research article path prediction method for effective sensor...

Research ArticlePath Prediction Method for Effective Sensor Filtering inSensor Registry System

Sukhoon Lee1 Dongwon Jeong2 Doo-Kwon Baik1 and Dae-Kyoo Kim3

1Department of Computer and Radio Communications Engineering Korea University 1 Anam-dong 5-gaSeongbuk-gu Seoul 136-701 Republic of Korea2Department of Statistics and Computer Science Kunsan National University 558 Daehangro GunsanJeollabuk-do 573-701 Republic of Korea3Department of Computer Science and Engineering Oakland University 2200 N Squirrel Road Rochester MI 48309-4401 USA

Correspondence should be addressed to Dongwon Jeong djeongkunsanackr and Doo-Kwon Baik baikdkkoreaackr

Received 30 January 2015 Revised 29 May 2015 Accepted 10 June 2015

Academic Editor Antonino Staiano

Copyright copy 2015 Sukhoon Lee et al This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

The Internet of Things (IoT) has emerged and several issues have arisen in the area such as sensor registration and managementsemantic interpretation and processing and sensor searching and filtering in Wireless Sensor Networks (WSNs) Also as thenumber of sensors in an IoT environment increases significantly sensor filtering becomes more important Many sensor filteringtechniques have been researched However most of them do not consider real-time searching and efficiency of mobile networksIn this paper we suggest a path prediction approach for effective sensor filtering in Sensor Registry System (SRS) SRS is a sensorplatform to register andmanage sensor information for sensor filteringWe also propose a method for learning and predicting userpaths based on the Collective Behavior Pattern To improve prediction accuracy we consider a time feature to measure weightsand predict a path We implement the method and the implementation and its evaluation confirm the improvement of time andaccuracy for processing sensor information

1 Introduction

The Internet ofThings (IoT) has emerged with the advance ofnetworks and embedded software technologies The IoT is anovel paradigm that is rapidly growing and has a significantinfluence over application domains such as telecommunica-tions transportation and healthcare [1 2] Environmentalmonitoring and context-awareness technologies are requiredfor devices to be connected to each other for communica-tion in the IoT Particularly abundant IoT services can beprovided in the application domains where various sensorsare used In the IoT paradigm a sensor network plays animportant role as a critical and indispensable infrastructureto provide richer services to users

In the IoT paradigm several issues are considered in theWireless Sensor Networks (WSNs) area The first issue isregistering and managing sensors as the number of sensorsincreases explosively To address this the sensorweb has beendeveloped which enables sharing and browsing sensor data

through the web [3] OGC SensorWeb Enablement (SWE) isa representative specification of platforms that provide sensorinformation and sensor data on the web [4] SensorMap[5] and Sensorpedia [6] are significant researches basedon SWE Recently SensorCloud [7] and OpenIoT [8] havebeen researched to manage sensors and to provide sensordata using a cloud-computing infrastructure Through theresearch users can receive sensor data wherever needed bymobile device

The second issue is semantic interpretation and pro-cessing Several works present semantics of sensors [9]Semantic Sensor Web enhances semantics by adding simplystructured sensor metadata to the sensor web [10] Addingsemantic information such as time space and theme tosensor metadata enables interpretation of sensor seman-tics and processing various sensor data by inference [11]In particular Semantic Sensor Network Ontology (SSNO)developed by W3C can represent sensor information invarious perspectives [12]

Hindawi Publishing CorporationInternational Journal of Distributed Sensor NetworksVolume 2015 Article ID 613473 14 pageshttpdxdoiorg1011552015613473

2 International Journal of Distributed Sensor Networks

The third issue is searching and filtering sensors Sen-sor filtering is based on searching specific sensors in theaforementioned platforms for registering sensors such asSensorCloud and OpenIoT Also sensor ontologies are usedto recognize user contexts and provide user-centric servicesSearching specific sensors especially in a large numberof entities requires effective sensor filtering technologiesLinked Sensor Middleware (LSM) is a web-based sensormanagement platform connected to the Semantic Web [13]LSMuses sensor information such as sensor type and locationfor searching sensors Mayer et al proposed a method forsearching sensor information by location using web-basedstructures of a building [14] Perera et al proposed a context-awareness sensor searching technique [15] Using SSNOthey search relevant sensors for the given context by settingweights of five features including accuracy reliability energyavailability and cost

The existing sensor searching techniques provide context-aware services including tracking user location Howeverthese techniques do not consider architectural problemscaused by real-time searching or efficiency of mobile net-works Considering applications with location-based servicessuch as iBeacon [16] the existing sensor searching techniquesare not sufficient for real-time reaction when the mobiledevice receives information from a sensor close to theuser Because the mobile device needs to receive sensorinformation whenever it is requested the mobile networkQoS influences services to be provided successfully to theuser

