sensor-task assignment in heterogeneous sensor networks
TRANSCRIPT
Sensor-Task Assignment in Heterogeneous Sensor Networks
Diego [email protected]
Research Group: KIS Supervisor: Prof. Alun Preece
• 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
Why sensor-task assignment?
• 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?)
Example
XTarget 1
XTarget 2
• Tasks vary in priority and have a different demandfor sensing resource capabilities.
• Each sensor has a different utility for each task, because of:
• Geography & distance
• Remaining battery life
• Goal: A sensor assignment that maximizes the utility that the sensor network can provide to tasks.
S1
S2
S3
S4
T1
T2
Sensors
Tasks
e11
(d1, p1)
(d2, p2)
e12
e = utility of sensor to a taskd = task utility demand
p = task priority
Simple modelSensor-Task Assignment
This problem is NP-Complete and very hard to approximate:We developed many heuristic algorithms to solve it (greedy algs)
• Utilities from multiple sensors do not always combine additively
• Example:
‣ Triangulation tasks
‣ We need two audio sensors for each task
Non additive utility!
XTarget 1
XTarget 2
Task 1: Utility(S1) = 0
XTarget 1
XTarget 2
Task 1: Utility(S1,S2) = 100
• 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.
S1
S2
S3
S4
B1
B2
SensorsBundles
e = joint utility of a bundle to a taskp = task priority
T1
T2
Tasks
(p1)
(p2)
e11
e12
Sensor-Bundle-Task Assignment
More detailed model
• 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.
Combinatorial auctions
• 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.
Related work - overview
• 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!).
Similarities and differences
• 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).
Learned lessons
Thanks for listening!
Questions?