1) The document proposes a multi-agent system called NMAOS to develop an agile wireless sensor network for structural health monitoring. 2) NMAOS is a self-learning system that can predict peak loads on a structure and trigger messages to activate the sensor network during unexpected incidents. 3) The multi-agent architecture includes triggering, comparative, sensor, collaborating, damage diagnostic, and prognostic agents to monitor the structure and assess damage.

1. Multi Agent System for Agile
Wireless Sensor Network to
Monitor Structures
Othman Sidek and S. A. Quadri
CEDEC (Collaborative Microelectronic Design Excellence Centre)
Universiti Sains Malaysia, Engineering Campus
14300, Malaysia
Paper presented at PIERS Conference , Proceedings Marrakesh, Morocco, Mar. 20-23, 2011 765

2. Keywords :
Multi agent systems
Structural health Monitoring
Wireless sensor networks
Agility

3. •Multi-agent system (MAS) is a system composed of
multiple interacting intelligent agents.
•An intelligent agent (IA) is an autonomous entity which
observes and acts upon an environment.
•Intelligent agent learns and use knowledge to achieve
goals.

4. Wireless Sensor Network
Wireless Sensor Network (WSN) consists of
spatially distributed autonomous sensors to
monitor physical or environmental conditions.

5. Agility
Agility is defined as the capability of a WSN to timely
capture the event response of the monitored
structure, process the measured data, extract the
relevant features, and interpret the results.

6. Why need an agile WSN ?

7. Golden Gate Bridge (GGB), California
University of California, Berkeley, installed 256 accelerometers to monitor bridge.

8. The sensor network on the bridge did not
collect any data during earthquakes in
2006 because the monitoring system
was in Passive mode !!!
The significant non-linear behavior
analysis at extreme-conditions could not
be studied because of non availability of
data.

9. Thus in order to ensure reliable WSN
system to monitor structure a
multi agent model is proposed.

10. NMAOS
N => Never
M => Miss
A => An
O => Opportunity
S => Scheme

11. Practical back ground / Previous work

12. Non-preemptive MAC without priority
support has failed to capture signals
during earthquake.

13. Liang Cheng and Shamim N. Pakzad ( IEEE Conf, 2009)
proposed a Pulse-based media access control scheme.
A trigger message from a nearby observation site can
be timely propagated across a WSN to preempt current
tasks such as energy-saving sleeping and scheduled
data transmissions so that the sensor network can be
forced into a record ready state before the high impact
load (earthquake) waves reach the monitored bridge.

14. NMAOS (never miss an opportunity scheme)
It is self-learning intelligent-based software
system which obtains the training data by
observing the real time traffic on a particular site
(bridge), and by efficiently analyzing the captured
data the system predicts maximum load on the
structure at a particular instance of time, thus can
trigger messages promptly to make the system
active.
Thus ensures the WSN system to be active during
unpredicted incidents.

15. To apply multi-agent technology to a
distributed SHM system, each component or
subsystem in the SHM should is changed to an
agent.
Six kinds of agents are defined to develop an
agile WSN for a SHM system.

16. The multi-agent architecture for an Agile WSN

17. Triggering agent:
It triggers the system by generating the pulse
A self-learning intelligent-based software
system predicts the maximum peak load on
the structure (bridge) at some particular time,
and trigger more priority pulses to active
monitoring system.

18. Comparative agent
Comparative logic is embedded in the
comparative agent, which accomplishes the
task of preempting current tasks, such as
energy-saving sleep and allows trigger pulses
to activate the whole system to monitor the
structure during any disastrous event.

19. Sensor agent
The sensor agent is responsible for sensing
parameters, such as stress, strain, pressure
displacement, acoustics, and temperature.
They accomplish many tasks such as data
processing and disseminating.

20. Automatic collaborating agent
Supports low-power, multi-point, and
heterogeneous operations with a distributed
synchronization mechanism.
It takes care of clustering, fusing and
communicating data exchange between
different agent entities.

21. Damage diagnostic agent
It comprises a real-time automated reasoning
and decision-making integrated software
system.
Training data is fed to Artificial Neural network
to incorporate Expert decision-making.

22. Prognostic agent
Prognostic agent evaluates the remaining life-
time of the structure at a given state of
damage and future loading. The future load
spectra can be either damage tolerance or
safe-life philosophy.
The results of the damage diagnostic agent
provide information on the current state of
the structure for prognostics.

23. CONCLUSION AND FUTURE WORK
Agility of a WSN is an important factor contributing to reliable and
successful SHM programs.
The prototype NMAOS (never miss an opportunity scheme) based
on multi-agent technology proposed uses the PB-MAC based
technique of triggering a message of high priority to preempt the
current passive state of the WSN and make it active for sensing and
capturing data at the time of an unpredicted event of high impact
on the structure, such as an earthquake.
Further studies can focus on evaluating the effectiveness and
validating this cognitive multi-agent based prototype in real-time
situations with a view to developing an agile wireless network
system to monitor the health of structures.