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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

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

Dm

Optimal coverage when

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
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

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
slide24
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

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