Wireless sensor networks - FESB

brainybootsMobile - Wireless

Nov 21, 2013 (3 years and 10 months ago)

59 views

Mario
Čagalj

supervised by prof. Jean
-
Pierre Hubaux (EPFL
-
DSC
-
ICA)

and prof.
Christian

Enz (EPFL
-
DE
-
LEG, CSEM)


mario.cagalj@epfl.ch

Wireless Sensor Networks:
Minimum
-
energy communication

2

Wireless Sensor Networks: Minimum
-
energy communication


Large number of

heterogeneous sensor devices



Ad Hoc Network


Sophisticated
sensor devices


communication
,
processing
,
memory capabilities


Wireless Sensor Networks

3

Wireless Sensor Networks: Minimum
-
energy communication

Project Goals


Devise a set communication mechanisms s.t.
they


Minimize energy consumption


Maximize network nodes’ lifetimes


Distribute energy load evenly throughout a network


Are scalable (distributed)


4

Wireless Sensor Networks: Minimum
-
energy communication

Minimum
-
energy unicast

5

Wireless Sensor Networks: Minimum
-
energy communication

c
AB
C
c
AE
D
E
A
B
c
ED
c
BC
c
CD
1
C
D
E
A
B
1
1
1
1
Unicast communication model


Link
-
based model


each link weighed


how to chose a weight?



Power
-
Aware Metric

[Chang00]


Maximize nodes’ lifetimes


include
remaining battery energy (
Ei
)


2
1
)
0
(
x
i
E
i
E
x
r
ij
e
ij
c














receiving
in
spent
energy
0
tting
in transmi
spent
energy


r
ij
e
6

Wireless Sensor Networks: Minimum
-
energy communication

Unicast problem description


Definitions


undirected graph
G = (N, L)


links are weighed by costs


the path
A
-
B
-
C
-
D
is a
minimum cost path
from node A to node D, which is the one
-
hop neighbour of the sink node


minimum costs

at node A are total costs
aggregated along minimum cost paths





Minimum cost topology


Minimum Energy Networks [Rodoplu99]


optimal spanning tree rooted at one
-
hop
neighbors of the sink node


each node considers only its
closest

neighbors
-

minimum neighborhood




A

B

C

D

7

Wireless Sensor Networks: Minimum
-
energy communication

Building minimum cost topology


Minimum neighborhood


notation:
-

minimum neighborhood of node


P1:

minimum number of nodes enough to ensure connectivity


P2:

no node falls into the
relay space

of any other node


Finding a minimum neighborhood


nodes maintain a matrix of mutual link costs among neighboring
nodes (
cost matrix
)


the cost matrix defines a subgraph
H
on the network graph
G

N
i

i
N
i
N

i
N

















1
1
1
1
1
54
53
52
51
45
43
42
41
35
34
32
31
25
24
23
21
15
14
13
12
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
c
A

B

C

8

Wireless Sensor Networks: Minimum
-
energy communication

Finding minimum neighborhood



We apply
shortest path algorithm

to find optimal
spanning tree rooted at the given node






Theorem 1:

The nodes that immediately follow the root
node constitute the minimum neighborhood of the root
node


Theorem 2:

The
minimum cost

routes are
contained

in
the minimum neighborhood


Each node considers just its min. neighborhood

subgraph H

9

Wireless Sensor Networks: Minimum
-
energy communication

Distributed
algorithm


Each node maintains
forwarding table


E.g.

[originator ¦ next hop ¦ cost ¦ distance]



Phase 1:



find minimum neighborhood


Phase 2:


each node
sends

its
minimum

cost

to it neighbors


upon receiving min. cost

update forwarding table



Eventually the minimum cost topology is built



10

Wireless Sensor Networks: Minimum
-
energy communication

An example of data routing



Properties



energy efficiency



scalability




increased fault
-
tolerance




Different routing policies



different packet priorities



nuglets
[Butt01]



packets flow toward nodes with


lower costs

11

Wireless Sensor Networks: Minimum
-
energy communication

Minimum
-
energy broadcast

12

Wireless Sensor Networks: Minimum
-
energy communication

Broadcast communication model

a

c

b

E
ab

E
ac

E
bc


Omnidirectional antennas


By transmitting at the power level
max{E
ab
,E
ac
}

node
a

can reach both node
b

and node
c

by a single
transmission


Wireless Multicast Advantage

(WMA) [Wieselthier et al.]



