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On agent-friendly aggregation in networks

On agent-friendly aggregation in networks. ATSN 2008 (at AAMAS 2008) Christian Sommer and Shinichi Honiden National Institute of Informatics, The University of Tokyo Tokyo, Japan. Agenda. Sensor networks Aggregation Agent aggregation specifics Problem model: aggregation graph

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On agent-friendly aggregation in networks

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  1. On agent-friendly aggregation in networks ATSN 2008 (at AAMAS 2008) Christian Sommer and Shinichi Honiden National Institute of Informatics, The University of Tokyo Tokyo, Japan Agent-friendly aggregation 1

  2. Agenda • Sensor networks • Aggregation • Agent aggregation specifics • Problem model: aggregation graph • Computing a tour Agent-friendly aggregation 2

  3. Sensor networks • Sense/measure the environment • Temperature • Sound • Vibration • Pressure • Motion • … Agent-friendly aggregation 3

  4. Sensor networks Base station Agent-friendly aggregation 4

  5. Wireless sensor networks Base station Agent-friendly aggregation 5

  6. 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 Agent-friendly aggregation 6

  7. 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 Agent-friendly aggregation 7

  8. Aggregation tree Base station Agent-friendly aggregation 8

  9. 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. Agent-friendly aggregation 9

  10. Advantages: ability to use code / aggregation function, which is Application-specific Dynamic Non-local Problems: Time Code size Security Aggregation tour Pros and cons of the agent approach Agent-friendly aggregation 10

  11. Advantages: ability to use code / aggregation function, which is Application-specific Dynamic Non-local Problems: Time Code size Security Aggregation tour Pros and cons of the agent approach ATSN 07 Agent-friendly aggregation 11

  12. Advantages: ability to use code / aggregation function, which is Application-specific Dynamic Non-local Problems: Time Code size Security Aggregation tour Pros and cons of the agent approach ATSN 08 Agent-friendly aggregation 12

  13. 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! Agent-friendly aggregation 13

  14. Aggregation tree Base station Agent-friendly aggregation 14

  15. Problem modelling • Sensor network as undirected graph Base station Agent-friendly aggregation 15

  16. Problem modelling • Sensor network as undirected graph Base station Agent-friendly aggregation 16

  17. Problem modelling • Sensor network as undirected graph Agent-friendly aggregation 17

  18. Assumption • Graph is known (to base station) • (i.e. sensors and their adjacency is known) • … and does not change, static Agent-friendly aggregation 18

  19. Hamiltonian cycle • Given a graph G=(V,E) • Find a cycle visiting all nodes • Hard problem Agent-friendly aggregation 19

  20. Travelling Salesman (TSP) • Given a weighted graph G=(V,E) • Find shortest tour visiting all nodes • Compare all Hamiltonian cycles • Hard problem Agent-friendly aggregation 20

  21. 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 Agent-friendly aggregation 21

  22. 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) Agent-friendly aggregation 22

  23. Hamiltonian cycle for unit disk graphs (Bollobas et al., Petit) • Remove trees, 2-core remains • While no cycle is found, backtrack through different rotations (permutations) • Take a path from the list of partial paths • Try to extend it at either side with an unvisited node If impossible, • If cyclic, search for a node with a yet unvisited neighbor (exists due to connectivity) • Else, for endpoints, check for another adjacent node on the path and rotate Agent-friendly aggregation 23

  24. Hamiltonian cycle for unit disk graphs (Bollobas et al., Petit) • Remove trees, 2-core remains • While no cycle is found, backtrack through different rotations (permutations) • Take a path from the list of partial paths • Try to extend it at either side with an unvisited node If impossible, • If cyclic, search for a node with a yet unvisited neighbor (exists due to connectivity) • Else, for endpoints, check for another adjacent node on the path and rotate Agent-friendly aggregation 24

  25. Hamiltonian cycle for unit disk graphs (Bollobas et al., Petit) Agent-friendly aggregation 25

  26. Hamiltonian cycle for unit disk graphs (Bollobas et al., Petit) • Remove trees, 2-core remains • While no cycle is found, backtrack through different rotations (permutations) • Take a path from the list of partial paths • Try to extend it at either side with an unvisited node If impossible, • If cyclic, search for a node with a yet unvisited neighbor (exists due to connectivity) • Else, for endpoints, check for another adjacent node on the path and rotate Agent-friendly aggregation 26

  27. Hamiltonian cycle for unit disk graphs (Bollobas et al., Petit) • Remove trees, 2-core remains • While no cycle is found, backtrack through different rotations (permutations) • Take a path from the list of partial paths • Try to extend it at either side with an unvisited node If impossible, • If cyclic, search for a node with a yet unvisited neighbor (exists due to connectivity) • Else, for endpoints, check for another adjacent node on the path and rotate Agent-friendly aggregation 27

  28. Hamiltonian cycle for unit disk graphs (Bollobas et al., Petit) Agent-friendly aggregation 28

  29. Hamiltonian cycle for unit disk graphs (Bollobas et al., Petit) • Remove trees, 2-core remains • While no cycle is found, backtrack through different rotations (permutations) • Take a path from the list of partial paths • Try to extend it at either side with an unvisited node If impossible, • If cyclic, search for a node with a yet unvisited neighbor (exists due to connectivity) • Else, for endpoints, check for another adjacent node on the path and rotate Agent-friendly aggregation 29

  30. Hamiltonian cycle for unit disk graphs (Bollobas et al., Petit) Agent-friendly aggregation 30

  31. Hamiltonian cycle for unit disk graphs (Bollobas et al., Petit) • Remove trees, 2-core remains • While no cycle is found, backtrack through different rotations (permutations) • Take a path from the list of partial paths • Try to extend it at either side with an unvisited node If impossible, • If cyclic, search for a node with a yet unvisited neighbor (exists due to connectivity) • Else, for endpoints, check for another adjacent node on the path and rotate Agent-friendly aggregation 31

  32. Hamiltonian cycle for unit disk graphs (Bollobas et al., Petit) Agent-friendly aggregation 32

  33. Hamiltonian cycle for unit disk graphs (Bollobas et al., Petit) • Remove trees, 2-core remains • While no cycle is found, backtrack through different rotations (permutations) • Take a path from the list of partial paths • Try to extend it at either side with an unvisited node If impossible, • If cyclic, search for a node with a yet unvisited neighbor (exists due to connectivity) • Else, for endpoints, check for another adjacent node on the path and rotate Agent-friendly aggregation 33

  34. Hamiltonian cycle for unit disk graphs (Bollobas et al., Petit) Agent-friendly aggregation 34

  35. Hamiltonian cycle for unit disk graphs (Bollobas et al., Petit) • Remove trees, 2-core remains • While no cycle is found, backtrack through different rotations (permutations) • Take a path from the list of partial paths • Try to extend it at either side with an unvisited node If impossible, • If cyclic, search for a node with a yet unvisited neighbor (exists due to connectivity) • Else, for endpoints, check for another adjacent node on the path and rotate Agent-friendly aggregation 35

  36. Hamiltonian cycle for unit disk graphs (Bollobas et al., Petit) Agent-friendly aggregation 36

  37. 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) Agent-friendly aggregation 37

  38. Thank you Agent-friendly aggregation 38

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