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Research ArticleSensor Networks Hierarchical Optimization Model
for SecurityMonitoring in High-Speed Railway Transport Hub
Zhengyu Xie1,2 and Yong Qin2
1School of Traffic and Transportation, Beijing Jiaotong
University, Beijing 100044, China2State Key Laboratory of Rail
Traffic Control and Safety, Beijing Jiaotong University, Beijing
100044, China
Correspondence should be addressed to Yong Qin;
[email protected]
Received 21 November 2014; Revised 31 March 2015; Accepted 7
April 2015
Academic Editor: Fei Yu
Copyright © 2015 Z. Xie and Y. Qin. 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.
We consider the sensor networks hierarchical optimization
problem in high-speed railway transport hub (HRTH). The
sensornetworks are optimized from three hierarchies which are key
area sensors optimization, passenger line sensors optimization,
andwhole area sensors optimization. Case study on a specific HRTH
in China showed that the hierarchical optimization method
iseffective to optimize the sensor networks for security monitoring
in HRTH.
1. Introduction
With the rapid development of high-speed railway in China,many
modern HRTHs have been built to match the develop-ing demands.
HRTHs become the crossing and interface ofmultitransportation which
include high-speed railway, civilaviation, highway, waterway, urban
rail transit, public trans-port, motor vehicle, and taxi. As a
vital node of passengertransport net, HRTH is an important
distribution place ofmassive passenger flow. With the increase of
high-speedrailway operation mileage, the distribution quantity of
pas-sengers will be sustained to increase sharply, which leadsHRTHs
to confront severe challenges in passenger flow secu-rity
monitoring.
At present, the video surveillance system is the mainsecurity
monitoring approach used in HRTH. The managerscan detect the
congestion of passenger flow, abnormal behav-iors of passengers,
abandoned objects, and so forth by usingsurveillance systems. The
basic workflow of system includesthe following: (i) data
acquisition: distribute surveillancesensors and develop a sensor
network; (ii) data transmission:choose suitable approaches to
transmit data acquired fromthe sensor networks; (iii) data
processing: utilize efficientimage processing method to process the
data acquired fromthe sensor networks and obtain processing result
based onthe demands of security monitoring; (iv) data
dissemination:
select various channels to disseminate the security moni-toring
information. Currently, the studies related to videosurveillance
system in HRTHmainly focused on (ii) and (iii)to improve detection
accuracy and speed; specific study on (i)is scarce. As a foundation
of other parts, the sensor networkshave important influences on
other parts. So it is necessary forHRTH security monitoring to
optimize the sensor networks.
The rest of this paper is organized as follows:The
relevantliterature is reviewed in the next section.The sensor
networkshierarchical optimization problem is described in Section
3and Section 4 proposes a sensor networks hierarchical
opti-mization model. A case study is reported in Section 5
andfinally Section 6 covers the conclusion.
2. Literature Review
The sensor networks optimization problem for security
mon-itoring in HRTH belongs to the art gallery problem (AGP)which
was first proposed in 1973 in a conversation betweenKlee and
Chvatal [1]. Based on the conversation, Chvatalproofed [𝑛/3]
cameras are always sufficient and sometimesnecessary. This
conclusion is called the Art Gallery The-orem, or Watchman Theorem
[2]. Fisk used triangulationtechniques and staining methods and got
the conclusion“any simple polygon after triangulation, the
correspondingdiagram can 3- stain,” and the same type of colored
dots
Hindawi Publishing CorporationJournal of SensorsVolume 2015,
Article ID 951242, 9 pageshttp://dx.doi.org/10.1155/2015/951242
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2 Journal of Sensors
Table 1: Data acquisition demands of security monitoring in
HRTH.
Level Description Concern Data acquisition demands
First level Key area monitoringFocus on the security ofkey,
important, andsensitive areas
The data of key areas in HRTHmust becontinuously acquired and
can meet theanomaly detection of key areas
Second level Passenger linemonitoring
Focus on the security ofpassenger input and outputlines
The data of entire passenger line in HRTHmust be continuously
acquired and can meetthe forecast demands of post node inpassenger
line
Third level Complete coveragemonitoringFocus on the security
ofwhole HRTH
The data of whole HRTH can beinconsecutively and optionally
acquired andmust ensure all function areas arecompletely
covered
can cover the entire simple polygon [3]. Avis, Toussaint,
andChazelle gave different algorithms for the simple
polygontriangulation. For any simple polygon with given point,
wecan determine the location of monitors in a simple polygonwithin
time, making any point in this simple polygon ableto see at least
one monitor [4, 5]. Lee and Lin proved thatthe algorithm of solving
any simple polygon which requiredminimum number of guards is
NP-hard [6].
