A distributed sensor relocation scheme for environmental control
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A Distributed Sensor Relocation Scheme for Environmental Control. Michele Garetto , Università di Torino Marco Gribaudo , Università di Torino Carla-Fabiana Chiasserini , Politecnico di Torino Emilio Leonardi , Politecnico di Torino. Outline. Introduction to the problem Our solution

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A Distributed Sensor Relocation Scheme for Environmental Control

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A distributed sensor relocation scheme for environmental control

A Distributed Sensor Relocation Scheme for Environmental Control

Michele Garetto, Università di Torino

Marco Gribaudo, Università di Torino

Carla-Fabiana Chiasserini, Politecnico di Torino

Emilio Leonardi, Politecnico di Torino


Outline

Outline

  • Introduction to the problem

  • Our solution

  • Performance evaluation

  • Conclusions


Mobile sensor networks

Mobile sensor networks ?

  • Traditionally, sensor networks have been assumed to be static…

  • …but mobile sensor networks are becoming real

  • …with many promising applications


Network scenario

Network scenario

  • Large number of self organizing, unattended mobile sensors with actuators (micro-robots)

  • Limited memory/computing capability

  • Short radio range

  • Energy-limited (battery operated)

  • No GPS


Deployment and relocation problem

Deployment and Relocation problem

  • How to achieve coordinated motion of the nodes to improve area coverage and/or relocate upon occurrence of events?

?


Our objective

Our objective

  • Design a unified algorithm to jointly achieve network deployment and relocation

  • Fully distributed solution: no centralized control, no coordination/communication between distant nodes

  • Meet the constraints of the nodes: limited energy, computation, communication capabilities

  • No need of absolute node localization (only relative position of neighboring nodes)


Our approach

Our approach

  • Consider large-scale relocation of the nodes, no fine-grained details (e.g.: filling holes)

  • Take a macroscopic view on how network behaves as a whole

  • Each nodes acts an independent agent and interacts with neighbors according to a simple set of rules

  • Exploit swarm intelligence to achieve self-deployment and relocation as emergent behavior


Our proposed solution

Our proposed solution

  • Customized virtual forces approach

  • The virtual force acting on bode i at time t is:

Friction forces (needed to stabilize the network)

static +viscous

Resultant of attractive/repulsive forces exchanged with neighboring nodes j

Potential force activated only when an event is sensed by the node


Attractive repulsive forces

Attractive/repulsive forces

  • Needed to achieve target distance (Dm) between nodes while maintaining network connectivity (no boundaries)

  • We need to estimate distance (from RSSI) and direction of arrival (DoA) of signals received by each neighbor

    errors considered: distance (±5%), angle (±10°)


Selection of active neighbors

Selection of active neighbors

60°- Δ°

Communication range


Self deployment

Rs

Self-deployment

  • Starting from any (connected) initial topology, the equilibrium configuration tends to a regular triangular lattice

Dm

Optimal coverage when


Example of self deployment

Example of self-deployment

n = 400 nodes


Self deployment coverage results

Our scheme – no errors

Our scheme – with error

Self deployment: coverage results

Rs = 1 n = 400

100

Perfect triangular lattice

Random placement

95

90

85

Coverage Percentage

80

75

70

65

2.4

1.2

1.4

1.6

1.8

2

2.2

Dm


Performance evaluation

Initial topology

Final topology

Performance evaluation

  • Metrics:

    • Time taken to reach final configuration

    • Total movement of the nodes (to save energy)

  • We compare our scheme with the optimum centralized solution reaching the same final configuration:

    • Nodes move at the maximum speed all the time

    • The selection of which node goes where is done solving a minimum Weight Matching (mWM) problem


Comparison with optimum centralized solution mwm

300

250

200

150

100

algorithm - G = 0.01

algorithm - G = 0.001

50

mWM

mWM

0

0

400

800

1200

1600

2000

Time

Comparison with optimum centralized solution (mWM)

350

300

250

Total Movement

200

150

100

50

0

0

100

200

300

400

Time


Relocation upon occurrence of event

Relocation upon occurrence of event

  • Nodes sensing an event are subject to an additional, constant force directed towards the event

  • The objective is to achieve a given node density around the event, possibly keeping a safe distance from it

  • Local density is obtained by dynamically tuning the intensity of the exchange forces among neighboring nodes


Example of event based relocation

Example of event-based relocation


Performance evaluation1

Performance evaluation

  • We compare again our distributed scheme with the optimal centralized one (mWM) which minimizes total node movement

  • We count how many nodes arrive at a given distance d from the event epicenter as a function of time


Comparison between our algorithm and mwm

Comparison between our algorithm and mWM

algorithm

d < 18

400

mWM

350

300

250

d < 12

Number of Sensors

200

150

d < 9

100

50

0

0

500

1000

1500

2000

2500

3000

3500

4000

Time


Optimum relocation mwm

Optimum relocation (mWM)


Limited event detection

Limited event detection


Multiple concurrent events

Multiple concurrent events


Conclusions

Conclusions

  • We have proposed a distributed, unified solution for self-deployment and event-based relocation in mobile sensor networks

  • Simple local rules allow the network to behave as an intelligent swarm

  • Performance comparable with that achieved by centralized optimum solution


A distributed sensor relocation scheme for environmental control

400

R = 80

R = 40

350

R = 30

300

250

Number of Sensors

200

150

100

50

0

0

1000

2000

3000

4000

5000

Time


Results coverage after deployment

Results: coverage after deployment


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