21/04/23 03:23

A Distributed Sensor Relocation Scheme for Environmental Control

Michele Garetto, Università di TorinoMarco Gribaudo, Università di Torino

Carla-Fabiana Chiasserini, Politecnico di Torino

Emilio Leonardi, Politecnico di Torino

Outline

Introduction to the problem Our solution Performance evaluation Conclusions

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

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

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

?

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

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

Customized virtual forces approach The virtual force acting on bode i

at time t is:

Resultant of attractive/repulsive forces exchanged with neighboring nodes j

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

Friction forces (needed to stabilize the network)

static +viscous

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

60°- Δ°

Communication range

Self-deployment Starting from any (connected) initial

topology, the equilibrium configuration tends to a regular triangular lattice

…

… …

…Dm

RsOptimal coverage when

Example of self-deployment

n = 400 nodes

Self deployment: coverage results

65

70

75

80

85

90

95

100

1.2 1.4 1.6 1.8 2 2.2 2.4

Covera

ge P

erc

en

tag

e

Dm

Random placement

Rs = 1 n = 400 Perfect triangular lattice

Our scheme – no errorsOur scheme – with error

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 Initial

topologyFinal topology

Comparison with optimum centralized solution (mWM)

0

50

100

150

200

250

300

350

0 100 200 300 400

Tota

l M

ovem

ent

Time

algorithm - G = 0.01

mWM

0

50

100

150

200

250

300

0 400 800 1200 1600 2000

algorithm - G = 0.001

mWM

Time

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

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

0

50

100

150

200

250

300

350

400

0 500 1000 1500 2000 2500 3000 3500 4000

Num

ber

of

Sen

sors

algorithmmWM

Time

d < 18

d < 12

d < 9

Optimum relocation (mWM)

Limited event detection

Multiple concurrent events

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

0

50

100

150

200

250

300

350

400

0 1000 2000 3000 4000 5000

R = 80R = 40

R = 30

Num

ber

of

Sen

sors

Time

0

50

100

150

200

250

300

350

400

0 1 2 3 4 5 6 7 8

Nu

mb

er

of S

en

so

rs

Distance from Epicenter

{/Symbol b} = 1 - W = 3 - algorithm

{/Symbol b} = 1 - W = 3 - target

{/Symbol b} = 2 - W = 2 - algorithm

{/Symbol b} = 2 - W = 2 - target

W = 1 - algorithm

W = 1 - target

0

1

2

3

4

5

6

1 1.2 1.4 1.6 1.8 2 2.2 2.4

Co

ve

rag

e P

erc

en

tag

e D

eficit

D_r

no errors

errors

Results: coverage after deployment

65

70

75

80

85

90

95

100

1.2 1.4 1.6 1.8 2 2.2 2.4

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

Co

ve

rag

e P

erc

en

tag

e

Ne

two

rk A

rea

D_r

G = 0.001 - no errors

G = 0.01 - no errors

G = 0.001 - errors

G = 0.01 - errors

triangular lattice

random deployment

network area