After Art Gallery Theorem is proved, more and morequestions of
AGP are proposed, including the following: themonitor can bemoved
at the edge, the monitor can bemovedat the diagonal, at least two
monitors are required that canbe guarded by each other, one guard
is removed while theother guards could know, and the walls of the
gallery shouldbe vertical [7, 8].
In computational geometry, the gallery can be abstractsimple
polygon; put a monitor abstraction for a point insimple polygon,
and then the problem can be abstracted as anart gallery plane
geometry problem; gallery guards problemcan be abstracted as how
many points can cover the entiresimple polygon. Variant problem can
be abstracted as jointguards, side cover, diagonal coverage,
coguards, orthogonalgallery guards, moving guard, limited
perspective guards,moving guard with limited perspective,
orthogonal polygonsmobile guards, and other issues [9, 10].
For unrealistic assumptions of monitor in the solvingof AGP and
its variant problem, such as magnifying themonitoring range of
single monitor, expanding the depth offield, and not limiting the
recognition accuracy and speed,lead to research in art galleries
and related issues are hard tobe good application in the actual
layout of video surveillancecapture point.
Applied researches of monitor sensors layout mainlyput video
monitor sensors layout problem into set coveringproblem.
Chakrabarty and Bulusu used the method of linearprogramming to
obtain the minimum activity to maintaincoverage node set [11, 12].
Meguerdichian et al. made morecomplex coverage model which, from
the perspective ofminimizing the uncovered area of the start,
considers theproblem of network coverage uniformity runtime based
onthe degree of coverage [13]. Erdem and Sclaroff proposedan
efficient algorithm to calculate the radial scans of eachcollection
point in the visual range of the camera, so thatthe total layout
costs are optimized while the collection pointlayout constraints
can be met [14].
Dataacquisitiondemands of
key areamonitoring
Data acquisition demands ofpassenger line monitoring
Data acquisition demands of completecoverage monitoring
Dem
ands
cove
red a
rea
Timeliness and precision
PriorityGrad
ually
cont
ain
Figure 1: Demands relationship among three levels.
3. Problem Description
In this section, the sensor networks hierarchical
optimizationproblem is described in three aspects. Firstly, the
data acquisi-tion demands of security monitoring in HRTH are
analyzed.Secondly, the hierarchical organization of sensor networks
isdescribed. Based on the previous two parts, the basic processof
security monitoring based on multilayer sensor networksis designed
in the last part.
3.1. Data Acquisition Demands of Security Monitoringin HRTH.
According to the different safety forewarningfocuses, the
securitymonitoring can be divided into three lev-els, and each
level has its specific data acquisition demands.The data
acquisition demands of security monitoring inHRTH are shown in
Table 1.
The demands relationship among three levels is shown inFigure
1.The demands covered areas are gradually increasingfrom the first
level to the third level, and the timeliness andprecision of data
acquisition are gradually increasing from theopposite
direction.
3.2. Hierarchical Organization of Sensor Networks. Based onthe
data acquisition demands analysis above, the sensor net-works for
security monitoring in HRTH are classified intothree hierarchies,
which are one-to-one correspondence tothe data acquisition demands
levels. The hierarchical orga-nization of sensor networks is shown
in Table 2.
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Journal of Sensors 3
Table 2: Hierarchical organization of sensor networks.
Sensornetwork
First hierarchy Key areamonitoring sensors
(i) Sensors in different key areas are independent and do not
haveany relevance(ii) Sensors do not need adjustment after
setting(iii) Sensors have front-end event detecting software
Second hierarchy Passenger linemonitoring sensors
(i) Sensors should be set following the passenger line(ii)
Sensors in same passenger line have association(iii) Sensors do not
need adjustment after setting(iv) The data acquired by sensors
should be continuouslytransferred to the control center to
process
Third hierarchy Complete coveragemonitoring sensors
(i) Sensors should cover all the function areas in HRTH(ii) The
monitoring areas of sensors should reduce overlaps asmuch as
possible(iii) Sensors can adjust monitoring areas after setting(iv)
The data acquired by sensors should be continuouslytransferred to
the control center to be stored
Keyareas
Passengerline
WholeHRTH
Sensornetwork
Anomalydetectiondatabase
Passengerflow
database
HRTHarea monitoring
database
Controlcenter
...