In this paper we present a path prediction approach foreffective sensor filtering This approach predicts user pathsbased on the user location and provides sensor informa-tion located nearby the predicted paths to a mobile devicebeforehand The path prediction approach can handle real-time situation or mobile network status The presentedmethod identifies the location of a user using predefinedroad information and predicts user paths using a simplepath prediction algorithm To implement the method weuse Sensor Registry System (SRS) [17] which registers andshares sensors For effective sensor searching the systemprovides sensor information by tracking the user predictinguser paths and identifying sensors located near the predictedpaths We also analyze user patterns using a time feature tovalidate prediction accuracy

The remainder of the paper is organized as followsSection 2 describes the problem being addressed in thispaper and gives an overview of the presented approach forsolving the problem Section 3 describes the path predictionmethod for SRS Section 4 presents the implementation andexperiment of the method and Section 5 gives an overview ofthe existing path prediction algorithms Section 6 evaluatesthe method in comparison to the existing work Section 7concludes the paper

2 Problem Definition and Solution Approach

This section discusses several problems in sensor filtering Asa solution for the problems we present a sensor registeringand sharing system Sensor Registry System (SRS) which is

able to search and filter sensors Then we extend SRS basedon path prediction to resolve the problems

21 Problem Definition The existing sensor filtering tech-niques are not suitable for real-time synchronization Sensorplatforms such as SensorCloud and OpenIoT have severalproblems when they have to support real-time sensor searchin the mobile computing environment One problem is thatthe performance of personalized sensor search is low becauseof limited mobile resources This mandates the sensor filter-ing process to be worked out on the server side instead ofthe mobile device The next problem is that a mobile deviceusing the sensor platforms has to allocate a large amount ofresources for requesting and receiving sensor informationThis problemmakes service process slowwhen the user wantsto provide some services Therefore the sensor platformsshould send sensor information before request and mobiledevice immediately uses the information needed Anotherproblem is that mobile devices are sensitively affected bythe mobile network QoS If a user moves into an unstablenetwork connection area the mobile device of the usercannot access sensor platforms and thus is not able to receivethe necessary sensor information Therefore the sensorfiltering process should recognize the user context predictneeded sensors for the user and receive sensor informationin advance

To detail the last problem Figure 1 shows an exampleof an unstable network connection status situation in theUK network coverage map provided by the EE coveragechecker [18] In Figure 1(a) the green area indicates the 4Gmobile network coverage the purple area indicates the 3Gmobile network coverage and the pink area indicates the 2Gmobile network coverage Figure 1(b) illustrates an exampleof success and failure in obtaining sensor information whena user moves into an unstable network connection areaSuppose that a user is moving and communicating with thesensor platform in real-time using a 4Gmobile device (whichis not supported in a 3G mobile communication system)When the usermoves along the blue arrow in the 4G coveragearea in Figure 1(b) the mobile device successfully connectsto the sensor platform and obtains the sensor informationHowever when the user moves into the 3G coverage areafollowing the red arrow the mobile device fails to obtain thesensor information around the user

This paper proposes a sensor filtering method that canprovide necessary sensors when a mobile device requiressensor information to the sensor platform in real-time whilepredicting the user paths and sending sensor informationnearby predicted paths to the mobile device using the sensorplatform

22 Path Prediction Approach This paper aims at improvingsensor filtering in Sensor Registry System (SRS) [17] whichis a sensor platform for registering and sharing sensors SRSwas proposed for semantic interoperability between sensorsand devices in a heterogeneous sensor network environment[19] Based on ISOIEC 11179 [20] SRS registers andmanagessensor information SRS also shares and provides metadataand sensor information such as locations units types and

International Journal of Distributed Sensor Networks 3

2G 3G 4GCoverage types key

(a)

Successfully obtainssensor information


s1

s2

s3

s4

s5

s6

s7

s8

s9

s10s11

s13

s12

s14s15

s16

s17s18

s19

s20

s21

s22

s23

s24

s25

s26

Fails to obtainsensor information

(b)

Figure 1 UK coverage checker and an example of a problem (a) 2G3G4G coverage map (b) example of an unstable network connectionstatus problem

other relevant information (eg manufacturer informationinstallation organization information) SRS enables a mobiledevice to instantly and directly interpret and process sensordata from heterogeneous sensors

A primary feature of SRS is that it allows a mobiledevice to access the system through the Internet and obtaindirectly sensor data from a sensor networkThemobile devicechanges its location and communicates with different sensorsas the user of the device moves to a different sensor networkHowever mobile devices can obtain only raw data fromsensors To address this SRS provides sensor information formobile devices to process semantics of raw data

