1. ADVISOR : Professor Yeong-Sung Lin STUDENT : Hung-Shi Wang
An Energy and Delay Efficient Data - Centric Scheduling Algorithm for Wireless Sensor Networks
ADVISOR : Professor Yeong-Sung Lin
STUDENT : Hung-Shi Wang
Presented by Hung-Shi Wang 2005/12/20
2019/2/15

2. Outline Introduction Problem Description Related Work Motivation
Notation
Problem Formulation
Lagrangean Relaxation
Subproblem and Solution Approach
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3. Related work SMAC TMAC DMAC Max-Min Fair Collision-Free Scheduling
Fixed duty cycle, sleep latency
TMAC
Sleep latency
DMAC
Data aggregation
Max-Min Fair Collision-Free Scheduling
ELECTION
Not Optimal
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4. Motivation
To propose an algorithm that achieves energy efficiency, data aggregation and ensures low latency.
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5. Problem description
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6. Problem Description Given Objective: Subject to:
The set of all sensor nodes
The set of all data sources
The sink node
The set of all candidate paths for each data source to reach sink node
Average packet arrival rate for each sensor node on data aggregation tree
Maximum propagation delay for transmission data packet
Transmission time for RTS, CTS, ACK frame
waiting time for SIFS, DIFS
Objective:
To minimize the total energy consumption
Subject to:
Routing constraint
Tree constraint
Maximum end-to-end delay constraint
Minimum begin time constraint
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7. Problem Description To determine: Routing path for each data source.
Whether a link should be on the data aggregation tree
The data aggregation tree
Maximum end-to-end delay for each sensor node on data aggregation tree
Minimum begin time of each sensor node on data aggregation tree
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8. Notation – Given Parameter
Description V
The set of sensor nodes Ps
The set of all possible paths from the data source s node to the sink node S
The set of all data source nodes H
Longest distance of shortest path to reach farthest data source node M
An arbitrary large number A
The possible maximum link delay Q
The sink node 
Packet arrival rate
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9. Notation – Given Parameter(cont.)
Description 
Maximum propagation delay for transmitting data packet
Average random backoff time
Average network allocation vector(NAV)
SIFS
Short inter-frame space time
DIFS
Distributed inter-frame space time
RTS
Transmission time for RTS frame
CTS
Transmission time for CTS frame Es
Energy consumption when sensor nodes are sending Er
Energy consumption when sensor nodes are receiving
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10. Notation – Given Parameter(cont.)
Description
The indicator function which is 1 if the link (u, v) is on the path p and 0 otherwise
The indicator function 1 if the node v is covered within transmission radius of the node u and 0 otherwise
Estimate error
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11. Notation – Decision Variables
Description
1 if the data source node uses the path p to reach the sink node
1 if the link (u, v) is on the tree
Data transmission delay from the node u to the node v
Data transmission time from the node u to the node v
Maximum end-to-end delay from leaf nodes to the node v on data aggregation tree
Minimum begin time of all flows to node v
1 if the node v is covered within interference range of the node u
1 if the maximum end-to-end delay from leaf nodes to node u is large than the minimum begin time of all flows to node v.
1 if the maximum end-to-end delay from leaf nodes to node v is large than the minimum begin time of all flows to node u.
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12. The relationship between nu mu nv and mv
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13. The relationship between nu mu nv and mvif
the communication between node u and node v exist interference
and if
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14. Problem Formulation Min subject to: Routing Constraint Tree Constraint
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15. Problem Formulation(cont.)
Minimum and Maximum End to End delay Constraint
Number of neighbors Constraint
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16. Problem Formulation(cont.)
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17. Approximated Function
For convenience of applying our solution approach to this model, we make some transformations on constrain (17) in order to make (IP) solvable.
Constraint (17) can be approximated by :
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18. Approximated Function
We take natural logarithm on both sides in order to make this function solvable
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19. Relaxation
In (IP), by introducing Lagrangean multiplier vector u1,u2, u3, u4, u5, u6, u7, u8, u9, u10 , u11 , u12 , u13 we dualize Constraints (2), (4), (8), (9), (10), (11), (12), (13), (14) , (15) , (16) , (17) and (18) to obtain the following Lagrangean relaxation problem(LR).
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20. Relaxation(cont.)
Min
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21. Relaxation(cont.)
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22. Relaxation(cont.)
subject to:
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23. Relaxation(cont.)
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24. Subproblem relate objective function min s.t.
(SUB 1) can be further decomposed into
independent shortest path problems
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25. Subproblem
relate
objective function
min
s.t.
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26. Subproblem
relate
objective function
min
s.t.
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27. Transformation
After transforming, we can decompose (SUB 3) into | V | independent subproblems. For each node u,
s.t.
calculate
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28. Subproblem
relate
objective function
min
s.t.
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29. Transformation
After transforming, we can decompose (SUB 4) into | V | independent subproblems. For each node u,
s.t.
calculate
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30. Subproblem
relate
objective function
min
s.t.
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31. Subproblem
relate
objective function
min
s.t.
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32. Subproblem
relate
objective function
min
s.t.
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33. Transformation
After transforming, we can decompose (SUB 6) into | V X V | independent subproblems. For each link (u, v),
s.t.
calculate
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34. Subproblem
relate
objective function
min
s.t.
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35. Transformation
(SUB 7) can be decomposed into | V X V | independent subproblems. For each link (u, v),
s.t.
calculate
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36. Subproblem
relate
objective function
min
s.t.
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37. Transformation
(SUB 8) can be decomposed into | V X V | independent subproblems. For each link (u, v),
s.t.
calculate
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38. End Thanks for your listening
To conclude: we designed a new sleep scheduling scheme for wireless active sensor networks. The scheme exploits spatio-temporal correlation to adaptively adjust sleep cycles of nodes. The analytical and simulation results show that our scheme outperforms leach and teen with respect to energy efficiency, latency and responsive.
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