on multi-sensor task allocation

On Multi-Sensor Task Allocation

Diego PizzocaroPhD candidate

Supervisors: Prof. Alun Preece - Dr. Roger Whitaker

Seminar @ DEI University of Padova (Italy)

April, 2009

http://users.cs.cf.ac.uk/D.Pizzocaro [email protected]

Cardiff School of Computer Science

mailto:[email protected]


• 2007 - Computer Engineering, University of Padova (Italy), supervised by Prof. Luca Schenato

• 2008 - Start PhD in Computer Science at Cardiff University (UK), supervised by Prof. Alun Preece and Dr. Roger Whitaker

• International Technology Alliance (ITA) project: multi-national teams supported by complex information networks

• Our research focus: intelligent resource allocation in sensor networks.

Brief Bio


U.S. Army Research LabU.K. Ministry of Defence



1. Motivations & MSTA problem

2. MSTA in Homogeneous Sensor Network

3. MSTA in Heterogeneous Sensor Network

4. Taxonomy of MSTA problems

5. Conclusion

Outline




Motivation &

MSTA problem




Sensors

Simple sensors

Platforms




Sensors Tasks

Simple sensors

Platforms

e.g. Search-&-Rescue mission




Sensors Tasks

Simple sensors

Platforms

e.g. Search-&-Rescue mission

TASK 1

Injured people to identify

TASK 4

Area Surveillance

(possible threats)

TASK 3

Area Surveillance

(possible threats)

TASK 2Injured

people to identify




Scenario




• An already deployed network of sensors

Scenario





- Support multiple tasks to be accomplished simultaneously

Scenario

TASK 8

Detect vehicles

TASK 7Monitor weather


TASK 3

Area Surveillance

TASK 5

Monitor weather

TASK 1


TASK 6

Identifyevacuation

route

TASK 2

Area Surveillance

TASK 4

Identifyevacuation

route





Scenario

TASK 8

Detect vehicles



TASK 3

Area Surveillance

TASK 5

Monitor weather

TASK 1


TASK 6

Identifyevacuation

route

TASK 2

Area Surveillance

TASK 4

Identifyevacuation

route

- Sensors are scarce and in high demand.





Scenario

TASK 8

Detect vehicles



TASK 3

Area Surveillance

TASK 5

Monitor weather

TASK 1


TASK 6

Identifyevacuation

route

TASK 2

Area Surveillance

TASK 4

Identifyevacuation

route


- Highly dynamic (sensor failures, change of plan)





Scenario


TASK 1


TASK 2

Area Surveillance


- Highly dynamic (sensor failures, change of plan)

“Where is it better to send that particular UAV?”



• We need schemes to allocate sensors to the task they best serve, considering all the relevant parameters.

• In general we can have

• static or mobile sensing devices

• tasks requiring multiple sensors or one sensor

• sensors shared or not shared between multiple tasks

• etc...

MSTA problem









• etc...

MSTA problem


Which sensor should be allocated to which task?

• The fundamental question remains:








• etc...

MSTA problem


Which sensor should be allocated to which task?

Multi-Sensor Task Allocation (MSTA)

• The fundamental question remains:



• MSTA arises in a variety of domains:

• environmental monitoring

• natural disaster (e.g. earthquakes), ....

MSTA applications







• We focus on military/humanitarian scenarios

‣ our allocation mechanisms can be applied to other domains!

MSTA applications









• The task allocation process differs a lot when

‣ in a homogeneous sensor network (e.g. only seismic sensors)

‣ in a heterogeneous sensor network (e.g. seismic+UAVs+UGVs)

MSTA applications









• The task allocation process differs a lot when

‣ in a homogeneous sensor network (e.g. only seismic sensors)

‣ in a heterogeneous sensor network (e.g. seismic+UAVs+UGVs)

• We discuss two examples of MSTA instances in both networks

MSTA applications




MSTA in HomogeneousSensor Network




• Governmental institutions (ARL, MoD), and researchers suggest Wireless Sensor Networks (WSNs) as “the future” for military operations.

Why Homogeneous SN ?





• WSNs are often composed exclusively by hundreds of cheap miniaturized wireless sensors (called motes) with the same sensing capabilities.






• WSNs are often composed exclusively by hundreds of cheap miniaturized wireless sensors (called motes) with the same sensing capabilities.

• In general every network composed exclusively sensors with the same sensing capabilities is called “Homogeneous Sensor Network”.





• We considered a particular instance of the MSTA problem in a Homogeneous Sensor Network (see DCOSS 08).

• Assumptions:

‣ Sensors can serve only one task per time: Single-Task sensors

- Therefore tasks are competing for the exclusive usage of a sensor

‣ A task might require more than one sensor: Multi-Sensor tasks

‣ Available info does not permit planning for future: Instantaneous allocation

Problem settings


T2

T1

x

T1



• Tasks:

‣ vary in priority

‣ have a different demand for sensing resource capabilities

‣ have to respect a budget (e.g. monetary).

• Each sensor:

‣ has a different utility for each task(e.g. geography & distance)

‣ has a different cost for each task.

• Goal: maximizes the utilitywhile not exceeding the budgets of each task.

Formal model


S1

S2

S3

S4

T1

T2

Sensors

Tasks(e11, c11)

(p1, d1, b1)(e

12, c12)

(p2, d2, b2)

e = utility of sensor to a task

c = cost of a sensor to a task

p = task priority

d = task utility demand

b = task budget



• At least as hard as the Knapsack problem which is NP-Complete

➡ we developed heuristic algs and compared them with state of the art pre-existent approaches.

