This document summarizes Diego Pizzocaro's seminar on multi-sensor task allocation held at the University of Padova in April 2009. The seminar covered multi-sensor task allocation problems in both homogeneous and heterogeneous sensor networks. It discussed motivations and applications of sensor networks, presented examples of task allocation problems, and outlined algorithms for allocating sensors to tasks in different network types.
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On Multi-Sensor Task Allocation
1. Seminar @ DEI
Cardiff School of Computer Science University of Padova (Italy)
April, 2009
On Multi-Sensor
Task Allocation
Diego Pizzocaro
PhD candidate
Supervisors:
Prof. Alun Preece - Dr. Roger Whitaker
http://users.cs.cf.ac.uk/D.Pizzocaro D.Pizzocaro@cs.cf.ac.uk
2. Brief Bio
• 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
U.K. Ministry of Defence U.S. Army Research Lab
• Our research focus: intelligent resource allocation in sensor networks.
http://users.cs.cf.ac.uk/D.Pizzocaro D.Pizzocaro@cs.cf.ac.uk
3. Outline
1. Motivations & MSTA problem
2. MSTA in Homogeneous Sensor Network
3. MSTA in Heterogeneous Sensor Network
4. Taxonomy of MSTA problems
5. Conclusion
http://users.cs.cf.ac.uk/D.Pizzocaro D.Pizzocaro@cs.cf.ac.uk
4. Motivation
&
MSTA problem
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8. Sensors Tasks
Simple sensors e.g. Search-&-Rescue mission
TASK 3
TASK 4
Area
Surveillance
Area (possible threats)
TASK 1
Platforms Surveillance
(possible threats) Injured
people to TASK 2
identify Injured
people to
identify
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10. Scenario
• An already deployed network of sensors
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11. Scenario
TASK 3
TASK 7
Monitor
Area
weather
Surveillance TASK 4
TASK 6
Identify
Identify evacuation
route
evacuation
route
TASK 2
TASK 5 TASK 8
TASK 1 Area
Monitor Surveillance Detect
weather Injured vehicles
people to
identify
• An already deployed network of sensors
- Support multiple tasks to be accomplished simultaneously
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12. Scenario
TASK 3
TASK 7
Monitor
Area
weather
Surveillance TASK 4
TASK 6
Identify
Identify evacuation
route
evacuation
route
TASK 2
TASK 5 TASK 8
TASK 1 Area
Monitor Surveillance Detect
weather Injured vehicles
people to
identify
• An already deployed network of sensors
- Support multiple tasks to be accomplished simultaneously
- Sensors are scarce and in high demand.
http://users.cs.cf.ac.uk/D.Pizzocaro D.Pizzocaro@cs.cf.ac.uk
13. Scenario
TASK 3
TASK 7
Monitor
Area
weather
Surveillance TASK 4
TASK 6
Identify
Identify evacuation
route
evacuation
route
TASK 2
TASK 5 TASK 8
TASK 1 Area
Monitor Surveillance Detect
weather Injured vehicles
people to
identify
• An already deployed network of sensors
- Support multiple tasks to be accomplished simultaneously
- Sensors are scarce and in high demand.
- Highly dynamic (sensor failures, change of plan)
http://users.cs.cf.ac.uk/D.Pizzocaro D.Pizzocaro@cs.cf.ac.uk
14. Scenario
“Where is it better to send that particular UAV?”
TASK 2
TASK 1 Area
Surveillance
Injured
people to
identify
• An already deployed network of sensors
- Support multiple tasks to be accomplished simultaneously
- Sensors are scarce and in high demand.
- Highly dynamic (sensor failures, change of plan)
http://users.cs.cf.ac.uk/D.Pizzocaro D.Pizzocaro@cs.cf.ac.uk
15. MSTA problem
• 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...
http://users.cs.cf.ac.uk/D.Pizzocaro D.Pizzocaro@cs.cf.ac.uk
16. MSTA problem
• 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...
• The fundamental question remains:
Which sensor should
be allocated to which task?
http://users.cs.cf.ac.uk/D.Pizzocaro D.Pizzocaro@cs.cf.ac.uk
17. MSTA problem
• 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...