In this work we transmit sensor information frommultiple sensors simultaneously for rapid services specific tomobile devices Because SRS receives a request to return thesensor information of one sensor the mobile device preloadsthe sensor information of near sensors from the sensorinformation set created by the proposed approach Thus themobile device can immediately use the sensor informationwhen it is required This approach is enabled by recognitionof user patterns and sensor filtering in advance

The approach collects user locations learns movementpatterns predicts user paths and preloads sensor informa-tion of the sensors located near the predicted paths Themobile device synchronizes with SRS and transmits thecurrent location to SRSThen SRS predicts a user path basedon the current location and transmits the sensor informationset of the sensors located near the predicted path to themobile device This enables the mobile device to processservices using the preloaded sensor information even if theuser moves into an unstable network connection area

Figure 2 shows SRS architecture extended by the pre-sented approach It consists of Sensor Filtering ModuleSensor Information Management Path Predication DB andSensor DB The Sensor Filtering Module involves userlocation monitoring path prediction and path and sensormatching The mobile device of a user constantly accessesSRS which allows SRS to monitor the user location If theuser changes hisher location SRS predicts the moving path

Sensor Registry System

Sensor Filtering Module

Sensor Information Managementmodule

Userlocation

monitoringPath

predictionPath and

sensor matching

Sensor information

searchingSensor DB

Sensor information

set

User Path PredictionDB

Request

Return

Figure 2 Extended SRS architecture including the proposedapproach

and collects the identifiers of the sensors located near theuser The Sensor Filtering Module connects to the PathPredictionDB and exchanges related data during the processIn Sensor Information Management the collected sensoridentifiers are used to search for sensor information Thesensor information acquired from Sensor DB is returned tothe user after SRS creates a sensor information set In thiswork we focus on path prediction for effective sensor filteringin SRS We also define a time feature for accurate predictionresults and evaluate the prediction accuracy

3 Path Predication Method

This section presents the path prediction method for sensorfiltering in SRSThemethod is composed of a path identifica-tion method and a path prediction algorithm We first definea set of variables used in the method and then discuss time-based predication

31 Path Prediction Process Figure 3 illustrates an overviewof the path prediction process Predication involves a set of


Preprocessing

Loading road information

Loading user history

Measuring weight of path fragment

Connecting a mobile device

Obtaining user point

Identifying path fragment

Predicting path fragment

Figure 3 Overview of the path prediction process

f1

f2

f3 f4 f5

f6

f7

cp1

cp2

up1up2

up3

up5

up4

Figure 4 Graphical representation of roads and user locations

preprocessing steps In the preprocessing SRS loads roadinformation and user history It then measures the weightof each path fragment which is a unit for path predic-tion To store path prediction information SRS creates adatabase Upon completion of the preprocessing SRS waitsfor a connection request from the mobile device When aconnection is established SRS collects the geolocation points(eg latitude longitude) of the mobile device for a specifictime duration and identifies the path fragment where theuser is currently located Then SRS predicts a path fragmentthat the user might move to using weights measured in thepreprocessing

32 Road Definition and Path Fragment This sectiondescribes the representation of a path and a user The pathprediction uses predefined road informationThe road that auser can move on is represented by a line The user locationis recognized using the GPS of the mobile device such aslatitude and longitude

Figure 4 shows a graphical representation of roads anduser locations A single point represents a location measuredby theGPS and is expressed as a pair119901 of (latitude longitude)A user point (119906119901) is the location of a user (119906) and 119880119875

119906is the

sequential set of locations of 119906 In Figure 4 the sequence of

locations for 119906 is 1199061199011rarr 119906119901

2rarr 119906119901

3rarr 119906119901

4rarr 119906119901

5and

119880119875119906= 119906119901

1 1199061199012 1199061199013 1199061199014 1199061199015 The blue shadow represents

roads on which a user can move and the solid lines in theshadow represent roads A crossroad point (119888119901) indicating aconnection between the roads is represented as a point Apath fragment (119891) is a unit of paths and it is represented asa line connection between two 119888119901s (eg 119891

4= (119888119901

1 1198881199012))

A path fragment has also a direction (119889) based on the usermovement history and the direction is decided by selectinga start point between 1198881199011 and 1198881199012 The path fragment set (119865)which includes a direction for each road is defined as follows

119865 = 119891119889

119894| 1le 119894 le 119899 119889 isin 1 minus 1 (1)

where 119899 is the number of path fragments and119889 is the directionof a path fragment

The direction 119889 of a path fragment 119891may be either 1 rep-resenting forward or minus1 representing backwardTherefore inFigure 4 119891