...
...
First
Secondhierarchy
Thirdhierarchy
hierarchy
Figure 2: Basic structure of security monitoring.
3.3. Basic Structure of SecurityMonitoring Based
onMultilayerSensor Networks. According to the above analysis in
thissection, a basic process of security monitoring is
designedbased on multilayer sensor networks. The structure is
shownin Figure 2.
As observed in Figure 2, in the first hierarchy, anomaliesin key
areas are detected by monitoring sensors, and thenthe anomaly
detection data are transmitted to control centerand inform
monitoring personnel to respond. In the secondhierarchy, passenger
flow data are acquired by monitoringsensors and transmitted to
control center. According to theincidence relation among sensors,
the passenger flow datacan be processed to obtain real-time
passenger flow status,search the post node in passenger line, and
forecast thevariation trend of passenger flow. The monitoring
personnelcan forewarn passenger flow congestion andmake
emergency
response based on the processing results. The third
hierarchymainly focuses on the overall safe state of HRTH.
Themonitoring personnel need to use the monitoring sensors
toobserve function areas in HRTH when the first or secondhierarchy
has safety forewarning. This hierarchy is a supple-ment for the
previous two hierarchies.
4. Sensor Networks HierarchicalOptimization Model
According to the problem description in Section 3, a
sensornetworks hierarchical optimization model is proposed inthis
section. Sensor networks for security monitoring inHRTH are
optimized from three hierarchies based on thehierarchical
organization mentioned above. The hierarchicaloptimization
framework is shown in Figure 3.
4.1. Key Area Sensors Optimization. The core concern ofkey area
sensors optimization is to determine the key areasin HRTH.
According to the different area characteristic,the key areas can be
mainly divided into congestion areasand sensitive areas. Each area
has its specific determinationmethod.
4.1.1. Congestion Areas Determination Method. The conges-tion
areas are mainly determined by the computation result.There are
three main methods to calculate the relationbetween passenger flow
and facilities capacity, which aredescribed as follows.
(1) Capacity Method. Capacity method is used to
determinefacilities congestion. This method divides the passenger
lineinto several units and calculates the capacity balance ofunits.
When the facility design capacity is less than practicalcapacity,
this facility is considered a key area. The facilitydesign capacity
is calculated by
𝐶 = 𝑊 ⋅ 𝑞 ⋅ 𝜑. (1)
𝑊 is the width of the facility. 𝑞 is the predicted passenger
flowvolume. 𝜑 is the peak period coefficient.
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4 Journal of Sensors
Key areasensors
optimization
Passengerline sensors
optimization
Whole areasensors
optimization
Congestion areasdetermination
Sensitive areasdetermination
Sensor layout ofkey areas
Passenger linetype determination
Passenger linegeneration
Sensor layout ofpassenger lines
Whole area sensorsoptimization model
A heuristicalgorithm
Sensor layout ofwhole area
Sensornetworks
hierarchicaloptimization
Figure 3: Hierarchical optimization framework.
Table 3: Calculation of three behaviors.Delay behaviors
Generation mechanism Calculation Range of application
Queuing delay The facility service capability isless than
passenger arrival rate𝜆
(𝜇 − 𝜆)2
Ticket entrance, wicket,baggage check entrance,and so forth
Congestingdelay
The facility cannot be used whenthe passengers arrive ∫
𝑡𝑛
𝑡0
𝑞 (𝑡) 𝑑𝑡 − 𝑛∫𝑛𝑇+(𝑛−1)(𝑘
1+𝑘2)𝑞(𝑡)
𝑛𝑇+(𝑛−1)𝑞(𝑡)
𝑞 (𝑡) 𝑑𝑡Ticket entrances delaycheck caused by train late
Waiting delay
The facility capacity isinsufficient, which leads to highdensity
and low speed ofpassenger flow
𝐿𝑘𝑗
𝑘𝑢𝑓
The service capability ofinterface channel betweenservice nodes
is insufficient
(2) Delay Method. Delay is an important judging parameterfor the
congestion of passenger line. The passenger delay inHRTHmainly
results from queuing, congesting, and waitingbehaviors. The
calculation of three behaviors is shown inTable 3.