• Simulation environment implemented in Java (with Penn State University)

Allocation algorithms




• At least as hard as the Knapsack problem which is NP-Complete

➡ we developed heuristic algs and compared them with state of the art pre-existent approaches.

• Simulation environment implemented in Java (with Penn State University)

• The algorithm which offers the best trade-off optimality Vs computational cost: MRGAP algorithm:

‣ a centralized algorithm: i.e. we collect all the info about the network in a single node

‣ it can be easily implemented as a distributed algorithm:less communication overhead.

Allocation algorithms




MSTA in HeterogeneousSensor Network




• Sensor network deployment during military/humanitarian missions:

‣ UAV will fly on the battlefield and drop hundreds of motes

‣ Some motes might also be mobile.

Why Heterogeneous SN ?







• Therefore we have a sensor network composed by sensors with different sensing capabilities and mobility capabilities: “Heterogeneous sensor network”








• Therefore we have a sensor network composed by sensors with different sensing capabilities and mobility capabilities: “Heterogeneous sensor network”

• Another example: Sensor Web by Open Geospatial Consortium (OGC) with environmental monitoring applications.





• We considered a particular instance of the MSTA problem in a Heterogeneous Sensor Network.

• Same assumptions:

‣ Single-Task sensors

‣ Multi-Sensor tasks

‣ Instantaneous allocation

• Difference with homogeneous case:

‣ Combined utilities of groups of sensors (bundles) are in general much complex to compute than the homogeneous SN.

Problem settings


T2

T1

x

T1

TASK 1

Area video Surveillance

(possible threats)



• We considered a particular instance of the MSTA problem in a Heterogeneous Sensor Network.

• Same assumptions:

‣ Single-Task sensors

‣ Multi-Sensor tasks

‣ Instantaneous allocation

• Difference with homogeneous case:

‣ Combined utilities of groups of sensors (bundles) are in general much complex to compute than the homogeneous SN.

Problem settings


T2

T1

x

T1

TASK 1

Area video Surveillance

(possible threats)

Sensor bundles



Formal model


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

• We first want to group sensors into bundles, and then we want to find the best assignment of bundles to tasks.



• Problem well studied in Multi-agent Systems: Coalition formation

• Typical approach: combinatorial auction

‣ bidders: tasks

‣ items: sensors

‣ tasks bids for bundles of sensors

Allocation mechanisms (1)




• Problem well studied in Multi-agent Systems: Coalition formation

• Typical approach: combinatorial auction

‣ bidders: tasks

‣ items: sensors

‣ tasks bids for bundles of sensors



• Need to enumerate all possible bundles for each task.

• Large number of sensors and tasks: the computational cost is too large.

• Our contribution:Prune the set of bids placed by tasks (i.e. reduce the number of possible bundles).



• We define a system architecture in which we gradually reduce the search space of the allocation algorithms (see EKAW08).






• Three main components:



Reasoner

Bundle

Generator

AllocationAlgorithms

< Package Config >

{ < Bundle1, e1 >,

< Bundle2, e2 >, ... }

Sensor types compatible with the task.

Sensor Bundles generated based on the package configuration.




• Three main components:

• Current work:Implementing/Testing this approach using the simulation environment “Player/Stage”.



Reasoner

Bundle

Generator

AllocationAlgorithms

< Package Config >

{ < Bundle1, e1 >,

< Bundle2, e2 >, ... }

Sensor types compatible with the task.

Sensor Bundles generated based on the package configuration.



Taxonomy of MSTA problems




• MSTA is closely related to Multi-Robot Task Allocation (MRTA):

“Which robot should execute which task?"

in a Multi-Robot System (MRS).

Related work - MRTA




• MSTA is closely related to Multi-Robot Task Allocation (MRTA):

“Which robot should execute which task?"

in a Multi-Robot System (MRS).

• Gerkey et al (2004) proposed an MRTA taxonomy:

➡ MRTA problems can be viewed as instances of other well-studied,

optimization problems.

➡ therefore allowing comparison of different solutions.

Related work - MRTA




• We propose a preliminary MSTA taxonomy as an extension of MRTA to cover important features of sensor networks (INFOCOM 09).

Preliminary MSTA taxonomy




• We propose a preliminary MSTA taxonomy as an extension of MRTA to cover important features of sensor networks (INFOCOM 09).

Preliminary MSTA taxonomy


• MSTA taxonomy organized on four main axes:

1. Sensors: Single-task (ST) vs. multi-task (MT).

2. Tasks: Single-sensor (SS) vs. multi-sensor (MS).

3. Assignment: Instantaneous (IA) vs. time-extended (TA).

4. Sensor Network: Homogeneous (HO) vs. heterogeneous (HE).

• Example: previously we have considered ST-MS-IA-HO and ST-MS-IA-HE



• We have presented the general MSTA problem,

• Discussed two MSTA instances with applications in military/humanitarian missions,

• Outlined the need for a MSTA taxonomy and presented a preliminary version of it.

Conclusion




• We have presented the general MSTA problem,

• Discussed two MSTA instances with applications in military/humanitarian missions,

• Outlined the need for a MSTA taxonomy and presented a preliminary version of it.

• Future research:

‣ Refine MSTA taxonomy

‣ What are the most important SN features to include?

‣ Explore different MSTA instances:

‣ Heterogeneous SN with Multi-Task sensors (sensors can be shared)

Conclusion




Thanks for listening !

Cardiff School of Computer Science

[email protected]

http://users.cs.cf.ac.uk/D.Pizzocaro



on multi-sensor task allocation

Health & Medicine

multiple sensors

sensors tasks simple

msta applications msta

multiple tasksetc

support multiple tasks

sensor networks

area monitorsurveillance

sensor shouldbe