Miao Zhao, Ming Ma and Yuanyuan Yang

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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

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

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)

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

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

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

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

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

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

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

Heuristic Algorithms Maximum Compatible Pair (MCP) Algorithm

Heuristic Algorithms Maximum Compatible Pair (MCP) Algorithm 2 1 1 2 3 5 6 4 8 3 4 10 Selected Polling Point 7 9 Polling Point Sensors Matched Compatible Pair

Heuristic Algorithms Minimum Covering Spanning Tree (MCST) Algorithm

Heuristic Algorithms Minimum Covering Spanning Tree (MCST) Algorithm 2 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

Heuristic Algorithms Revenue-Based (RB) Algorithm

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

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.

Performance Evaluation

Performance Evaluation

Performance Evaluation Performance comparison among different MDG algorithms.

Performance Evaluation

Performance Evaluation

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.

Thanks for your attention!