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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

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

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  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

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