(3) Density Method. Passenger flow density is an
effectiveindicator tomeasure congestion level.Thehigher density
pas-senger flow has, the more congestion in passenger line
arises.This density is named congestion density and calculated
by
𝜌𝑖𝑗(𝑡) =𝑛𝑖𝑗(𝑡)
𝑀𝑖𝑗
. (2)
𝜌𝑖𝑗(𝑡) is congestion density of the 𝑗th segment in the 𝑖th
passenger line. 𝑀𝑖𝑗is the facility available area of the 𝑗th
segment in the 𝑖th passenger line. 𝑛𝑖𝑗(𝑡) is passenger
amount
of the 𝑗th segment in the 𝑖th passenger line.
4.1.2. Sensitive Areas Determination Method. Sensitive
areasdetermination, compared with congestion areas determina-tion,
is relatively simple and does not have specific calculatingmethod.
Most of sensitive areas are determined based onthe actual demand of
security monitoring in HRTH. Thecommon sensitive areas include
distribution facility areas,fireproofing facility areas, office
areas, and security checkareas.
4.2. Passenger Line Sensors Optimization. The core concernsof
passenger line sensors optimization are to determine thepassenger
line type and generate the passenger line underestablished facility
layout.
Transferhall
Waitinghall
Ticket entrance Platform
Arriving Entering WaitingtrainGettingin train
Passenger inputline
Passenger inputprocedure
Purchasingtickets Shopping
Securitychecking Checking
Entrance Securitycheck point
Ticket office
Business service
area
Figure 4: Passenger input line and procedure.
4.2.1. Passenger Line Type Determination. The passenger linein
HRTH can be mainly divided into passenger output line,passenger
input line, and passenger transfer line. These threetypes of
passenger lines are described as follows.
(1) Passenger Input Line. The passenger input line begins
atpassenger arriving at HRTH and finishes after passenger getsin
trains. In the period between passenger arriving and leav-ing,
there are several events happening, such as purchasingtickets,
shopping, dining, andwaiting in trains.Thepassengerinput line and
procedure are shown in Figure 4.
(2) Passenger Output Line. The passenger output line beginsat
train arriving at HRTH and finishes after passenger leavesHRTH.
Compared with passenger input line, passengeroutput line has few
events and is relatively simple.The outputpassengers flow has
characteristics of being concentrated,
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Journal of Sensors 5
Platform Exit Ticket exit Transfer hall
Getting offtrain Exiting Checking Leaving
Business service area
Shopping Passenger output
procedure
Passenger output line
Figure 5: Passenger output line and procedure.
high density, and short stay time. The passenger output lineand
procedure are shown in Figure 5.
(3) Passenger Transfer Line. Passenger transfer line is
similarto passenger input line and relatively simple, so we do
notintroduce it in detail.
4.2.2. Passenger Line Generation. After determining the
pas-senger line type, passenger line is generated based on
theestablished facility layout of HRTH. Generation steps areshown
as follows.
Step 1. Mark the geometric center of facilities in HRTHfunction
areas and use these geometric centers as the origin-destination
points.
Step 2. Use directed line segments to link geometric
centersbased on passenger moving tracks in different type
passengerlines.
Step 3. Classify the directed line segments, use different
colorto denote different type passenger lines, and use
differentthicknesses lines to denote the amount of passenger
flow.
4.3. Whole Area Sensors Optimization. In order to ensurethat all
function areas in HRTH are covered by sensors, awhole area sensors
optimization framework is proposed inthis section. After space
two-dimension, space partition andvisibility analysis, we change
the whole area sensors opti-mization problem into set covering
problem and developa set covering model. The whole area sensors
optimizationframework is shown in Figure 6.
Step 1 (HRTH space two-dimension). HRTH space two-dimension is
tomake the three-dimensional space into a two-dimension ichnography
and scale down the layout of facilitiesand instruments. After HRTH
space two-dimension, we canobtain a schematic representation of
whole HRTH.