• The fundamental question remains:
Which sensor should Multi-Sensor Task Allocation
be allocated to which task? (MSTA)
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18. MSTA applications
• MSTA arises in a variety of domains:
• environmental monitoring
• natural disaster (e.g. earthquakes), ....
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19. MSTA applications
• MSTA arises in a variety of domains:
• environmental monitoring
• natural disaster (e.g. earthquakes), ....
• We focus on military/humanitarian scenarios
‣ our allocation mechanisms can be applied to other domains!
http://users.cs.cf.ac.uk/D.Pizzocaro D.Pizzocaro@cs.cf.ac.uk
20. MSTA applications
• MSTA arises in a variety of domains:
• environmental monitoring
• natural disaster (e.g. earthquakes), ....
• We focus on military/humanitarian scenarios
‣ our allocation mechanisms can be applied to other domains!
• 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)
http://users.cs.cf.ac.uk/D.Pizzocaro D.Pizzocaro@cs.cf.ac.uk
21. MSTA applications
• MSTA arises in a variety of domains:
• environmental monitoring
• natural disaster (e.g. earthquakes), ....
• We focus on military/humanitarian scenarios
‣ our allocation mechanisms can be applied to other domains!
• 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
http://users.cs.cf.ac.uk/D.Pizzocaro D.Pizzocaro@cs.cf.ac.uk
22. MSTA
in Homogeneous
Sensor Network
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23. Why Homogeneous SN ?
• Governmental institutions (ARL, MoD), and researchers suggest Wireless
Sensor Networks (WSNs) as “the future” for military operations.
http://users.cs.cf.ac.uk/D.Pizzocaro D.Pizzocaro@cs.cf.ac.uk
24. Why Homogeneous SN ?
• Governmental institutions (ARL, MoD), and researchers suggest Wireless
Sensor Networks (WSNs) as “the future” for military operations.
• WSNs are often composed exclusively by hundreds of cheap miniaturized
wireless sensors (called motes) with the same sensing capabilities.
http://users.cs.cf.ac.uk/D.Pizzocaro D.Pizzocaro@cs.cf.ac.uk
25. Why Homogeneous SN ?
• Governmental institutions (ARL, MoD), and researchers suggest Wireless
Sensor Networks (WSNs) as “the future” for military operations.
• 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”.
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26. Problem settings
• 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
x T2
T1
‣ A task might require more than one sensor: Multi-Sensor tasks
T1
‣ Available info does not permit planning for future: Instantaneous allocation
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27. Formal model
• Tasks: Sensors
‣ vary in priority S1
(e11, c11
Tasks
)
‣ have a different demand for sensing resource (e
12
,c (p1, d1, b1)
capabilities
T1
12
)
‣ have to respect a budget (e.g. monetary). S2
• Each sensor:
T2 (p2, d2, b2)
‣
S3
has a different utility for each task
(e.g. geography & distance)
‣ has a different cost for each task.
S4
e = utility of sensor to a task
• Goal: c = cost of a sensor to a task
maximizes the utility p = task priority
while not exceeding the budgets of each task. d = task utility demand
b = task budget
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28. 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)
http://users.cs.cf.ac.uk/D.Pizzocaro D.Pizzocaro@cs.cf.ac.uk
29. 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.
http://users.cs.cf.ac.uk/D.Pizzocaro D.Pizzocaro@cs.cf.ac.uk
30. MSTA
in Heterogeneous
Sensor Network
http://users.cs.cf.ac.uk/D.Pizzocaro D.Pizzocaro@cs.cf.ac.uk
31. Why Heterogeneous SN ?
• Sensor network deployment during military/humanitarian missions:
‣ UAV will fly on the battlefield and drop hundreds of motes
‣ Some motes might also be mobile.
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32. Why Heterogeneous SN ?
• Sensor network deployment during military/humanitarian missions:
‣ UAV will fly on the battlefield and drop hundreds of motes
‣ Some motes might also be mobile.
• Therefore we have a sensor network composed by sensors with different sensing
capabilities and mobility capabilities: “Heterogeneous sensor network”
http://users.cs.cf.ac.uk/D.Pizzocaro D.Pizzocaro@cs.cf.ac.uk
33. Why Heterogeneous SN ?
• Sensor network deployment during military/humanitarian missions:
‣ UAV will fly on the battlefield and drop hundreds of motes
‣ Some motes might also be mobile.
