HR: 08:50h
AN: H51N-04 INVITED    [Abstracts]
TI: Realtime Delivery of Alarms and Key Observables in a Deployed Hydrological Sensor Network
AU: * Marshall, I W
EM: i.w.marshall@lancaster.ac.uk
AF: Lancaster Environment Centre, Lancaster University, Lancaster, LA1 4YQ, United Kingdom
AU: Price, M C
EM: pricemc@comp.lancs.ac.uk
AF: Infolab21 (Computer Science), Lancaster University, Lancaster, LA1 4YQ, United Kingdom
AU: Li, H
EM: h.li@lancaster.ac.uk
AF: Infolab21 (Computer Science), Lancaster University, Lancaster, LA1 4YQ, United Kingdom
AU: Boyd, N
EM: n.boyd@salamander-group.co.uk
AF: Salamander Group, Williams House Manchester Science Park, Manchester, M15 6SE, United Kingdom
AU: Boult, S
EM: s.boult@manchester.ac.uk
AF: School of Earth, Atmospheric and Environmental Sciences, Manchester University, Manchester, M60 1QD, United Kingdom
AB: It has widely [1-3] been proposed that sensor networks are a good solution for environmental monitoring. However, this application presents a number of major challenges for current technology. In particular environmental science involves the study of coupled non-equilibrium dynamic processes that generate time series with non-stationary means and strongly dependent variables and which operate in the presence of large amounts of noise/interference (thermal, chemical and biological) and multiple quasi-periodic forcing factors (diurnal cycles, tides, etc). This typically means that any analysis must be based on large data samples obtained at multiple scales of space and time. In addition the areas of interest are large, relatively inaccessible and typically extremely hostile to electronic instrumentation. Our analysis of these factors has encouraged us to focus on this list of generic requirements; a) Node lifetime (between visits) should be 1 yr or greater b) Communication range should be ~250m c) Nodes should be portable, unobtrusive, low cost, etc. d) Networks are expected to be sparse since areas of interest are large and budgets are small However, the characteristics of each environment, the dominant processes operating in it and the measurements that are of interest are sufficiently different that the design of an appropriate sensor network solution is normally most determined by site specific constraints. Most importantly the opportunities for exploiting contextual correlation to disambiguate observations and improve the maintenance and robustness of a deployed sensor network are always site specific. We will describe the design and initial deployment of a hydrological sensor network we are developing to assess the hydro-dynamics of surface water drainage into Great Crowden Brook in the Peak District (UK). The complete network will observe soil moisture, temperature and rainfall on a number of transects across the valley, and will also investigate water quality parameters (colouration, turbidity, Ph) in the stream. GSM access for remote real time reporting of network status is only available from the hilltops so a multihop communication strategy is being used for communication from the valley floor. To minimise radio usage and maximise battery life we are reporting only those alarms and events that are judged to be of high priority by embedding a simple rule based decision engine in each node. The rule conditions are derived from spatio-temporal cross-correlation of the available sensor inputs. . We report on our initial experiments with correlating readings for management purposes, and offer some initial hypotheses regarding aspects of this that might be generic based on a comparison with data obtained in an earlier experiment in a marine setting [4]. 1. http://cens.ucla.edu/ 2. http://eyes.eu.org/ 3. http://www.ee.unimelb.edu.au/ISSNIP/ 4. J. Tateson, C. Roadknight, A. Gonzalez, T. Khan, S. Fitz, I. Henning, N. Boyd, C. Vincent, and I. W. Marshall. Real World Issues in Deploying a Wireless Sensor Network. In Workshop on Real-World Wireless Sensor Networks REALWSN'05, Stockholm, Sweden, June 2005
DE: 0555 Neural networks, fuzzy logic, machine learning
DE: 1804 Catchment
DE: 1848 Monitoring networks
DE: 1895 Instruments and techniques: monitoring
SC: Hydrology [H]
MN: 2007 Fall Meeting