-
Research ArticleAn Adaptive Aggregation Scheduling Algorithm
Based onthe Grid Partition in Large-Scale Wireless Sensor
Networks
Xiaogang Qi,1 Lifang Liu,2 Gengzhong Zheng,3 and Mande Xie4
1School of Mathematics and Statistics, Xidian University, Xi’an
710071, China2School of Computer Science and Technology, Xidian
University, Xi’an 710071, China3School of Computer Science and
Engineering, Hanshan Normal University, Chaozhou 521041,
China4College of Computer and Information Engineering, Zhejiang
Gongshang University, Hangzhou 310018, China
Correspondence should be addressed to Lifang Liu;
[email protected]
Received 8 May 2015; Accepted 16 July 2015
Academic Editor: Jianping He
Copyright © 2015 Xiaogang Qi et al. This is an open access
article distributed under the Creative Commons Attribution
License,which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly
cited.
Data aggregation algorithm aims to reduce the redundant
information by gathering the sensed data, save energy, and prolong
thelifetime of the network. However, the data aggregation
technology will increase the network transmission delay of wireless
sensornetworks. Minimum-latency aggregation scheduling is designed
to minimize the number of scheduled time slots to perform
anaggregation. In this paper, we present an Adaptive Aggregation
Scheduling Algorithm based on the Grid Partition (AASA-GP)
inlarge-scale wireless sensor networks. By dividing the network
into grids based on the geographical information, we allocate
thechannels according to the grid coordinates. Nodes with the same
grid coordinates use the same channel and the adjacent grids usethe
different channels, so we can effectively avoid the wireless media
transmission interference, increase the parallel transfer rate,and
reduce the aggregation latency. Our extensive evaluation results
demonstrate the superiority of the AASA-GP. For
small-scalenetworks, the resultant latency is comparable with the
best practice, and it is more suitable for large-scale wireless
sensor networks.
1. Introduction
In multihop wireless sensor networks, a fundamental task isto
gather data from all sensors to a distinguished sink node [1,2]. It
is already noted that adjacent sensor nodes monitoringan
environmental feature typically register similar values[3]. This
data redundancy of the spatial correlation amongsensor observations
inspires the research of in-network dataaggregation. In general,
each intermediate node aggregates itsreceived data with its own
record according to some aggre-gation functions (e.g., taking the
maximum or minimumof them) into a single packet with fixed size.
This type ofapplication is called data aggregation, and its
communicationpattern is called convergecast [4]. The naive
aggregationapproaches which purely rely on
medium-access-controllayer mechanisms could result in latency that
is too highto be practical due to the existence of mutual
transmissioninterference [5, 6]. The goal of our study is to
minimize the
average data aggregation latency of the convergecast process,and
a synchronized aggregation scheduling is necessary,where all
transmissions proceed in synchronous time slots.Such an aggregation
scheduling is designed under threeconditions:
(1) Each node transmits atmost one packet with the fixedsize in
its allocated time slot.
(2) A node cannot transmit until all of its childrencomplete the
transmissions to itself.
(3) The assigned transmissions in the same time slotshould be
interference-free.
In this paper, the latency is measured by the number of
timeslots of the whole aggregation convergecast process, and
ourgoal aims to minimize the latency.
Hindawi Publishing CorporationInternational Journal of
Distributed Sensor NetworksVolume 2015, Article ID 283209, 9
pageshttp://dx.doi.org/10.1155/2015/283209
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2 International Journal of Distributed Sensor Networks
D1
R
𝜌
S1
D4
D2
S2 D3S3
S4
Figure 1: Transmission interference model.
0
1 2 3
4 5 6
7 8 9
10 11 12
(a)
0
1 2 3
4 5 6
7 8 9
10 11 12
(9) (1)
(3)
(8)
(6)
(2)
(7)(4)(5)
(3)(2)
(1)
(b)
0
1 3
4 6
7 9
10 12
2
5
11
8
(7) (6)(3)
(6) (2)
(5) (4)
(3)(1)(4)
(2)
(c)
Figure 2: Illustrations of single frequency channel.
