of the testbed. While this is not the specific original purpose of the sound level microphones, it provided a useful test case and produced engaging content for the project. Analysis of the data streams showed significant packet loss during the events and further investigations were conducted to understand where and how this loss occurred. This paper describes the smart stadium testbed configuration using Intel gateways linking with the Azure cloud platform and analyses the performance of the system during the sound monitoring evaluation.