1. Agents, Mobility, Ubiquity & Virtuality Gregory O’Hare Department of Computer Science, University College Dublin Mobile Agents & Wireless Sensor Networks COMP4019 Multi-Agent systems Lecture Materials 2007 Gregory O’Hare School of Computer Science & Informatics, University College Dublin (UCD)

2. A adaptive information cluster I C

3. The Adaptive Information Cluster  In addition to the academic partners, the AIC has industrial participation from:  University College Dublin  Dublin City University  Mitsubishi Electric Research Labs in Cambridge Mass.  Ericsson, Ireland  ChangingWorlds, Ireland  IBM Ireland  Approx €6m ($7m) over 4 years and a team of 100 researchers;

4. AIC has Three Research Themes  Adaptive Sensor Networks  Adaptive Media  Adaptive Utilization

5. Wireless Sensors VTT Soapbox Mica2 Mote NMRC 10mm Cube

6. Distributed Interpolation In WSNs it is not always possible to deploy sensors in such a high density so as to be able to get an exact sensor reading at every desired point. Need a mechanism to “guess” values at intermediate points between sensor nodes. The agents on the motes cooperate to calculate interpolated temperatures between nodes. Demonstration of this is available at the poster session.

7. Hibernating redundant sensors, must be decided based upon both routing and sensing connectivity. Redundant based on routing and sensing coverage u Zone n+1 Zone n- 1 Zone n Redundant based on sensor coverage Disconnected Disconnecting sensors based on solely sensing or routing coverage may lead to a disconnected network. Transmission radius Opportunistic Power Management Gateway Sensing Coverage: If for any point within the sensed area is covered by at least 1 sensor; Redundant node: can be switched off without affecting the level of coverage provided by the network; Routing Coverage: If there exists at least one communication path from any node within the network to the gateway Integrated Sensor & Routing Agents

8. Intelligent Coverage Agents A sensor is redundant if every point in the sensing radius is within the radius of at least 1 other sensor A `temperature sensor is redundant if: actual temp – interpolated temp < threshold Use weighted Average Interpolation Request d i & value i from each neighbour Calculate summations Decide to hibernate  value(x, y) the interpolated value  d i distance of sensor i to (x, y)  value i is sensor I’s value  Repeated for each agent until no other sensors can be hibernated.  Periodically each agent will wake up to verify its redundance activating itself accordingly  Prolongs network life while maintaining data integrity

9. Deployment Irregularity & Coverage in Wireless Sensor Networks The arrangement of nodes is very important for coverage It influences the number of nodes that are required to be on, which affects the power management of the network square hexagonal random Irregular cases aren’t much less efficient when redundancy is high Regular can use more nodes effectively Irregular fails prematurely When redundancy is low, the number of nodes that need to be active is high Regular arrangements don’t need as much redundancy as irregular ones

10. Mobile Sensors By attaching a sensor to a robot we could dynamically evaluate the accuracy of our interpolation and get our agents to dynamically refine their model of the interpolation. Collaboration with Prof. John Leonard MIT, Cambridge

11. Collaborations - NSRC & EPA Environmental Nervous System Chemical sensor implemented with the motes. Can detect the presence or absence of Chemicals specifically Acetic Acid. A new Collaboration has been initiated With the Environmental Protection Agency (EPA). Demonstration of this to follow at the poster presentation.

12. Collaborations - IBM T.J. Watson Obstacle Modeling for Distributed Interpolation – IBM. We have a model of the Park City Mall in Pennsylvania, which takes a cruifix format. To accurately model the interpolation we will be modeling the mall with the walls as obstacles. In Collaboration with Ron Ambrossio IBM Watson

13. Park City Mall

14. Wireless Sensors Mica2 Motes

15. What is Autonomic Computing ? Autonomic computing is the next generation of integrated computer technology that will allow networks to manage themselves with little or no human intervention. Term dates back to 2001 and is attributable to Paul Horn of IBM Research. The central tenets of this model : Self-Awareness; Self-Healing Self-Management; Self-Protect; Self-optimise Proactive; See www.research.ibm.com/autonomic/

16. Autonomic WSN Architecture A sensor node operates within a context. This context involves consideration of the overall well being of the sensor network. The network is empowered with rudimentary self-management and self-organisation capabilities – intelligent coverage, intelligent power management, choice of scheduling and routing algorithms Such decisions can be difficult and demand collaborative reasoning. We advocate a multi-agent systems approach.

17. Autonomic WSNs Transmission rate biggest factor in determining longevity of WSN. Manage transmission frequency to maximise network life-span. Implemented an intrusion detection system, which used the drop in light level as a person enters a room to detect entry. Our experimental setup was as follows: We found that we could cut the number of transmissions by 250% while still increasing the accuracy of our system when compared to the standard sense-transmit protocol

18. Device Control Continuum

19. Mobility & Agility

20. Agent Mutation & Evolution

21. Collaborative Agent Tuning Within ABLE this is known as the closed-loop control. Similarly the DIOS++ architecture consists of sensors and actuators, which monitor and adjust system state.

22. Embodied Intentional Agents Mental State drives Avatar behaviour

23. Methodology Debugging almost impossible  3 leds => 8 execution states Compounded when considering distributed applications Solution – Methodology for Agents on WSNs  Stage 1: Create centralised solution for an application  Stage 2: Distribute to agents on base station: Key 1 agent to 1 mote  Stage 3: Map agent instructions of agents to statements of the Motes.

24. Tools Tinyos IDE  http://csb109pc1.ucd.ie:8080 http://csb109pc1.ucd.ie:8080  Automatic Updater also available  Online bug reporting  Improvement Request Form Experiment Logger/Player  Log experimental data  Replay many times to repeat experiment  Incorporated into IDE  Aids in the use of the Methodology

25. TinyOS IDE

26. Wearable/Ubiquitous Sensors Motes in Wearable Sensor Networks. By linking pressure sensitive pads through a personal wireless network we are going to experiment with mechanisms to measure bodily functions such as breathing. This could allow us to detect hyper- ventilation and inform paramedics through a mobile phone.

27. Mobile Sensors By attaching a sensor to a robot we could dynamically evaluate the accuracy of our interpolation and get our agents to dynamically refine their model of the interpolation.

28. Mobile Sensing Modalities Sonar for obstacle avoidance Vision Laser Scan Matching