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
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