1. Miao Zhao, Ming Ma and Yuanyuan Yang
Mobile Data Gathering with Space-Division Multiple Access in Wireless Sensor Networks
Miao Zhao, Ming Ma and Yuanyuan Yang
Department of Electrical and Computer Engineering, State University of New York
IEEE INFOCOM 2008

2. Outline Introduction SDMA Technique MDG-SDMA Problem
Heuristic Algorithms
Performance Evaluation
Conclusions

3. Introduction
Recent years have witnessed a surge of interest in efficient data gathering schemes in WSNs.
Routing protocol
Distributed data compression
Efficient transmission schedule
Hierarchical infrastructure
Mobile data gathering
Data MULEs (Data collector)

4. Introduction Mobile data gathering (MDG)
Radically solves the non-uniformity of energy consumption among sensors.
The mobile data collector works well not only in a fully connected network, but also in a disconnected network.
Sink
SenCar (data mule)
Sensors

5. Introduction
The total time of a data gathering tour mainly consists of
Data uploading time
Moving time
Sink
SenCar (data mule)
Sensors

6. Introduction
This paper improve the performance of data gathering in WSNs by considering two critical factors:
The mobility of Data MULE .
Space-Division Multiple Access (SDMA) technique.
Sink
SenCar (data mule)
Sensors

7. SDMA Technique
The SenCar is the receiver equipped with multiple antenna
Sensors are the senders each having a single antenna to upload sensing data to the SenCar.
Assume that the SenCar is the receiver
equipped with two antennas
Sensors
Polling Point
Compatible
Matched
Compatible Pair
Maximum Matching Problem

8. MDG-SDMA Problem Maximum Matching Problem
Traveling Salesman Problem (TSP)
Polling Point
Matched
Compatible Pair

9. MDG-SDMA Problem Maximum Matching Problem
Traveling Salesman Problem (TSP) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
Polling Point
Compatible
Matched
Compatible Pair 28 29 30 31 32 33 34

10. Heuristic Algorithms Maximum Compatible Pair (MCP) Algorithm
Minimum Covering Spanning Tree (MCST) Algorithm
Revenue-Based (RB) Algorithm

11. Heuristic Algorithms
Maximum Compatible Pair (MCP) Algorithm

12. Heuristic Algorithms Maximum Compatible Pair (MCP) Algorithm 2 1 1 2 35 6 4 8 3 4 10
Selected
Polling Point 7 9
Polling Point
Sensors
Matched
Compatible Pair

13. Heuristic Algorithms
Minimum Covering Spanning Tree (MCST) Algorithm

14. Heuristic Algorithms Minimum Covering Spanning Tree (MCST) Algorithm2 1
τ1(P1)=0/4=0
τ1(P2)= d/4
τ1(P3)= d/4
τ1(P4)=√2d/5. 1 2 3 5 6 4
τ2(P2)= d/2
τ2(P3)= d/3
τ2(P4)=√2d/5. 8 3 4 10 7 9
τ3(P2)= ∞
τ3(P3)= d
τ : the average cost of a polling point
d : the distance between two adjacent polling points

15. Heuristic Algorithms
Revenue-Based (RB) Algorithm

16. Heuristic Algorithms Revenue-Based (RB) Algorithm
R(i)= −αω(i)+βτ(i) ,whereαandβare positive coefficients,
ω(i) is the maximum matching among the uncovered sensors in the neighbor set of Pi
τ(i) is the average cost of Pi as defined in the MCST algorithm.
ω1(P1)=2
ω1(P2)=2
ω1(P3)=1
ω1(P4)=2
τ1(P1)=0
τ1(P2)= d/4
τ1(P3)= d/4
τ1(P4)=√2d/5. 2 1 1 2
ω2(P2)=1
ω2(P3)=1
ω2(P4)=2
τ2(P2)= d/2
τ2(P3)= d/
τ2(P4)=√2d/5. 3 5 6 4
α=1 ,β=1 8 3 4 10 7 9

17. Performance Evaluation
There are a total of 20 sensors scattered over the 60m×60m square area
25 polling points are located at the intersections of grids and each one is 15m apart from its adjacent neighbors in horizontal and vertical directions.
The authors set the radius of the coverage area of each polling point to 30m, which is also the transmission range of each sensor.

18. Performance Evaluation

19. Performance Evaluation

20. Performance Evaluation
Performance comparison among different MDG algorithms.

21. Performance Evaluation

22. Performance Evaluation

23. Conclusions
The authors have introduced a joint design of mobility and SDMA technique to data gathering in WSNs.
The authors formulated MDG-SDMA Problem.
The authors proposed three heuristic algorithms to provide practically good solutions to the problem.

24. Thanks for your attention!