Step 2 (HRTH space partition). After obtaining the sche-matic
representation, we abstract the facilities and instru-ments into
square or rectangle and lengthen the sides of
InputSpace information of HRTH
HRTH space two-dimension
HRTH space partition
Whole area sensorsoptimization model
A heuristic
Visibility analysis
OutputSensor layout of whole area
algorithm
Figure 6: Whole area sensors optimization framework.
square and rectangle. A space partition sample is shown inFigure
7.
Step 3 (visibility analysis). Based on HRTH space partition,we
analyze the visibility of each region in schematic repre-sentation.
The visibility analysis includes two parts.
The first part is to analyze the geometric visibility. Assumethe
region center is the laying position. If the link linebetween two
regions is not interrupted by facilities or instru-ments, the two
regions are considered geometric visibility.Figure 8(a) is a
geometric visibility analysis sample. The geo-metric visibility set
of 𝑅
12is {𝑅3, 𝑅4, 𝑅5, 𝑅7, 𝑅8, 𝑅9, 𝑅10, 𝑅11,
𝑅13, 𝑅14, 𝑅15, 𝑅16, 𝑅17}.
The second part is visual range analysis. We set thecoverage of
one sensor as a circle whose radius is the visualrange of sensor.
The regions which are covered by the circleare the visual regions.
Figure 8(b) is a visual range analysissample. The visual visibility
set of 𝑅
12is 𝑉12= {𝑅7, 𝑅8, 𝑅10,
𝑅11, 𝑅13, 𝑅14, 𝑅17}.
Step 4 (a set covering model for whole area sensors
opti-mization). In this step, we develop a set covering model
todescribe the whole area sensors optimization problem.
(1) Notations and Variables. Consider the following:
𝑖, 𝑗: region index,𝑚: total amount of regions,𝑅: visual range of
sensor,dis(𝑖, 𝑗): the distance between region 𝑖 and region 𝑗,𝑊𝑖:
weighted values,𝑥𝑖: 0-1 variable; if sensor is set in region 𝑖,
𝑥
𝑖= 1;
otherwise, 𝑥𝑖= 0,
cov(𝑖, 𝑗): 0-1 variable; if the visual range of region 𝑖
cancover region 𝑗, cov(𝑖, 𝑗) = 1; otherwise, cov(𝑖, 𝑗) = 0.
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6 Journal of Sensors
Facilities and instruments
(a)
Facilities and instruments
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(b)
Figure 7: A space partition sample.
R1 R6 R10 R15
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(b)
Figure 8: Visibility analysis samples.
(2) Objective Function. The objective function of whole
areasensors optimization model is written as follows:
Minimize𝑍 =𝑚
∑𝑖=0
𝑊𝑖𝑥𝑖. (3)
The objective function minimizes the amount of sensorsto cover
whole function areas in HRTH.
(3) Constraints. The constraints of whole area sensors
opti-mization model are introduced as follows to ensure
thepractical feasibility of the solution:
𝑚
∑𝑖1=0
cov (𝑖, 𝑗) 𝑥𝑖1
≥ 1, 0 ≤ 𝑗 ≤ 𝑚, (4)
cov (𝑖, 𝑗) ⋅ (𝑅 − dis (𝑖, 𝑗)) ≥ 0,
0 ≤ 𝑖 ≤ 𝑚, 0 ≤ 𝑗 ≤ 𝑚,(5)
(1 − cov (𝑖, 𝑗)) ⋅ (dis (𝑖, 𝑗) − 𝑅) ≥ 0,
0 ≤ 𝑖 ≤ 𝑚, 0 ≤ 𝑗 ≤ 𝑚,(6)
𝑥𝑖∈ {0, 1} , (7)
cov (𝑖, 𝑗) ∈ {0, 1} . (8)
Constraint (4) represents that each region in HRTHshould at
least be covered by one sensor. Constraint (5)ensures that the
distance between sensor and covered regioncannot be larger than the
visual range of sensor. Constraint(6) represents that the region
whose distance is larger thanthe visual range of sensor cannot be
covered by this sensor.Constraint (7) and Constraint (8) are 0-1
variable constraints.
