Sensor networks are currently being trialed by the water distribution industry for monitoring complex distribution infrastructure. The paper presents an investigation in to the architecture and performance of a sensor system deployed for monitoring such a distribution network. The study reveals lapses in systems design and management, resulting in a fifth of the data being either missing or erroneous. Findings identify the importance of undertaking in-depth consideration of all aspects of a large sensor system with access to either expertise on every detail, or to reference manuals capable of transferring the knowledge to non-specialists. First steps towards defining a set of such guidelines are presented here, with supporting evidence
The water supply industry is trialing a range of sensor network designs for monitoring distributed infrastructure. The paper investigates the performance of such a sensor system deployed to monitor a water distribution network. The study reveals up to one fifth of the data intended to be collected either to be missing or erroneous. Findings reinforce the importance of in-depth design consideration of all aspects of large scale sensor systems, and the necessity for expertise on every detail of the system, or access to a rule set which embeds this knowledge allowing non-specialists to make near optimal choices. First steps towards defining such a rule set is presented here with supporting evidence
Distributed sensor networks are used for asset monitoring within industrial environments. Energy efficient operation of such systems is an important characteristic. Mobile data collectors have been proposed as an approach for reducing energy requirements of data dissemination. Macro level optimisation techniques for selecting the overall best route for data collection exists, but they often oversee dynamic localised conditions which also need to be considered.A novel localised optimisation technique designed to be used in conjunction with existing global path strategies is presented here. The proposed technique considers dynamic radio conditions and optimises localised collection strategy accordingly. This is achieved using a Naive Bayesian Classifier trained to detect and react to different radio conditions observed through onboard diagnostics.The approach is evaluated using a series of real world experiments. Results indicate localised optimisation to provide measurable improvements, especially under complex and unpredictable radio conditions riddled with path loss, reflection, and noise. Incidentally these are characteristics which can be typical expected within industrial environments.
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