1 / 31

Distributed Data Gathering Scheduling in Multi-hop Wireless Sensor Networks for Improved Lifetime

Distributed Data Gathering Scheduling in Multi-hop Wireless Sensor Networks for Improved Lifetime. Subhasis Bhattacharjee and Nabanita Das. International Conference on Computing: Theory and Applications (ICCTA'07). Outline. Introduction System model WRT construction algorithm

ulla
Download Presentation

Distributed Data Gathering Scheduling in Multi-hop Wireless Sensor Networks for Improved Lifetime

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Distributed Data Gathering Scheduling in Multi-hop Wireless Sensor Networks for Improved Lifetime Subhasis Bhattacharjee and Nabanita Das International Conference on Computing: Theory and Applications (ICCTA'07)

  2. Outline • Introduction • System model • WRT construction algorithm • Performance evaluation • Conclusion

  3. 1. Introduction • The energy of node is mainly drained bytransmission and reception of data packets • Maximizes the lifetime is referred as the Maximum LifetimeData Aggregation (MLDA) problem

  4. Constructing a rooted spanning tree based on adjacent neighborhood to enhance the lifetime • Comparing with Minimum Spanning Tree (MST) and Shortest Path (SP)

  5. 2. System model • A set of sensor nodes {v1, v2,…,vn} • A fixed base station • Each sensor generates one data packet per unit time to the base station

  6. Energy consumption and data aggregation • The energy consumed by a sensor vi in receiving a k-bit message is • The energy consumed by sensor vi to transmit a k-bit message to vj is

  7. Definitions and notations • Definition 1 • The topology graph G ( V, E ), V={v1, v2, …, BS} • Definition 2 • A weighted topology graph G ( V, E, W )

  8. The proposed algorithm extracts a rooted spanning tree • vt is the root of the tree, VT is the set of nodes, and ET is the set of directed edges

  9. Definition 3 • A weighted rooted tree (WRT) denoted by T( vt, VT, ET, WT ) • Definition 4 • The node cost Ci=inix Rx + wi,out(vi) In-degree of vi The node vj

  10. Ci＝ini× Rx ＋wi,out(vi) , Rx＝2 2×2＋9＝13 1×2＋10＝12 0×2＋6＝6 Cmax＝13

  11. Problem statement • Minimizes the maximum node cost Cmax • Given a weighted topology graph G( V, E, W ) to find a weighted rooted spanning tree T( BS, V, E’, W’ )

  12. 3. WRT construction algorithm • Starting the node BS as WRT T0 • In kth iteration the tree is Tk( BS, Vk, Ek, Wk ) • The node costs are updated accordingly until T covers all n nodes

  13. Ci: the node cost • Ni: the set of nodes adjacent to vi • lcni: the neighboring node of a node vi

  14. Algorithm sequence • 1. Computing lcni , • 2. If wi,lcni > Ci+Rx • CLi=wi,lcni, CHi=Ci+Rx • 3. Send (lcni,CHi,CLi) to BS • 4. BS select vi, (CHi,CLi) ≦(CHj,CLj), • 5. Informing vito include lcniinto T(K+1)

  15. BS C D G F E H A B Step 0 T0=[BS,BS,Ø, Ø] 10 9 12 6 4 13 16 13 9 9 10 12 3 4

  16. BS C D G F E H A B [leni, CHi, CLi] BS-C:[C,2,9] BS-F:[F,2,16] BS-E:[E,2,12] If wi,lcni > Ci+Rx CHi=wi,lcni CLi=Ci+Rx Step 1 [9] 10 9 12 6 4 13 16 13 9 9 10 12 3 4

  17. BS A B C D G F E H BS-F:[F,2,16] BS-E:[E,2,12] C-B:[B,10,11] C-D:[D,4,11] C-F:[F,13,11] Step 2 [11] 10 9 12 6 4 13 16 [4] 13 9 9 10 12 3 4

  18. BS A C D G F E H B BS-F:[F,2,16] BS-E:[E,2,12] C-B:[B,10,13] C-F:[F,13,13] D-G:[G,6,9] D-H:[H,6,9] Step 3 [11] 10 9 12 6 4 13 16 [6] 13 9 9 10 12 3 4 [9]

  19. BS C D G F E H A B BS-F:[F,2,16] BS-E:[E,2,12] C-B:[B,10,13] C-F:[F,13,13] G-F:[F,4,11] D-H:[H,8,9] G-H:[H,3,11] Step 4 [11] 10 9 12 6 4 13 16 [8] 13 9 9 10 12 3 4 [9] [9] [12]

  20. BS C D G F E H A B BS-F:[F,2,16] BS-E:[E,2,12] C-B:[B,10,13] C-F:[F,13,13] G-F:[F,4,11] H-A:[A,10,11] H-B:[B,11,13] Step 5 [11] 10 9 12 6 4 13 16 [8] 13 9 9 10 12 3 4 [9] [11] [4]

  21. BS C D G F E H A B BS-F:[F,2,16] BS-E:[E,2,12] C-B:[B,10,13] F-E:[E,6,12] H-A:[A,10,11] H-B:[B,11,13] Step 6 [11] 10 9 12 6 4 13 16 [10] [8] 13 9 9 10 12 3 4 [11] [11] [4]

  22. BS C D G F E H A B BS-E:[E,2,12] C-B:[B,10,13] F-E:[E,6,12] A-B:[A,6,12] H-B:[B,13,13] Step 7 [11] 10 9 12 6 4 13 16 [10] [8] [12] 13 9 9 10 12 3 4 [11] [11] [4]

  23. BS A C D G F E H B C-B:[B,10,13] A-B:[B,6,12] H-B:[B,13,13] Step 8 [6] [11] 10 9 12 6 4 13 16 [8] [12] [12] 13 9 9 10 12 Cmax＝12 3 4 [11] [11] [4]

  24. 4. Performance evaluation • 50≦n≦200 nodes • 200m×200m 2-D region • Transmission range from 40m to 100m • Energy values:

  25. Comparison with MST Cmax＝13 MST

  26. Comparison with SP Cmax＝18 SP

  27. Rounds vs n for range=500 units

  28. Rounds vs range for n=100

  29. 5. Conclusion • For a random distribution of n sensor nodes the algorithm takes O(n) steps • No knowledge of global topology is required • Improving the lifetime with Minimum Spanning Tree (MST) and Shortest Path (SP) routs

  30. Comparison with PEGASIS • 100 nodes distributed over at 50m×50m 2-D region • The range of each node is 110m

  31. Comparison when 10％, 20％, 50％and 100％of nodes die out

More Related