Sensor-Task Assignment in Heterogeneous Sensor Networks

Diego PizzocaroD.Pizzocaro@cs.cf.ac.uk

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?