1. Information Agents for Autonomous Acquisition of Sensor Network Data A. Rogers and N. R. Jennings University of Southampton, UK M. A. Osborne and S. J. Roberts University of Oxford, UK

2. Provide situational awareness support to first responders at the scene of a disaster Use sensor networks to perform this perception –Weather, temperature, visibility, noise, gas detection etc. –Exploit sensors already in the environment. –Allow easy deployment of additional sensors. Situational awareness is the PERCEPTION of elements in the environment within a volume of time and space, the COMPREHENSION of their meaning, and the PROJECTION of their status in the near future (Endsley 1988). Our goal is to develop intelligent software agents to support situational awareness

3. Handles noisy, missing, delayed sensor readings –Use a principled and explicit representation of uncertainty Detects faulty or unreliable sensors –Open system with multiple stakeholders Makes short term predictions based on past sensor readings –Without explicit physical models Makes efficient use of limited local resources to acquire maximum information (active data selection) –Limited bandwidth, power or computational resources –Select between sensors and optimise sampling schedule minimal domain knowledge > learning algorithms real-time > computationally efficient algorithms An information agent autonomously acquires and processes sensor data

4. We view information processing as multi- dimensional regression and prediction problem We use Gaussian processes to perform principled Bayesian inference regarding the underlying environmental parameters based on data streams of sensor readings subject to: missing readings correlations delays

5. Gaussian processes exploit the attractive analytical properties of a multivariate Gaussian distribution Marginal and conditional probabilities are both Gaussian. Defined by a mean vector and a covariance matrix.

6. Gaussian processes exploit the attractive analytical properties of a multivariate Gaussian distribution

7. A Gaussian process is the generalisation of a multivariate Gaussian distribution to a potentially infinite number of variables.

8. Covariance functions represent our prior knowledge about the correlation between measurements at different times and places periodic relationships hyper-parameters (length scale) hyper-parameters (period)

9. delays scale correlation matrix weight length scale period Spatial and temporal correlation can be combined to represent correlations both over time and between sensors

10. Wireless weather sensors deployed in the Solent –Wind speed, direction, air temperature, air pressure, tide height etc –Samples every minute –Website for each sensor displaying data Augmented these sensor websites with machine readable RDF markup –RDF is a key building block of the semantic web We have validated our approach using a network of weather sensors in the Solent

11. Our information agent can predict missing readings when sensors fail and autonomously acquire sensor readings

13. A web-based implementation of our information agent is available online www.aladdinproject.org/situation/

14. More details of our approach can be found in the following publications Rogers, A., Corkill, D. D. and Jennings, N. R. (2009) Agent Technologies for Sensor Networks. IEEE Intelligent Systems, 24 (2). pp. 13-17. Osborne, M. A., Rogers, A., Ramchurn, S., Roberts, S. J. and Jennings, N. R. (2008) Towards Real-Time Information Processing of Sensor Network Data using Computationally Efficient Multi-output Gaussian Processes. In: International Conference on Information Processing in Sensor Networks (IPSN 2008), pp 109-120, St. Louis, Missouri, USA. Rogers, A., Osborne, M. A., Ramchurn, S., Roberts, S. J. and Jennings, N. R. (2008) Information Agents for Pervasive Sensor Networks. In: Fourth IEEE International Workshop on Sensor Networks and Systems for Pervasive Computing (PerComm 20008), pp 294-299, Hong Kong, China. Available online at: www.ecs.soton.ac.uk/~acr/