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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
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
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
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
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
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
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
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.
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
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)
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
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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CONCLUSION
• The examples described here illustrate that even experimental sensor agent technology has become sufficiently reliable.
• Doing so will no doubt introduce novel challenges
Md. Merazul Islam, CSE, KUET 14
Thanks to all