2. Background
2.1. Transmission Interference Model. In wireless sensor
net-works, each node has a given transmission radius 𝑅
𝑡and
an interference radius 𝜌. The communication range and
theinterference range of a node V are illustrated by the twodisks
centered at V of radius 𝑅
𝑡and radius 𝜌, respectively
(see node 𝑆1 in Figure 1). A pair of communication edges𝑆1 → 𝐷1
and 𝑆2 → 𝐷2 are said to be interference-free; if the two line
segments (𝑆1, 𝐷2) and (𝑆2, 𝐷1) are bothlonger than 𝜌, they can be
scheduled in the same time slot,as shown in Figure 1. Otherwise,
they cannot be scheduledin the same time slot (e.g., 𝑆1 → 𝐷1 and 𝑆2
→ 𝐷4). Weassume that a nodeworks in half-duplexmode, so it can
eithersend or receive data at one time slot or it can receive
datacorrectly only if exactly one of its neighbors is
transmittingat that moment. For example, when 𝑆3 is transmitting
to𝐷3,it cannot simultaneously receive the packet from 𝑆4.
2.2. Time Scheduling on a Single Frequency Channel. Anexample
network is shown in Figure 2(a), and the dash linesamong nodes
denote the communication neighborhood rela-tionship, where node 0
is sink node. A (Δ−1)𝑅 approximationalgorithm, Shortest Data
Aggregation (SDA), is proposed byChen et al. [7], where Δ is the
maximum degree and 𝑅 is theradius of the network. SDA constructs
shortest spanning tree
(SPT) in the first phase. After that, the scheduling is
iterativelyimplemented; each round introduces a schedule of the
corre-sponding aggregation step. In round 𝑟, SDA picks sender
onlyfrom the leaf nodes according to the interference-free
princi-ple. The performance of SDA varies greatly, which dependson
the SPT’s initial provision, and this is illustrated by theexample
network in Figure 2(b) and 9 time slots are required.
GGT [8] algorithm is designed to construct the spanningtrees
rooted at the sink, and the initial spanning tree containsonly the
sink node. In each round, all nonleaf nodes of thecurrent spanning
tree are the candidates of receivers, and allleaf nodes are the
candidates of senders. As for the candidatesenders, there are two
rules to sort them in a selectionsequence: (1) sort all nodes,
based on the increasing order ofthe number of neighbors on the
tree, and (2) sort nodes withthe same order by the first rule,
based on the increasing orderof the number of neighbors out of the
tree. The schedulingresult is shown in Figure 2(c) and 7 time slots
are required.
2.3. Time Scheduling on Multiple Frequency Channels. Inthe
transmission interference model [9], there exist twoconstraints:
(1) adjacency constraint is due to the half-duplextransceiver on
each node which prevents it from simul-taneous transmission and
reception, as shown in Figure 1;𝑆3 → 𝐷3 and 𝑆4 → 𝑆3 cannot be
scheduled in the same
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International Journal of Distributed Sensor Networks 3
1 3
42
5 6
Sink
(a)
1 3
42
5 6
Sink
2, F2 3, F2
1, F2
1, F1
2, F1 3, F1
(b)
1 3
42
5 6
Sink
1, F1
2, F2
3, F3
2, F1
1, F2 3, F2
(c)
Figure 3: Illustrations of multiple frequency channels.
time slot as this constraint. (2) There is a wireless
mediatransmission interference constraint. 𝑆1 → 𝐷1 and 𝑆2 →𝐷4
cannot be scheduled in the same time slot.
Multichannelcommunication is an efficient method for eliminating
thesecond constraint by enabling concurrent transmissions
overdifferent frequencies.
In Figure 3(a), there is a network with 6 sensor nodesand the
solid lines represent the tree edges, and the dashedlines represent
the interfering links. JFTSS [10] schedules anetwork starting from
the link that has the largest numberof packets (load) to be
transmitted. When the load of theadjacent links is equal, such as
in aggregated convergecast,the most constrained link is considered
first, that is, the linkfor which the number of other links
violating the interferingand adjacency constraints when scheduled
simultaneouslyis the maximum. Figure 3(b) shows the aggregated
tree,which is scheduled by JFTSS. In JFTSS, the link (2, sink)
isfirstly assigned with frequency 𝐹1 and then the link (4, 1)is
scheduled to frequency 𝐹2 in the first slot. It is hard tohave a
distributed solution since the interference relationshipbetween all
the links must be known.
TMCP [11] partitions the network into multiple subtreesand
minimizes the intratree interference by assigning dif-ferent
channels to the nodes residing on different branchesstarting from
the top to the bottom of the tree. Figure 3(c)shows the same tree
which is scheduled by TMCP to collectthe aggregated data. Here, the
nodes on the leftmost branchare assigned with frequency 𝐹1, the
nodes on the middlebranch are assigned with frequency 𝐹2, and the
nodes onthe rightmost branch are assigned with frequency 𝐹3.
