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 1. Introduction 2. System model 3. WRT construction algorithm 4. Performance evaluation 5. Conclusion

1. Introduction The energy of node is mainly drained by transmission and reception of data packets Maximizes the lifetime is referred as the Maximum Lifetime Data Aggregation (MLDA) problem

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

2. System model A set of sensor nodes {v 1, v 2,…,v n } A fixed base station Each sensor generates one data packet per unit time to the base station

Energy consumption and data aggregation The energy consumed by a sensor v i in receiving a k-bit message is The energy consumed by sensor v i to transmit a k-bit message to v j is

Definitions and notations Definition 1 – The topology graph G ( V, E ), V={v 1, v 2, …, BS} Definition 2 – A weighted topology graph G ( V, E, W )

The proposed algorithm extracts a rooted spanning tree v t is the root of the tree, V T is the set of nodes, and E T is the set of directed edges

Definition 3 – A weighted rooted tree (WRT) denoted by T( v t, V T, E T, W T ) Definition 4 – The node cost C i =in i x Rx + w i,out(v i ) In-degree of v i The node v j

C i ＝ in i × Rx ＋ w i,out(v i ), Rx ＝ 2 0×2 ＋ 6 ＝ 6 1×2 ＋ 10 ＝ 12 2×2 ＋ 9 ＝ 13 C max ＝ 13

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

3. WRT construction algorithm Starting the node BS as WRT T 0 In kth iteration the tree is T k ( BS, V k, E k, W k ) The node costs are updated accordingly until T covers all n nodes

C i : the node cost N i : the set of nodes adjacent to v i lcn i : the neighboring node of a node v i

Algorithm sequence 1. Computing lcn i, 2. If w i,lcn i > C i +Rx – C L i =w i,lcn i, C H i =C i +Rx 3. Send (lcn i,C H i,C L i ) to BS 4. BS select v i, (C H i,C L i ) ≦ (C H j,C L j ), 5. Informing v i to include lcn i into T (K+1)

Step 0 BSCDGFEHAB T 0 =[BS,BS,Ø, Ø]

Step 1 BSCDGFEHAB [len i, C H i, C L i ] BS-C:[C,2,9] BS-F:[F,2,16] BS-E:[E,2,12] [9] If w i,lcn i > C i +Rx C H i =w i,lcn i C L i =C i +Rx

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 BSCDGFEHAB [11] [4]

Step 3 BSCDGFEHAB 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] [11] [6] [9]

Step 4 BSCDGFEHAB 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] [11] [8] [9] [12] [9]

Step 5 BSCDGFEHAB 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] [11] [8] [11][9][4]

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 BSCDGFEHAB [11] [8] [11] [4] [10]

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 BSCDGFEHAB [11] [8] [11] [4] [10][12]

Step 8 BSCDGFEHAB C-B:[B,10,13] A-B:[B,6,12] H-B:[B,13,13] [11] [8] [11] [12] [11][4] [12] [6] C max ＝ 12

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

Comparison with MST MST C max ＝ 13

Comparison with SP SP C max ＝ 18

Rounds vs n for range=500 units

Rounds vs range for n=100

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

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

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