On agent-friendly aggregation in networks
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Transcript of On agent-friendly aggregation in networks
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On agent-friendly aggregation in networks
ATSN 2008 (at AAMAS 2008)
Christian Sommer and Shinichi HonidenNational Institute of Informatics,
The University of Tokyo
Tokyo, Japan
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Agenda
• Sensor networks
• Aggregation
• Agent aggregation specifics
• Problem model: aggregation graph
• Computing a tour
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Sensor networks
• Sense/measure the environment– Temperature– Sound– Vibration– Pressure– Motion– …
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Sensor networks
Base station
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Wireless sensor networks
Base station
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Example: Sun SPOT Sensors
• Processing– 180 MHz 32 bit ARM920T core - 512K RAM - 4M Flash
– 2.4 GHz IEEE 802.15.4 radio with integrated antenna
• Sensor Board• Battery
– 3.6V rechargeable 750 mAh lithium-ion battery
– 30 uA deep sleep mode
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Data aggregation
• Severe resource limitations (battery, sending power)
• Often high redundancy of sensor measurements (time and space)
• Aggregate data before sending it to the base station (e.g., AVG, SUM, MIN,…)
• Aggregation tree
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Aggregation tree
Base station
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Aggregation using a mobile (software) agent
• Code is sent through the sensor network…
• … runs on (all/some) network nodes …– collects and aggregates data
• … and returns to the base station.
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Pros and cons of the agent approach
Advantages:• ability to use code /
aggregation function, which is– Application-specific– Dynamic– Non-local
Problems:• Time• Code size• Security• Aggregation tour
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Pros and cons of the agent approach
Advantages:• ability to use code /
aggregation function, which is– Application-specific– Dynamic– Non-local
Problems:• Time• Code size• Security• Aggregation tour
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Pros and cons of the agent approach
Advantages:• ability to use code /
aggregation function, which is– Application-specific– Dynamic– Non-local
Problems:• Time• Code size• Security• Aggregation tour
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What route to take?
• Visit all nodes
• Energy-efficiency– Avoid visiting nodes/edges several times
(possible exception: base station)• Possibly not a tree-like structure!
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Aggregation tree
Base station
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Problem modelling
• Sensor network as undirected graph
Base station
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Problem modelling
• Sensor network as undirected graph
Base station
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Problem modelling
• Sensor network as undirected graph
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Assumption
• Graph is known (to base station)
• (i.e. sensors and their adjacency is known)
• … and does not change, static
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Hamiltonian cycle
• Given a graph G=(V,E)
• Find a cycle visiting all nodes
• Hard problem
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Travelling Salesman (TSP)
• Given a weighted graph G=(V,E)
• Find shortest tour visiting all nodes
• Compare all Hamiltonian cycles
• Hard problem
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Hard problems?
• Hard in the worst case• But: there is hope for some graphs;
problems are solvable on average for these instances
• Unit disk model: n nodes are distributed uniformly at random in the unit disk, nodes within distance r (trans-mission radius) can communicate
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Assumption
• Apart from base station, all sensors can send and receive within the same distance, not possible to adapt signal strength (due to unit disk model)
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Hamiltonian cycle for unit disk graphs (Bollobas et al., Petit)
1) Remove trees, 2-core remains2) While no cycle is found, backtrack through
different rotations (permutations)1) Take a path from the list of partial paths2) Try to extend it at either side with an unvisited
nodeIf impossible,
1) If cyclic, search for a node with a yet unvisited neighbor (exists due to connectivity)
2) Else, for endpoints, check for another adjacent node on the path and rotate
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Hamiltonian cycle for unit disk graphs (Bollobas et al., Petit)
1) Remove trees, 2-core remains2) While no cycle is found, backtrack through
different rotations (permutations)1) Take a path from the list of partial paths2) Try to extend it at either side with an unvisited
nodeIf impossible,
1) If cyclic, search for a node with a yet unvisited neighbor (exists due to connectivity)
2) Else, for endpoints, check for another adjacent node on the path and rotate
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Hamiltonian cycle for unit disk graphs (Bollobas et al., Petit)
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Hamiltonian cycle for unit disk graphs (Bollobas et al., Petit)
1) Remove trees, 2-core remains2) While no cycle is found, backtrack through
different rotations (permutations)1) Take a path from the list of partial paths2) Try to extend it at either side with an unvisited
nodeIf impossible,
1) If cyclic, search for a node with a yet unvisited neighbor (exists due to connectivity)
2) Else, for endpoints, check for another adjacent node on the path and rotate
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Hamiltonian cycle for unit disk graphs (Bollobas et al., Petit)
1) Remove trees, 2-core remains2) While no cycle is found, backtrack through
different rotations (permutations)1) Take a path from the list of partial paths2) Try to extend it at either side with an unvisited
nodeIf impossible,
1) If cyclic, search for a node with a yet unvisited neighbor (exists due to connectivity)
2) Else, for endpoints, check for another adjacent node on the path and rotate
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Hamiltonian cycle for unit disk graphs (Bollobas et al., Petit)
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Hamiltonian cycle for unit disk graphs (Bollobas et al., Petit)
1) Remove trees, 2-core remains2) While no cycle is found, backtrack through
different rotations (permutations)1) Take a path from the list of partial paths2) Try to extend it at either side with an unvisited
nodeIf impossible,
1) If cyclic, search for a node with a yet unvisited neighbor (exists due to connectivity)
2) Else, for endpoints, check for another adjacent node on the path and rotate
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Hamiltonian cycle for unit disk graphs (Bollobas et al., Petit)
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Hamiltonian cycle for unit disk graphs (Bollobas et al., Petit)
1) Remove trees, 2-core remains2) While no cycle is found, backtrack through
different rotations (permutations)1) Take a path from the list of partial paths2) Try to extend it at either side with an unvisited
nodeIf impossible,
1) If cyclic, search for a node with a yet unvisited neighbor (exists due to connectivity)
2) Else, for endpoints, check for another adjacent node on the path and rotate
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Hamiltonian cycle for unit disk graphs (Bollobas et al., Petit)
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Hamiltonian cycle for unit disk graphs (Bollobas et al., Petit)
1) Remove trees, 2-core remains2) While no cycle is found, backtrack through
different rotations (permutations)1) Take a path from the list of partial paths2) Try to extend it at either side with an unvisited
nodeIf impossible,
1) If cyclic, search for a node with a yet unvisited neighbor (exists due to connectivity)
2) Else, for endpoints, check for another adjacent node on the path and rotate
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Hamiltonian cycle for unit disk graphs (Bollobas et al., Petit)
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Hamiltonian cycle for unit disk graphs (Bollobas et al., Petit)
1) Remove trees, 2-core remains2) While no cycle is found, backtrack through
different rotations (permutations)1) Take a path from the list of partial paths2) Try to extend it at either side with an unvisited
nodeIf impossible,
1) If cyclic, search for a node with a yet unvisited neighbor (exists due to connectivity)
2) Else, for endpoints, check for another adjacent node on the path and rotate
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Hamiltonian cycle for unit disk graphs (Bollobas et al., Petit)
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Conclusion
• If agent-based aggregation is benefitial in a sensor network, it can be done quite efficiently.
• (the algorithm of Bollobas et al. quickly computes an energy-efficient tour (a Hamiltonian cycle) in a unit disk graph)
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Thank you