Afterthe channel assignments, time slots are assigned to the
nodesaccording to the BFS-Time Slot Assignment algorithm.
At present, many tree-based topology control and
routingalgorithms are designed to aggregate and collect the
sensingdata; these are appropriate for the small-scale, short
commu-nication radius networks [12]. Multichannel communicationis
an efficient method to eliminate interference by enablingconcurrent
transmissions over different frequencies. But itis very difficult
to assign channels to the tree networkstructure. Motivated by grid
partition induction [13], wepropose AASA-GP to schedule the
aggregation process. In
our algorithm, we firstly divide the network into grids basedon
the geography information and then allocate channels tothe links
based on grid coordinates. Nodes with the same gridcoordinate using
the same channel, adjacent grids using theother channels, which can
effectively avoid the transmissioninterference thereby reduce the
aggregate delay. To the bestof our knowledge, it is the first time
to use grid-based routingtopology to solve aggregation latency.
The following lists our key findings and contributions:
(1) Use the tree-based topology to route and solve aggre-gation
latency.
(2) Allocate channel based on grid coordinates.(3) Algorithm is
appropriate for large-scale wireless sen-
sor networks with the large communication range.
3. Protocol Description
3.1. Basic Idea. By dividing the network into grids andassigning
different channels to adjacent grids, the wirelesstransmission
medium interference constraint is avoided, andthe data from other
source nodes in the same grid can becollected and aggregated on the
selected cluster head and thenproceed to the sink.
3.2. Meshing. In our scheme, we randomly select 𝑁 wirelesssensor
nodes to construct wireless sensor networks in 𝑆 ×𝑆 square region.
Sink (deployed at the right side of thenetwork) broadcasts grid
side length 𝑙 to the wireless sensornetworks, as shown in Figure 4;
all nodes receive the messageaccording to the location information
and the grid side lengthto calculate its grid coordinates:
𝐺𝑥 = ⌊𝑥
𝑙⌋ ,
𝐺𝑦 = ⌊𝑦
𝑙⌋ ,
(1)
where (𝐺𝑥, 𝐺𝑦) indicates the grid coordinates, (𝑥, 𝑦)
indicatesthe location coordinates of the nodes, and ⌊𝑥/𝑙⌋
indicatesthe largest integer less than 𝑥/𝑙. The network is divided
into
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4 International Journal of Distributed Sensor Networks
(0, 0) (1, 0) (2, 0) (3, 0)
(0, 1) (1, 1) (2, 1)
(0, 2) (1, 2)
(0, 3)Sink
lGy
Gx
K
...
4
3
2
1
0 1 2 3 4 · · ·
· · ·
· · ·
· · ·
· · · · · ·
K
S
Figure 4: Network mesh.
𝑚 = 𝑥2/𝑙2 grids, and the average number of nodes in each
grid is𝑁 ∗ 𝑙2/𝑥2.Each node broadcasts its grid coordinates, and
the nodes
with the same grid coordinates will form a cluster, in whichthe
highest-energy node serves as the cluster head andreceives the data
from other members in this grid and thenaggregates the data into a
fixed-size packet.
Due to the limitations of half-duplex mode, nodes withthe same
grid coordinates cannot communicate with the clus-ter head at the
same time, but nodes with the different gridcoordinates can
communicate through multiple channels toavoid wireless media
transmission interference and increasethe parallel
transmission.
3.3. Channel Assignment. We assign different channels toadjacent
grids, and the scheme of the channel assignmentof the network is
shown in Figure 5, in which ch1 indicateschannel 1. According to
this allocation, we assign 9 differentchannels to the entire
network so that nodes in the differentgrid can transmit data at the
same time. For example, inFigure 5, red grid is allocated channel 9
and its channelnumber is different from the adjacent grids. In this
way, whennodes in the red grid communicate with cluster head, it
isinterference-free with the adjacent 24 grids, in which reddashed
line passes through. The total number of grids innetwork is 𝑥 ∗ 𝑥/𝑙
∗ 𝑙, and the number of channels is 𝐹, sothe computational
complexity of the channel assignment is𝑂(𝑥2∗ 𝐹/𝑙2).
