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
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