Power
-
aware metric


include
remaining battery energy (
Ei
)


embed

WMA
(e
j
/N
j
)



Trade
-
off between the spent energy and
the number of newly reached nodes

set

uncovered

s
'

node

and


nodes

of
set

g
overlappin
ood
neighbourh

s
'

node
j
U
j
i
O
j
N
j
ij
j



3
2
1
b
)
(
X
X
j
j
X
j
j
j
U
E
E
e
c










Every node
j

is assigned a

broadcast cost

b
j
c
13

Wireless Sensor Networks: Minimum
-
energy communication

Broadcast cover problem (BCP)


Set cover problem







)}
(
{
min
arg
*
cover

Find

)
(
)
(
with
associated

)
(

.
.



},
,...,
1
{
:
:
i
C
cost
C
j
S
cost
C
cost
C
j
S
j
S
cost
j
S
N
t
s
F
C
Covering
N
j
S
m
S
S
F
i
C
j
S
j
C
j
S
j
C
















S
1
S
2
S
3
S
4
S
5
)
(
)
(

,
)
(
)
(

,
2
1
2
1
2
1
C
cost
C
cost
C
cost
C
cost
C
C





C
1
={S
1,
S
2,
S
3
}

C
2
={S
3,
S
4,
S
5
}

C
*
=

Example:

originator
at

rooted

tree
a

to
belong

nodes

forwarding

of
set

The
cost
cover
broadcast

minimizes

cover that

Find
cost
cover
broadcast
)
(
)
(








C
cost
e
j
S
cost
N
S
j
j
j

BCP







Greedy algorithm:


at each iteration add the set S
j

that minimizes
ratio

cost(S
j
)/(#newly covered nodes)

3
2
1
b
)
(
X
X
j
j
X
j
j
j
U
E
E
e
c









14

Wireless Sensor Networks: Minimum
-
energy communication

Distributed algorithm for BCP


Phase 1:



learn neighborhoods (overlapping sets)


Phase 2:

(upon receiving a bcast msg)

1:

if neighbors covered
HALT

2:

recalculate the broadcast cost

3:

wait for a random time before re
-
broadcast

4:

if receive duplicate msg in the mean time goto
1:



Random time calculation



random number distributed uniformly between 0 and










b
b
i
c
c
0
15

Wireless Sensor Networks: Minimum
-
energy communication

Simulations


GloMoSim [UCLA]


scalable simulation environment for wireless and wired networks

average node degree ~ 6

average node degree ~ 12

16

Wireless Sensor Networks: Minimum
-
energy communication

Simulation results (1/2)

17

Wireless Sensor Networks: Minimum
-
energy communication

Simulation results (2/2)

18

Wireless Sensor Networks: Minimum
-
energy communication

Conclusion and future work


Power
-
Aware Metrics


t
rade
-
off between
residual battery
capacity

and transmission
power are necessary


Scalability


each node executes a simple
localized
algorithm


U
nicast communication


link based model


B
roadcast communication


node based model


Can we do better by exploiting WMA properly?


19

Wireless Sensor Networks: Minimum
-
energy communication

Minimum
-
energy broadcast


Propagation model:


Omnidirectional antennas


Wireless Multicast Advantage

(WMA) [Wieselthier et al.]


a

c

b

P
ab

P
ac

P
bc

if

(
P
ac



P
ab
<
P
bc
)
then

transmit at

P
ac


Minimum
-
energy broadcast:

]
6
..
2
[

,




ab
ab
kd
P

Challenges
:



As the number of destination increases the complexity of this formulation increases rapidly.


Requirement
for distributed algorithm.


What are good
criteria
for selecting forwarding nodes?


Broadcast Incremental Power (BIP)
[Wieselthier et al.]


Add a node at
minimum additional cost


Centralized


Cost (BIP) <= Cost (MST)




Improvements?


Take MST as a reference


Branch exchange heuristic…


… to embed WMA in MST





10
9
4
1
3
2
8
6
5
7
1
5
8
4
2
2
5
5
4
- forwarding nodes