At the same time, we can adjust the size of the grid(grid side
length 𝑙) in order to guarantee nodes in red gridand in green grid
to transmit data in parallel, so that nodesthat belong to different
grids can transmit data withoutinterference. After the in-grid data
collection, cluster headcan forward the sensed data across the
other grids to sink.
ch1 ch2 ch3
ch4 ch5 ch6
ch7 ch8 ch9
ch1 ch2 ch3
ch4 ch5 ch6
ch7 ch8 ch9
ch1 ch2 ch3
ch4 ch5 ch6
ch7 ch8 ch9
ch1 ch2 ch3
ch4 ch5 ch6
ch7 ch8 ch9
ch1
ch1
Sink
ch9 ch9
Gy
K
... ...
...6
5
4
3
2
1
0 1 2 3 4 5 6 · · ·
· · ·
· · ·
· · ·
K Gx
Figure 5: Illustrations of channel assignment.
3.4. Routing between Grids. Routing across the grids
mainlyinvolves the communication between cluster heads, and
ourrouting scheme can be analyzed by following two casesaccording
to the location of the sink.
In Figure 6, the example network is divided into a 8 ∗ 8grid.
When sink locates at the center of the network, theroute scheme of
this grid is shown as the directed arrows inFigure 6(a). The number
of the same channels indicates thenumber of the time slots; the
same number indicates data inthe two grids can be transmitted in
parallel mode.
When sink is located in the center of the network, theroute
structure between grids is shown in Figure 6(b).
3.5. The Connectivity of the Network. Because of the limita-tion
of communication capabilities of wireless sensor nodes,we assume
that communication radius is 𝑅
𝑡; the grid side
length is 𝐿. To obtain a better network connectivity,
thecandidate cluster head must lie in the circular region
whosecenter is the grid center and radius is 𝑅 as shown in Figure
7.
3.5.1. Connectivity within the Grid. As shown in Figure 7,
wesuppose node A is the cluster head; if any node in the gridcould
communicate with A, we should make the 𝐿
1satisfy
𝐿1≤ 𝑅𝑡; in other words, the following inequation should be
satisfied. Consider
𝑅 +√2
2𝐿 ≤ 𝑅
𝑡. (2)
3.5.2. Connectivity between the Grids. In order to guaranteethe
adjacent cluster heads can communicate with each other,the maximum
distance between two cluster heads should beless than the node
communication radius 𝑅
𝑡. In Figure 8(a),
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International Journal of Distributed Sensor Networks 5
Sink
(1) (2) (3) (4)
(5)
(6)
(1) (2) (3)
(1) (2) (3)
(1) (2) (3)
(1) (2) (3)
(1) (2) (3)
(1) (2) (3)
(1) (2) (3)
(4)
(4)
(4)(7)
(8)(9)
(a) Sink is located at the center
Sink
(2)
(1)
(1)
(2)
(3)
(2)
(2)
(2)
(2)
(2)
(1)
(1)
(1)
(1)
(1)
(4)
(3)
(3)
(3)
(3)
(3)
(5)
(4)
(4)
(4)
(4)
(6)
(5)
(5)
(5)
(7)
(6)
(6)
(7)
(8)
(9)
(10)
(b) Sink is located at the corner
Figure 6: Illustrations of data forwarding between grids.
B
A
L
R
L1
Figure 7: Cluster head selection area.
𝐿2≤ 𝑅𝑡(sink is in the center) or 𝐿
3≤ 𝑅𝑡(sink is in the
corner). That is,
2𝑅 + 𝐿 ≤ 𝑅𝑡, (3)
2𝑅 + √2𝐿 ≤ 𝑅𝑡. (4)
However, as shown in Figure 8(b), node G and node Huse the same
channel 1; if they want to transmit the data inparallel mode, the
grid side length 𝐿 should be satisfied as inthe following
inequality:
2𝑅 ≤ 𝑅𝑡. (5)
When 𝐿 and 𝑅 are required to satisfy (3) or (4), they
mustsatisfy (2).
In summary, when sink lies at the center or edge of thenetwork,
if the network connectivity is to be ensured, 𝑅, 𝐿,and 𝑅
𝑡should satisfy the following constraints:
2𝑅 + 𝐿 ≤ 𝑅𝑡,
2𝐿 ≥ 𝑅𝑡.
