Data Communications (Sensor Network) 1
Energy-Efficient Routing with Reliability Constraint
Team 2
Hojoong Kwon
Taehyun Kim
Data Communications (Sensor Network) 2
“Routing for Maximum System Lifetime in Wireless Ad-hoc Networks ”
Annual Allerton Conference on Communication 1999
J.-H. Chang and L. Tassiulas
Data Communications (Sensor Network) 3
Contents Problem Suggestion Solutions
Optimal Energy Consumption FR(Flow Redirection) MREP(Maximum Residual Energy Path Routing)
Simulation Results Conclusion Comments
Data Communications (Sensor Network) 4
Problem Suggestion
Many routing algorithms are focused on minimum energy dissipating path.
Nodes in that path will be drained out quickly
To maximize the system lifetime, energy dissipating load should be distributed to all nodes in the network
Minimum energy path
I’m DIEIN
G!
Data Communications (Sensor Network) 5
Solutions How can we distribute energy dissipating load?
Using multiple paths! FR (Flow Redirection)
Data route which causes a early system halt is redirected by using other nodes.
Maximum Residual Energy Path Routing Optimal Energy Consumption
This is computed by linear programming
Data Communications (Sensor Network) 6
Optimal Energy Consumption
Linear programming problem
Data Communications (Sensor Network) 7
Flow Redirection Algorithm FR is motivated by the following observation Theorem 1(Necessary optimality condition) - If the
minimum lifetime over all nodes is maximized then the minimum lifetime of each path flow from the origin to the destination with positive flow has the same value as the other paths.
Data Communications (Sensor Network) 8
Flow Redirection – con’t
Outgoing flow redirection Procedure
Determine the from which path to which path Calculate the amount of redirection (εi) Redirect the flow properly
+εi
-εi
Multihop routes
Giver
Taker
Data Communications (Sensor Network) 9
Flow Redirection – con’t
Firstly, define followings
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Flow Redirection – con’t Determine the from which path to which path
If (node i’s lifetime should be increased),
If ,
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Flow Redirection – con’t Calculate the amount of redirection flow (εi)
Constrains
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Flow Redirection – con’t Redirect the flow properly
Add & Subtract ei properly Adding ei is easy, but subtracting is not easy since there may be
some links in the path whose flow is less than ei. Subtracting procedure
Subtract ei from qig If qjk (g<j<d, j<k<d) < ei , subtract qjk from node j to node k
recursively Subtract ei -n· qjk
Data Communications (Sensor Network) 13
MREP Routing Maximum Residual Energy Path Routing Define Lp as a vector whose elements are the reciprocal of the
residual energy for each link in the path after the route has been used by a unit flow Element of Lp for link (j,k) is
: Residual Energy : Unit flow
The largest element (the least energy node) comparing
Data Communications (Sensor Network) 14
Simulation Results Performance Measure of algorithm X
Random graph generation 5x5 square space 20 nodes(5 origins & 2 destinations) Initial energy = 1, generation rate Q = 1 TX range : 2.5 Energy expenditure per bit TX from i to j is
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Simulation Results MTE(Minimum Transmitted Energy routing)
The shortest path algorithm based on energy expenditures per bit transmission.
The performance comparison
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Conclusion To maximize the lifetime, the traffic is routed so that the energy
consumption is balanced.
FR & MREP algorithm are local and amenable to distributed implementation with close to optimal performance.
Data Communications (Sensor Network) 17
Comments Bellman-Ford algorithm is used
Stationary topology is needed to be assumed Setting up procedure has to be preceded for lifetime & energy
consumption calculation.
Is MREP better than FR? If so, what can we gain by using FR?
Routing overhead is not considered Maybe it is quite serious.
Author did not mention how to distribute this algorithm
Data Communications (Sensor Network) 18
Reliability Definition
End-to-end, event-to-sink, sink-to-sources Reliable data transmission, reliable event detection
Management MAC layer vs. transport layer hop-by-hop recovery vs. end-to-end recovery
Our approach Energy-efficient routing algorithm while guaranteeing reliable
end-to-end transmission
Data Communications (Sensor Network) 19
“Providing Application QoS through Intelligent Sensor Management”
WSNA 2003
“Optimal Sensor Management Under Energy and Reliability Constraints”
WCNC 2003
M. Perillo and W. B. Heinzelman
Data Communications (Sensor Network) 20
Introduction Maximize lifetime while meeting application-specific
QoS (reliability)
Only certain subsets of sensors may satisfy reliability constraint.
Two strategies Turn off redundant sensors Energy-efficient routing
Data Communications (Sensor Network) 21
Example Problem Wish to detect the presence of phenomena
anywhere in the observation space
Feasible sensor sets
• F1 = { S1, S2 }
• F2 = { S1, S5, S6 }
• F3 = { S2, S3, S4 }
Data Communications (Sensor Network) 22
Problem Formulation Given
Feasible set : Feasible set makeup
Path makeup
: sensing power and transmission power : receiving, processing and transmission power
( when routing sensor Sj2’s data )
}},...,1{,{ Fi NiFF
0
1ija
0
121 ljjr
Sensor Sj is in set Fi
Else
Sensor Sj1 is included on Sj2‘s lth path
Else
jsP ,
21, jjrP
Data Communications (Sensor Network) 23
Problem Formulation Find
Total time that the set Fi is used :
Fraction of time that path l is used to route Sj’s data during the time that Fi is used :
Energy constraint
Maximize
iT
jliF
1
2
2,
222121111 1 1
,1
, j
N
i
N
j
N
liijlijjjrljj
N
iijsij ETafPrTPa
F S jPF
FN
iiT
1
0
1,
1
jPN
ljlif
data sink not in Sj’s tx range
otherwise
Data Communications (Sensor Network) 24
Maximum Flow Graph Problem
ds
S4
S1
S2
S3
F3
F1
F2
P211
P431
P432
R21
R43
Energy
Time
Data Communications (Sensor Network) 25
Maximum Flow Graph Problem
S3
4
2
1
S3
4
2
1
S3
4
2
1
1F 2F 3F
Data Communications (Sensor Network) 26
Maximum Flow Graph Problem
s
S4
S1
S2
S3
Capacities on arcs from initial energy constraint
4E
F3
4,34
1
sPa
Arc multipliers to convert energy to time
P431
P432
S4 has 2 valid routes
41,31
1
rPa
Arc multipliers to normalize time contribution
R4
d
2
12
1
1
Energy
Time
Data Communications (Sensor Network) 27
Simulation Results Lifetime vs. transmission range
Environment dimension : 100m x 100m Number of sensors : 100
Data Communications (Sensor Network) 28
Simulation Results Lifetime vs. number of sensors
Transmission range : 25m
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Simulation Results Lifetime vs. field size
Node density : 0.01 node/m2
Data Communications (Sensor Network) 30
Conclusions & Comments Joint optimization of sensor scheduling and data
routing
Optimally balance the tradeoff between application performance and network lifetime
Centralized solution using global information High computation and signaling cost
It may not be easy to find the feasible sensor sets.