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AGENT TECHNOLOGIES FOR SENSOR NETWORKS
Reference: Alex Rogers and Nicholas R. Jennings, University of Southampton
Daniel D. Corkill, University of Massachusetts Amherst
IEEE Intelligent Systems, March-April 2009
Presented By: Md. Merazul Islam0507036
Dept. of CSE, KUET
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Md. Merazul Islam, CSE, KUET 2
INTRODUCTION
• Wireless Sensor Network– Way of wide-area monitoring– Work with environmental, security, and military
scenarios– Consist of small, battery-powered devices– Connected through a wireless communication
network– Faces some challenges
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Md. Merazul Islam, CSE, KUET 3
CHALLENGES
• Wireless Sensor Network– Collect data over extended periods of time– Deployed in inhospitable environments– Replacing batteries is impossible
• Goals not achieve– Sensors don’t share their sensing actions– Network don’t adapt responses in a dynamically
changing environment
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Md. Merazul Islam, CSE, KUET 4
OVERCOMES
• Multiagent Systems Need– Extensive set of formalisms, algorithms, and
methodologies– Mapping from sensor to agent– Use of more low power resources– Reliable hardware and communication
Rather than we need A New Synthesis
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Md. Merazul Islam, CSE, KUET 5
NEW SYNTHESIS
• Synthesis Has Succeeded 1. Efficient decentralized coordination algorithms 2. Sensor-agent platforms in the field3. Intelligent agents
These three examples are Proved & Evaluated by the researchers in real, hostile environment
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Md. Merazul Islam, CSE, KUET 6
AGENT-BASED DECENTRALIZED COORDINATION
• Coordination Might Include – Routing data through the network– Choosing appropriate sampling rates of sensors
• Coordination Should Performed1. No central point of failure exits2. Computation must shared over the distributed
resources3. Number of devices in the network increases
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Md. Merazul Islam, CSE, KUET 7
AGENT-BASED DECENTRALIZED COORDINATION
• Proposed Algorithms – Agent update their state for its own not globally– Max-sum algorithm used to solve it– Requires less computational and communication
resources– Generates good solutions applied to cyclic graphs
Researchers have implemented it in hardware
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Md. Merazul Islam, CSE, KUET 8
Figure 1. Hardware implementation of the
max-sum algorithm and the graph-coloring
benchmark problem using the Texas
Instruments CC2430 System-on-Chip. The
seven-segment display indicates the number
of neighbors that each sensor has located, and the three LEDs
indicate their respective sensor’s
chosen color.
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Md. Merazul Islam, CSE, KUET 9
DEPLOYING SENSOR AGENTSIN THE FIELD
o Field deployment presents significant additional challenges
o The CNAS has created a agent-based sensor network
o Each agent decides what and when to perform the activities
o Sharing of information is better to inform high-level operational decision making
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Md. Merazul Islam, CSE, KUET 10
Figure 2. A CNAS sensor agent at the 2006 Patriot Exercise at Fort McCoy, Wisconsin, deployed to collect real-time weather data at a landing strip. (photo courtesy of the US Air Force)
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Md. Merazul Islam, CSE, KUET 11
INFORMATION AGENTS FOR PERVASIVE SENSOR NETWORKS
• Agents Must be Able to – Handle missing or delayed data– Detect faulty sensors– Fuse noisy measurements from several sensors– Efficiently manage bandwidth– Predict both the value of missing sensor
A live implementation of this prototype agent is currently available
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Md. Merazul Islam, CSE, KUET 12
Figure 3. The Bramble Bank weather Station, located in the Solent.
Figure 4. Screenshot of an information agent. A live
implementation is available at www.aladdinproject.org/situation
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Md. Merazul Islam, CSE, KUET 13
CONCLUSION
• The examples described here illustrate that even experimental sensor agent technology has become sufficiently reliable.
• Doing so will no doubt introduce novel challenges
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Md. Merazul Islam, CSE, KUET 14
Thanks to all