(6)
When sink is in the corner of the network, 𝑅, 𝐿, and 𝑅𝑡
should satisfy the following constraints:
2𝑅 + √2𝐿 ≤ 𝑅𝑡,
2𝐿 ≥ 𝑅𝑡.
(7)
In (6) and (7), node’s communication radius 𝑅𝑡is a
constant, 𝐿 and 𝑅 are adjustable, and the greater the 𝐿 is,
thesmaller the 𝑅 is. Thus the number of cluster heads to chooseis
less; there may even be a grid that could not elect a clusterhead,
so it should make 𝑅 as large as possible, so that therewill be
plenty of nodes you can choose to be cluster headand cluster head’s
energy consumption can be balanced. Forexample, when𝑅
𝑡= 30, (6) can take𝐿 = 15,𝑅 = 7.5, as shown
in Figure 8(c), and the nodes are located in the inscribedcircle
of this grid. When sink is located at the center or edgeof the
network, we make 𝐿 = 0.5𝑅
𝑡, 𝑅 = 0.25𝑅
𝑡. When sink
is located in the corner of the network, we make 𝐿 = 0.45𝑅𝑡,
𝑅 = 0.2𝑅𝑡.
3.6. Network Topology of the Algorithm. According to theabove
algorithm description, we simulate a network in whichthe edge
length is 200, the number of nodes is 800, thecommunication radius
of node is 30, topology is shown inFigure 9, the red dots in each
grid are cluster heads, the bluedot is sink, and the other dots are
ordinary sensor nodes.
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6 International Journal of Distributed Sensor Networks
C D
E
F
L
L2
L3
R
(a)
G Hch1 ch2 ch3 ch1
L4
(b)
30
15
(c)
Figure 8: Connectivity between the grids.
Figure 9: Network topology.
4. Simulation and Performance Analysis
4.1. Experiment Setup. We use C++ to simulate the
followingalgorithms. Multichannel algorithms are JFTSS-channel: 2
(2channels of JFTSS algorithm), JFTSS-channel: 16 (16 chan-nels of
JFTSS algorithm), TMCP-channel: 2 (2 channels ofTMCP algorithm),
TMCP-channel: 16 (16 channels of TMCPalgorithm), our algorithm (9
channels). Single-channel algo-rithms are SDA and GGT. The routing
architecture of our
algorithm is based on grid, suitable for large-scale and
largecommunication radius (𝑅
𝑡= 30, 40, 50) wireless sensor
networks. The topology structure of other algorithms ismainly
based on the tree, and the node’s communicationradius of these
algorithms is small (𝑅
𝑡= 10, 20, 30). Due
to differences in the application background, when ouralgorithm
compared with other algorithms, we take 𝑅
𝑡= 30.
We randomly arrange 𝑁 sensor nodes in a square areawith the side
length 𝑆; the average node density is𝑁/𝑆2. For arandomly generated
network topology, we use average nodedegree Φ to indicate the
strength of the interference. Here,The greater the average degree
of nodes is, the stronger theinterference is.
4.2. Comparison with Other Algorithms. In our simulation,we set
the average node density as 𝑁/𝑆2 = 0.02. For 𝑆 =50, 100, 150, 200,
250, we set 𝑁 = 50, 200, 450, 800, 1250,respectively. When the
communication radius is set as 𝑅
𝑡=
10, 20, 30, 40, 50, the changes ofΦ are shown in Figure 10.With
the increase of node communication radius, the
average degree of nodes also increases, so the
networktransmission interference also increases; this results in
theincrease of the aggregation delay.
Figure 11 shows the number of time slots neededwhen thenumber of
nodes 𝑁 varies from 50 to 1250 (i.e., 𝑆 from 50
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International Journal of Distributed Sensor Networks 7
1020
3040
50
50100
150200
250
0
50
100
150
Transmis
sion rang
e
Square edge length
Aver
age d
egre
e
Figure 10: Average degree of the network.
to 250), with the 𝑅𝑡value of 10, 20, and 30. In Figure
11(a),
sink is located in the center of the network, the grid
sidelength of AASA-GP is 𝐿 = 0.5, 𝑅
𝑡= 15, and the average
number of nodes in each grid is about 5. Due to the
randomarrangement, the distribution of nodes in each grid is
notuniform which lead to the AASA-GP aggregate delay in theactual
simulation process that is higher than the theoreticalanalysis. As
shown in Figure 11(a), AASA-GP reduces theaggregation delay by 20
percent compared to that by TMCP-16 channels and 40 percent
compared to GGT.
