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Sensor-Task Assignment in Heterogeneous Sensor Networks
Sensor-Task Assignment in Heterogeneous Sensor Networks
Sensor-Task Assignment in Heterogeneous Sensor Networks
Sensor-Task Assignment in Heterogeneous Sensor Networks
Sensor-Task Assignment in Heterogeneous Sensor Networks
Sensor-Task Assignment in Heterogeneous Sensor Networks
Sensor-Task Assignment in Heterogeneous Sensor Networks
Sensor-Task Assignment in Heterogeneous Sensor Networks
Sensor-Task Assignment in Heterogeneous Sensor Networks
Sensor-Task Assignment in Heterogeneous Sensor Networks
Sensor-Task Assignment in Heterogeneous Sensor Networks
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Sensor-Task Assignment in Heterogeneous Sensor Networks

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  • 1. Sensor-Task Assignment in Heterogeneous Sensor Networks Diego Pizzocaro D.Pizzocaro@cs.cf.ac.ukResearch Group: KIS Supervisor: Prof. Alun Preece
  • 2. Why sensor-task assignment?• Heterogeneous Sensor Networks (HSN) introduce new resource allocation problems in which sensors must be assigned to the tasks they best help• An already deployed HSN is usually required to support multiple sensing tasks of different nature to be accomplished simultaneously• Tasks might compete for the exclusive usage of the same sensing resource ➡ We need schemes to assign individual sensors to tasks• Research focus: find the right way to model this allocation problem • we defined increasingly detailed models • we developed computationally efficient approaches to solve them
  • 3. Example• Two target identification tasks. (Tasks may have different priorities)• Targets are close in the field.• We only have one video sensor that could identify both.• Problem: Where do we point the video sensor? (i.e. To which task do we assign the sensor?) X Target 2 X Target 1
  • 4. Simple model Sensor-Task Assignment• Tasks vary in priority and have a different demand Sensors for sensing resource capabilities. S1 e11 Tasks e1 T1 (d1, p1) 2• Each sensor has a different utility for each task, S2 because of: • Geography & distance S3 T2 (d2, p2) • Remaining battery life S4• Goal: A sensor assignment that maximizes the e = utility of sensor to a task utility that the sensor network can provide d = task utility demand to tasks. p = task priority This problem is NP-Complete and very hard to approximate: We developed many heuristic algorithms to solve it (greedy algs)
  • 5. Non additive utility!• Utilities from multiple sensors do not always combine additively• Example: ‣ Triangulation tasks ‣ We need two audio sensors for each task X X Target 2 Target 2 X XTarget 1 Target 1 Task 1: Utility(S1,S2) = 100 Task 1: Utility(S1) = 0
  • 6. More detailed model Sensor-Bundle-Task Assignment• We first want to group sensors into bundles, and then we want to find the best assignment of bundles to tasks.• NP-Complete problem: we will use COMBINATORIAL AUCTION techniques. Sensors Bundles Tasks S1 e11 B1 T1 (p1) S2 e1 2 S3 B2 T2 (p2) S4 e = joint utility of a bundle to a task p = task priority
  • 7. Combinatorial auctions• Combinatorial auction: ‣ It is a silent auction in which bidders can bid on sets of items (instead of single items). ‣ Each bidder provides sets of items and corresponding prices for each set. ‣ The auctioneer chooses the set of bids that maximizes the payment.• The Sensor-Bundle-Task Assignment model can be seen as a combinatorial auction.
  • 8. Related work - overview• Combinatorial auctions have been already applied to sensor-task assignment problems in scenarios where sensor utility does not combine additively: ‣ J. Ostwald, V. Lesser, and S. Abdallah. Combinatorial auctions for resource allocation in a distributed sensor network. In RTSS ’05 (Real Time Systems Symposium), Washington, DC, USA.• Problem: assign radar sensors to weather monitoring tasks ‣ Sensors have multiple settings (and can be configured) ‣ Joint utility of a bundle is computed with a probabilistic approach (i.e. non-additively) ‣ A sensor can be shared by multiple tasks• This problem is a variant of the classic combinatorial auction: • they modified a pre-existent algorithm.
  • 9. Similarities and differences• Similarities: ‣ Their problem can be modeled as a variant of classic combinatorial auctions. ‣ Their joint utility is computed using a task dependent joint utility function. ‣ They use heuristic/preprocessing to limit the number of possible bundles and sensor configurations.• Differences: ‣ They assume an homogeneous sensor network (only radars) but configurable. ‣ Their tasks are not different by nature (only “monitoring tasks”): joint utility is easier to compute. ‣ The size of the network and the number of simultaneous tasks (a few dozens of radar sensors, and ten tasks). ‣ The computational time is not the main focus (time to solve it is 10 seconds!).
  • 10. Learned lessons• Applying combinatorial auctions to solve our current model seems reasonable.• We need to modify (again!) our model considering configurable sensors and resource sharing ‣ To solve it we could adopt a similar approach to modify combinatorial auction.• Critique to their approach: ‣ they should have modified other algorithms or developed new greedy algorithms to compare the performances (quality of solution, computational cost).
  • 11. Thanks for listening! Questions?

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