mobile data gathering with space-division multiple access in wireless sensor networks miao zhao,...
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Mobile Data Gathering with Space-Division Multiple Access in Wireless Sensor Networks
Miao Zhao, Ming Ma and Yuanyuan YangDepartment of Electrical and Computer Engineering, State
University of New York
IEEE INFOCOM 2008
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Outline
Introduction
SDMA Technique
MDG-SDMA Problem
Heuristic Algorithms
Performance Evaluation
Conclusions
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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)
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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.
SenCar (data mule)
Sensors
Sink
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Introduction
The total time of a data gathering tour mainly consists of Data uploading time Moving time
SenCar (data mule)
Sensors
Sink
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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.
SenCar (data mule)
Sensors
Sink
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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.
Maximum Matching Problem
Compatible
Matched Compatible Pair
Polling Point
Sensors
Assume that the SenCar is the receiver equipped with two antennas
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MDG-SDMA Problem
Maximum Matching Problem Traveling Salesman Problem (TSP)
0
Matched Compatible Pair
Polling Point
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MDG-SDMA Problem
Maximum Matching Problem Traveling Salesman Problem (TSP)
0 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
28 29 30 31 32 33 34 Matched Compatible Pair
Polling Point
Compatible
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Heuristic Algorithms
Maximum Compatible Pair (MCP) Algorithm
Minimum Covering Spanning Tree (MCST) Algorithm
Revenue-Based (RB) Algorithm
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Heuristic Algorithms
Maximum Compatible Pair (MCP) Algorithm
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Heuristic Algorithms
Maximum Compatible Pair (MCP) Algorithm
1 2
43
12
3
45 6
7
8
9
10
Matched Compatible Pair
Polling Point
Selected Polling Point
Sensors
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Heuristic Algorithms
Minimum Covering Spanning Tree (MCST) Algorithm
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Heuristic Algorithms
Minimum Covering Spanning Tree (MCST) Algorithm
1 2
43
12
3
45 6
7
8
9
10
τ : the average cost of a polling point d : the distance between two adjacent polling points
τ1(P1)=0/4=0τ1(P2)= d/4τ1(P3)= d/4τ1(P4)=√2d/5.
τ2(P2)= d/2τ2(P3)= d/3τ2(P4)=√2d/5.
τ3(P2)= ∞τ3(P3)= d
d
dd2
d
i
ii n
d)(P
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Heuristic Algorithms
Revenue-Based (RB) Algorithm
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Heuristic Algorithms
Revenue-Based (RB) Algorithm
1 2
43
12
3
45 6
7
8
9
10
R(i)= −αω(i)+βτ(i) ,whereαandβare positive coefficients,
ω(i) is the maximum matching among the uncovered sensors in the neighbor set of Piτ(i) is the average cost of Pi as defined in the MCST algorithm.
ω1(P1)=2ω1(P2)=2ω1(P3)=1ω1(P4)=2
τ1(P1)=0τ1(P2)= d/4τ1(P3)= d/4τ1(P4)=√2d/5.
ω2(P2)=1ω2(P3)=1ω2(P4)=2
τ2(P2)= d/2τ2(P3)= d/τ2(P4)=√2d/5.
α=1 ,β=1
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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.
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Performance Evaluation
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Performance Evaluation
5
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Performance Evaluation
Performance comparison among different MDG algorithms.
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Performance Evaluation
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Performance Evaluation
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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.
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Thanks for your attention!