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An Energy and Delay Efficient Data - Centric Scheduling Algorithm for Wireless Sensor Networks

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. Outline. Introduction Related Work Motivation Problem Description Problem Description

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An Energy and Delay Efficient Data - Centric Scheduling Algorithm for Wireless Sensor Networks

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

  2. Outline • Introduction • Related Work • Motivation • Problem Description • Problem Description • Notation • Problem Formulation • Lagrangean Relaxation • Subproblem and Solution Approach

  3. Related work • SMAC • Fixed duty cycle, sleep latency • TMAC • Sleep latency • DMAC • Data aggregation • Max-Min Fair Collision-Free Scheduling • Data aggregation • ELECTION • Not Optimal

  4. Motivation • To propose an algorithm that achieves energy efficiency, data aggregation and ensures low latency.

  5. Problem description

  6. Problem Description • Given • 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

  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

  8. Notation – Given Parameter

  9. Notation – Given Parameter(cont.)

  10. Notation – Given Parameter(cont.)

  11. Notation – Decision Variables

  12. The relationship between nu mu nv and mv

  13. if and the communication between node u and node v exist interference The relationship between nu mu nv and mv if

  14. Routing Constraint Tree Constraint Problem Formulation Min subject to:

  15. Minimum and Maximum End to End delay Constraint Number of neighbors Constraint Problem Formulation(cont.)

  16. Problem Formulation(cont.)

  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 :

  18. Approximated Function We take natural logarithm on both sides in order to make this function solvable

  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).

  20. Relaxation(cont.) Min

  21. Relaxation(cont.)

  22. Relaxation(cont.) subject to:

  23. Relaxation(cont.)

  24. Subproblem relate objective function min s.t. independent shortest path problems (SUB 1) can be further decomposed into

  25. Subproblem relate objective function min s.t.

  26. Subproblem relate objective function min s.t.

  27. calculate Transformation After transforming, we can decompose (SUB 3) into | V | independent subproblems. For each node u, s.t.

  28. Subproblem relate objective function min s.t.

  29. calculate Transformation After transforming, we can decompose (SUB 4) into | V | independent subproblems. For each node u, s.t.

  30. Subproblem relate objective function min s.t.

  31. Subproblem relate objective function min s.t.

  32. Subproblem relate objective function min s.t.

  33. calculate Transformation After transforming, we can decompose (SUB 6) into | V X V | independent subproblems. For each link (u, v), s.t.

  34. Subproblem relate objective function min s.t.

  35. calculate Transformation (SUB 7) can be decomposed into | V X V | independent subproblems. For each link (u, v), s.t.

  36. Subproblem relate objective function min s.t.

  37. calculate Transformation (SUB 8) can be decomposed into | V X V | independent subproblems. For each link (u, v), s.t.

  38. End • Thanks for your listening

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