• 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.
http://users.cs.cf.ac.uk/D.Pizzocaro D.Pizzocaro@cs.cf.ac.uk
34. Problem settings
• We considered a particular instance of the MSTA problem in a Heterogeneous
Sensor Network.
x
• Same assumptions: T2
T1
‣ Single-Task sensors
‣ Multi-Sensor tasks
‣ Instantaneous allocation
T1
• Difference with homogeneous case:
‣ Combined utilities of groups of sensors (bundles) are in general much
complex to compute than the homogeneous SN.
TASK 1
Area video
Surveillance
(possible threats)
http://users.cs.cf.ac.uk/D.Pizzocaro D.Pizzocaro@cs.cf.ac.uk
35. Problem settings
• We considered a particular instance of the MSTA problem in a Heterogeneous
Sensor Network.
x
• Same assumptions: T2
T1
‣ Single-Task sensors
‣ Multi-Sensor tasks
‣ Instantaneous allocation
T1
• Difference with homogeneous case:
‣ Combined utilities of groups of sensors (bundles) are in general much
complex to compute than the homogeneous SN.
Sensor bundles
TASK 1
Area video
Surveillance
(possible threats)
http://users.cs.cf.ac.uk/D.Pizzocaro D.Pizzocaro@cs.cf.ac.uk
36. Formal model
• We first want to group sensors into bundles,
and then we want to find the best assignment of bundles to tasks.
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
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37. 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
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38. 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).
http://users.cs.cf.ac.uk/D.Pizzocaro D.Pizzocaro@cs.cf.ac.uk
39. Allocation mechanisms (2)
• We define a system architecture in which we gradually reduce the search space of the
allocation algorithms (see EKAW08).
http://users.cs.cf.ac.uk/D.Pizzocaro D.Pizzocaro@cs.cf.ac.uk
40. Allocation mechanisms (2)
• We define a system architecture in which we gradually reduce the search space of the
allocation algorithms (see EKAW08).
• Three main components:
Reasoner
< Package Config >
Sensor types compatible
with the task.
Bundle
Generator
{ < Bundle1, e1 >,
< Bundle2, e2 >, ... } Sensor Bundles generated based
on the package configuration.
Allocation
Algorithms
http://users.cs.cf.ac.uk/D.Pizzocaro D.Pizzocaro@cs.cf.ac.uk
41. Allocation mechanisms (2)
• We define a system architecture in which we gradually reduce the search space of the
allocation algorithms (see EKAW08).
• Three main components:
Reasoner
< Package Config >
Sensor types compatible
with the task.
Bundle
Generator
{ < Bundle1, e1 >,
< Bundle2, e2 >, ... } Sensor Bundles generated based
on the package configuration.
Allocation
Algorithms
• Current work:
Implementing/Testing this approach using the simulation environment “Player/Stage”.
http://users.cs.cf.ac.uk/D.Pizzocaro D.Pizzocaro@cs.cf.ac.uk
42. Taxonomy of
MSTA problems
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43. 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).
http://users.cs.cf.ac.uk/D.Pizzocaro D.Pizzocaro@cs.cf.ac.uk
44. 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.
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45. Preliminary MSTA taxonomy
• We propose a preliminary MSTA taxonomy as an extension of MRTA to cover
important features of sensor networks (INFOCOM 09).
http://users.cs.cf.ac.uk/D.Pizzocaro D.Pizzocaro@cs.cf.ac.uk
46. Preliminary MSTA taxonomy
• We propose a preliminary MSTA taxonomy as an extension of MRTA to cover
important features of sensor networks (INFOCOM 09).
• 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
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47. 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.
http://users.cs.cf.ac.uk/D.Pizzocaro D.Pizzocaro@cs.cf.ac.uk
48. 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)
http://users.cs.cf.ac.uk/D.Pizzocaro D.Pizzocaro@cs.cf.ac.uk
49. Thanks for listening !
Cardiff School of Computer Science
D.Pizzocaro@cs.cf.ac.uk
http://users.cs.cf.ac.uk/D.Pizzocaro