Distributed chemical sensor networks for environmental sensing

Society is increasingly accustomed to instant access to real-time information, due to the ubiquitous use of the internet and web-based access tools. Intelligent search engines enable huge data repositories to be searched, and highly relevant information returned in real time. These repositories increasingly include environmental information related to the environment, such as distributed air and water quality. However, while this information at present is typically historical, for example, through agency reports, there is increasing demand for real-time environmental data. In this paper, the issues involved in obtaining data from autonomous chemical sensors are discussed, and examples of current deployments presented. Strategies for achieving large-scale deployments are discussed

Behavioural pattern identification and prediction in intelligent environments

In this paper, the application of soft computing techniques in prediction of an occupant's behaviour in an inhabited intelligent environment is addressed. In this research, daily activities of elderly people who live in their own homes suffering from dementia are studied. Occupancy sensors are used to extract the movement patterns of the occupant. The occupancy data is then converted into temporal sequences of activities which are eventually used to predict the occupant behaviour. To build the prediction model, different dynamic recurrent neural networks are investigated. Recurrent neural networks have shown a great ability in finding the temporal relationships of input patterns. The experimental results show that non-linear autoregressive network with exogenous inputs model correctly extracts the long term prediction patterns of the occupant and outperformed the Elman network. The results presented here are validated using data generated from a simulator and real environments

Participatory sensing as an enabler for self-organisation in future cellular networks

In this short review paper we summarise the emerging challenges in the field of participatory sensing for the self-organisation of the next generation of wireless cellular networks. We identify the potential of participatory sensing in enabling the self-organisation, deployment optimisation and radio resource management of wireless cellular networks. We also highlight how this approach can meet the future goals for the next generation of cellular system in terms of infrastructure sharing, management of multiple radio access techniques, flexible usage of spectrum and efficient management of very small data cells

Remote real-time monitoring of subsurface landfill gas migration

The cost of monitoring greenhouse gas emissions from landfill sites is of major concern for regulatory authorities. The current monitoring procedure is recognised as labour intensive, requiring agency inspectors to physically travel to perimeter borehole wells in rough terrain and manually measure gas concentration levels with expensive hand-held instrumentation. In this article we present a cost-effective and efficient system for remotely monitoring landfill subsurface migration of methane and carbon dioxide concentration levels. Based purely on an autonomous sensing architecture, the proposed sensing platform was capable of performing complex analytical measurements in situ and successfully communicating the data remotely to a cloud database. A web tool was developed to present the sensed data to relevant stakeholders. We report our experiences in deploying such an approach in the field over a period of approximately 16 months

Air quality mapping and visualisation: An affordable solution based on a vehicle-mounted sensor network

This paper describes a prototype of the ExpoLIS system, which aims at: (1) informing citizens regarding the air quality of their surroundings and how to cope with it (e.g., choosing commuting routes according to a health model); and (2) gathering dense spatiotemporal air quality data to support the empirical work of environmental experts. The system is composed of: (1) an affordable and custom vehicle-mounted sensor network for air quality monitoring; (2) a server to store, process, and map all gathered geo-referenced sensory data; and (3) a set of user-centred visualisation and prediction services tailored for citizens and environmental experts. Experimental validation of each component of the proposed system shows that the current prototype is capable of tracking spatiotemporal air quality changes and of providing users with access to these events via a set of interfaces. The results show evidence of a strong correlation in static situations (R2 of 0.96 for PM2.5) between the proposed low-cost all-weather system and a high-cost equipment with no weather protection. The results also show a weaker correlation (R2 of 0.57 for PM2.5), but still satisfactory, in dynamic settings. In short, this paper presents experimental evidence that supports the claim that the ExpoLIS system is feasible and valuable to both citizens and environmental scientists.info:eu-repo/semantics/publishedVersio