In Figure 11(b), sink is located at the corner of thenetwork;
the grid side length of AASA-GP is 𝐿 = 0.45,𝑅𝑡= 13.5. The aggregate
delay of each algorithm has
increased to some extent; this is due to the increase of
thedistance between sink and the other nodes. AASA-GP is
stillsignificantly better than other algorithms; this reveals
thatAASA-GP is applicable to the different topologies and has
abetter performance in a wide range of applications.
4.3. Simulation of Large-Scale Wireless Sensor Networks.
Wesimulate large-scale wireless sensor networks. 𝑆 varies from100
to 1000, with 𝑅
𝑡value of 30, 40, and 50, and the average
node density is set constant as 0.02. In Figure 12(a), sinkis
located at the center of the network; when the networksize
increases, the aggregation delay of AASA-GP increases.According to
the three curves in Figure 12(a), we find that thetransmission
interference increases when the node commu-nication radius
increases; the transmission interference alsoincreases. But when
the network size increases to a certainextent, the aggregate delay
of 𝑅
𝑡= 40 and 𝑅
𝑡= 50 is
less than the aggregate delay of 𝑅𝑡= 30. This is due to the
fact that the larger the node communication radius is,
thegreater the grid edge length is, which leads to the increasingof
aggregate delay within the grid. However, at the same time,the
number of grids decreases; the aggregate delay between
JFTSS, 2 channelsJFTSS, 16 channelsTMCP, 2 channelsTMCP, 16
channels
SDAGGTAASA-GP
0102030405060708090
100
Agg
rega
tion
time s
lots
0 100 150 200 250 30050Square edge length
(a) Sink is located at the center
JFTSS, 2 channelsJFTSS, 16 channelsTMCP, 2 channelsTMCP, 16
channels
SDAGGTAASA-GP
0102030405060708090
100
Agg
rega
tion
time s
lots
50 100 150 200 250 3000Square edge length
(b) Sink is located at the corner
Figure 11: Performance comparison with fixed node density
(𝑅𝑡=
30, Φ ≈ 40).
grids reduces. When the network size increases, this decreaseis
more significant.
From Figure 12(b), we conclude that the variation trendof the
aggregation network delay is similar to that shown inFigure 12(a),
which indicates that AASA-GP can be appliedto different network
topologies.
5. Conclusions
This paper presents an adaptive aggregation schedulingalgorithm
based on the grid partition in large-scale wirelesssensor networks
(AASA-GP). By dividing the network intogrids based on geographical
information, when we assignthe different channels to the adjacent
grids, the wirelesstransmission interference can be avoided. By
selecting thecluster head in each grid, the network load can be
effectivelybalanced. Simulation results show that aggregation
delayby AASA-GP is significantly less than that by the
otheralgorithms. In wireless sensor networks, when the networkscale
and the node’s communication radius are larger, theadvantages of
AASA-GP are more obvious.
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8 International Journal of Distributed Sensor Networks
200 300 400 500 600 700 800 900 1000100Square edge length
10
20
30
40
50
60
70
80
Agg
rega
tion
time s
lots
AASA-GP (Rt = 30)AASA-GP (Rt = 40)AASA-GP (Rt = 50)
(a) Sink is located at the center
200 300 400 500 600 700 800 900 1000100Square edge length
10
20
30
40
50
60
70
80
90
100
110
120
Agg
rega
tion
time s
lots
AASA-GP (Rt = 30)AASA-GP (Rt = 40)AASA-GP (Rt = 50)
(b) Sink is located at the corner
Figure 12: Performance for large-scale wireless sensor
networks.
Conflict of Interests
The authors declare that there is no conflict of
interestsregarding the publication of this paper.
Acknowledgments
This work is partially supported by the Project of
NationalNatural Science Foundation of China under Grant
nos.71271165, 61373174 and 61572435, the Key Project of
NaturalScience Foundation of Shaanxi Province under Grant
nos.2015JZ002 and 2015JM6311, the Project of the Guangxi
KeyLaboratory of Trusted Software under Grant no. kx201416,
the Project of the High Level Talents in Colleges of Guang-dong
Province (Guangdong Finance Education (2013) no.246), and the
Project of the Natural Science Foundation ofGuangdong Province
under Grant no. 2014A030307014.
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International Journal of Distributed Sensor Networks 9
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