Step 5 (solution algorithm). In order to solve the opti-mization
model developed above, a heuristic algorithm isproposed in this
section. 𝑋 is the area set of two-dimensiondivision. 𝑚 is the
elements amount in 𝑋. 𝐶 is the area set of
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Journal of Sensors 7
Start
Yes
No
No
Yes
Calculate the element amount in set C
Yes
No
No
Yes
Sensor layout of whole area
Set C and S empty
Randomly select Rn in X
Rn ∈ S
Obtain the visual visibility region Vn of Rn
Add Rn to set C and S;add Vn to set S
S = X
Set qn = qn−1 qn − qn−1 ≥ 0
Set n = n + 1
n = m
Figure 9: Algorithm implement process.
sensors layout. 𝑞 is the elements amount in 𝐶. The
algorithmimplement process is shown in Figure 9.
5. A Case Study
To illustrate the proposed model and algorithm for
sensornetworks hierarchical optimization problem, a case study
isperformed by using the actual data from a specific HRTH
inChina.We choose comprehensive transfer layer of the HRTHas
optimization object.
This layer is composed by transfer hall, parking area,passenger
output system, and passenger input system. Thereare six entrances,
six exits, and four ticket offices in this layer.The transfer hall
connects with metro, taxi, and bus. Thewhole layer has various
kinds of passenger lines and crossoveramong passenger lines.
We use the hierarchical optimization method mentionedin Section
4 to optimize the sensor networks in this layer.Thehierarchical
optimization is shown as follows.
5.1. Key Area Sensors Optimization. According to the
actualpassenger flow data in this layer, we use the key area
deter-mination method mentioned in Section 4.1 to determine
keyareas. The distribution of key areas in this layer is shown
inFigure 10.
Figure 10: Distribution of key areas.
Figure 11: Passenger lines in the layer.
Figure 12: Space partition process.
5.2. Passenger Line Sensors Optimization. Through analysisof
origin-destination points and passenger moving tracks, wegenerate
the passenger lines in this layer. The passenger linesare shown in
Figure 11.
5.3. Whole Area Sensors Optimization. Follow the whole
areasensors optimization framework mentioned in Figure 6; thespace
is partitionedwhich is shown in Figure 12 and the spacepartition
result is shown in Figure 13.
After space partition, the layer is divided into 58
regions.Through the visibility analysis, we can obtain the
visualvisibility sets of 58 regions and use the heuristic
algorithmto find a solution for the whole area sensors
optimizationmodel. The final sensors layout region set is {𝑅
2, 𝑅5, 𝑅6,
𝑅9, 𝑅12, 𝑅14, 𝑅21, 𝑅22, 𝑅29, 𝑅30, 𝑅34, 𝑅36, 𝑅41, 𝑅49, 𝑅50, 𝑅57}.
The
solution obtained by the heuristic algorithm is shown inFigure
14.
According to the hierarchical optimization, the finalsensor
networks for security monitoring in HRTH are shownin Figure 15.
6. Conclusion
In this paper, we considered the sensor networks
hierarchicaloptimization problem in HRTH. A hierarchical
optimizationframework is proposed, and the problem is solved
from
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8 Journal of Sensors
R1
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R24 R32
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R31 R38R27R19
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R10
R5
Figure 13: Space partition result.
R1
R2
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R6
R7
R8 R12
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R17 R25 R33R29
R28
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R36 R46
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34
R
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10
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99
R10
R
Figure 14: Solution obtained by the heuristic algorithm.
Sensors for key areasSensors for passenger linesSensors for
whole areas
Figure 15: Final sensor networks for security monitoring in
HRTH.
three hierarchies which are key area sensors
optimization,passenger line sensors optimization, and whole area
sensorsoptimization. In the third hierarchy, a whole area
sensors
optimization model is developed and a heuristic algorithm
isdesigned. Case study on a specific HRTH in China showedthat the
hierarchical optimization method is effective tooptimize the sensor
networks for security monitoring inHRTH. In the future, considering
the layout costing inoptimization method is a possibility for
further research.
Conflict of Interests
The authors declare that there is no conflict of
interestsregarding the publication of this paper.
Acknowledgments
This research was supported by the Fundamental ResearchFunds for
the Central Universities (Grant no. 2015JBM044),the National
Natural Science Foundation of China (Grant no.61374157), and the
Talented Faculty Funds of Beijing JiaotongUniversity (Grant no.
2014RC005).
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Journal of Sensors 9
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