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Mario Čagalj
supervised by prof. Jean-Pierre Hubaux (EPFL-DSC-ICA)
and prof. Christian Enz (EPFL-DE-LEG, CSEM)
Wireless Sensor Networks: Minimum-energy communication
Wireless Sensor Networks: Minimum-energy communication
2
Large number of heterogeneous sensor devices Ad Hoc Network
Sophisticated sensor devices communication, processing, memory capabilities
Wireless Sensor Networks
Wireless Sensor Networks: Minimum-energy communication
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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)
Wireless Sensor Networks: Minimum-energy communication
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Minimum-energy unicast
Wireless Sensor Networks: Minimum-energy communication
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cA B
C
cA EDEA
B
cE D
cB C
cC D
1
C
DEA
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)
21)0
(x
iEiE
xrijeijc
receivingin spent energy 0
ttingin transmispent energy
rije
Wireless Sensor Networks: Minimum-energy communication
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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 AB
C
D
Wireless Sensor Networks: Minimum-energy communication
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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
Ni iN
iN iN
1
1
1
1
1
54535251
45434241
35343231
25242321
15141312
cccc
cccc
cccc
cccc
cccc
AB
C
Wireless Sensor Networks: Minimum-energy communication
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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
Wireless Sensor Networks: Minimum-energy communication
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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
Wireless Sensor Networks: Minimum-energy communication
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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
Wireless Sensor Networks: Minimum-energy communication
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Minimum-energy broadcast
Wireless Sensor Networks: Minimum-energy communication
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Broadcast communication model
a c
b
Eab
Eac
Ebc
Omnidirectional antennas By transmitting at the power level max{Eab,Eac} 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 (ej/Nj)
Trade-off between the spent energy and the number of newly reached nodes
set uncovered s' node
and nodes ofset goverlappin
oodneighbourh s' node
jU
jiO
jN
j
ij
j
3
2
1
b
)(X
X
j
jXj
jjU
E
Ee
c
Every node j is assigned a broadcast cost bjc
Wireless Sensor Networks: Minimum-energy communication
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Broadcast cover problem (BCP) Set cover problem
)}({minarg*cover Find
)()(
with associated )(
..
},,...,1{
:
:
iCcostC
jScostCcost
CjSjScost
jSNtsFCCovering
NjSmSSF
i
Cj
Sj
Cj
Sj
C
S 1 S2 S3
S 4
S 5 )()( ,
)()( ,
21
21
2
1
CcostCcost
CcostCcost
C
C
C1={S1, S2, S3}
C2={S3, S4, S5}
C*=
Example:
originatorat rooted treea tobelong nodes forwarding ofset The
costcover broadcast minimizes cover that Find
costcover broadcast )(
)(
Ccost
ejScost
NS
j
jj
BCP Greedy algorithm:
at each iteration add the set Sj that minimizes ratio cost(Sj)/(#newly covered nodes)
3
2
1
b
)(X
X
j
jXj
jjU
E
Ee
c
Wireless Sensor Networks: Minimum-energy communication
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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
cc
0
Wireless Sensor Networks: Minimum-energy communication
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Simulations GloMoSim [UCLA]
scalable simulation environment for wireless and wired networks
average node degree ~ 6
average node degree ~ 12
Wireless Sensor Networks: Minimum-energy communication
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Simulation results (1/2)
Wireless Sensor Networks: Minimum-energy communication
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Simulation results (2/2)
Wireless Sensor Networks: Minimum-energy communication
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Conclusion and future work Power-Aware Metrics
trade-off between residual battery capacity and transmission power are necessary
Scalability each node executes a simple localized algorithm
Unicast communication link based model
Broadcast communication node based model Can we do better by exploiting WMA properly?
Wireless Sensor Networks: Minimum-energy communication
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Minimum-energy broadcast Propagation model: Omnidirectional antennas Wireless Multicast Advantage (WMA) [Wieselthier et
al.]a c
b
Pab
Pac
Pbc
if (Pac – Pab < Pbc) then transmit at Pac
Minimum-energy broadcast:
]6..2[ , abab kdP
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
109
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1
3
2
86
57
15
8
4
2
2 5
5
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- forwarding nodes