Proactive monitoring in natural environments Ian Marshall, Computing Laboratory, University of Kent Technical Director of the Envisense Research centre

Current research methods Single expensive package In situ process studies Low spatial resolution Short lifetime Small areas

Wireless Sensor networks Ad-hoc wireless communication Physical measurement No access to mains Large area (sq kms) Long life (months) Many measurement points

WSN management Low probability of manual intervention Highly dynamic, unpredictable environment Very unreliable nodes and comms Need to automate response to events model free adaptive control

Peak district Experiments

Floodnet

SECOAS Scroby sands wind farm and its impact on sedimentation processes

CEFAS Survey April 2002

Mechanical General Arrangement Buoy (yellow) Radio equipment Data cable Warp Chain Warp Plough anchor

Real trial Oct-Nov 2004

Initial Deployment Areas 1 NM 6 Sensors 150m apart Shore station

Seabed Package Measure Oceanographic variables (15 minute cycle) Temperature (1 sample/min) Pressure (1 sample/s for 5 mins) Turbidity (10 samples/min) Tilt (aka current) - (1 sample/s for 5 mins) Conductivity (1 sample/min) Adapt sampling rates Adaptively log data Transmit selected data to radio buoy

Adaptive sampling Measure, delete, combine, forward, sleep Use local variability, neighbour variability and internal state Self configure using distributed evolutionary algorithm (bacteria) Can adjust priorities and frequency of actions Can form groups (quorum sensing) Reward set by user using a diffusion (gossip) protocol – changes drive auto- reconfiguration of genome

QoS on a Sensor Network

Processing

Summary Autonomous adaptive control is needed in environmental sensor networks Network protocols must support and respond to application semantics (be app aware) In simulation adaptation was almost as good as optimal sliding window In practice it dealt well with change from calm to stormy More research will be needed