4is expressed as 1198911

4= 1198881199011rarr 119888119901

2and 119891

minus1

4=

1198881199012rarr 119888119901

1 An undirected path fragment (119891

119894) implies a pair

of (1198911119894 119891minus1

119894) In this work we use predefined road information

such as the crossroad points and path fragments for learningand predicting user paths As the results of prediction theproposed method returns a predicted path fragment (119891119889

119894)

Finally the set of connected fragments (119862119865119889119894) defines all

the path fragments that are connected to 119891119889119894 For example

in Figure 4 11986211986514represents all the path fragments connected

to 1198911

4 Because of the direction of 1198911

4 11986211986514includes all the

path fragments starting from 1198881199012and it is expressed as 1198621198651

4=

119891minus1

2 1198911

5 1198911

7 119862119865119889119894is defined as follows

119862119865119889

119894= 119891119889

119895| 119891119889

119894= 119888119901119886997888rarr119888119901

119887 119891119889

119895= 119888119901119887

997888rarr119888119901119888 119891119889

119894 119891119889

119895 sube119865 119889 isin 1 minus 1

(2)

The following list of symbols and their definitions areused in path prediction

Symbols and Definitions for Path Prediction119906 A user119880119904119890119903 A set of users 119880119904119890119903 = 119906

1 1199062 119906

119899

119901 A point 119901 = (latitude longitude)119906119901 A user point 119906119901 = (latitude longitude)119880119875119906 The set of user points by user

119880119875119906 1199061199011 1199061199012 119906119901

119899

119888119901 A crossroad point 119888119901 = (latitude longitude)119862119875 The set of crossroad points119862119875 119888119901

1 1198881199012 119888119901

119899

119891119894 The 119894th path fragment 119891

119894= 1198911

119894 119891minus1119894

= (119888119901119886 119888119901119887)

119891119889

119894 The 119894th path fragment with direction 119889

1198911

119894 119888119901119886rarr 119888119901

119887 119891minus1119894

= 119888119901119887rarr 119888119901

119886

119865 The set of all path fragments119865 11989111 119891minus1

1 1198911

2 119891minus1

2 119891

1

119899 119891minus1

119899

119862119865119889

119894 The set of path fragment connected with 119891119889

119894


upf2

f1

f3

f4

p1

p2l1 l2

lpr

lh2

lh1lf1205791 1205792

cp3

cp1 cp2

Figure 5 Graphical representation of path fragment identification

33 Path Fragment Identification In this section we presenta method for identifying the path fragment of a user usingthe location of the user That is a location 119906119901 is projectedon a path fragment by the path fragment identification Theprojection implies that the user exists on the path fragmentwhich is expressed as a road

The process of path fragment identification measures thevertical distance between the user point and a path fragmentand defines the user located path fragment with the lowestvertical distance Figure 5 illustrates a graphical represen-tation of the path fragment identification To measure thevertical distance between the location 119906119901 and a path fragment119891119894 we use an equation that determines the height of a triangle

given by three side lengths 1198911is a path fragment between 1198881199011

and 1198881199012 and we calculate a vertical distance (119897ℎ1) between 119906119901

and1198911using three points119906119901 1198881199011 and 11988811990121199011 is the projection

of 119906119901 on 1198911and 119897119901119903

is a length between 1198881199011 and 1199011 Angles1205791and 120579

2are angles of distance pairs (119897

1 119897119891) and (119897

2 119897119891)

respectively The vertical distance is measured as follows

dist (119906119901 1198911) = 119897ℎ1 = radic11989712minus 119897119901119903

2

= radic11989712minus (

11989712minus 1198972

2+ 119897119891

2

2119897119891

)

2

(3)

where 11989712+ 119897119891

2ge 1198972

2 and 11989722+ 119897119891

2ge 1198971

2119906119901 is a user point and 1198911 is a path fragment on which 119906119901

is projected Equation (3) has a constraint that both 1205791and 1205792

are acute angles because the user point has to be projectedon the path fragment The constraint is measured by thePythagorean theorem (eg 119897

1

2

+119897119891

2

ge 1198972

2 1198972

2

+119897119891

2

ge 1198971

2)Thisenables avoiding infeasible calculations such as the verticaldistance between 119906119901 and 1198913 which cannot be projected

After calculating 119897ℎ1

using (3) we calculate the verticaldistance 119897

ℎ2between 119906119901 and 1198912 In comparison 119897

ℎ1is shorter

than 119897ℎ2 Thus 1198911 is identified as the user location To predict

the subsequent path fragment of 119906119901 we identify the directionof the path fragment Both the result of the 119906119901 identificationand the path fragment history of the user are requiredfor identifying the direction The previous path fragment(119901119903119890V119891) is the path fragment from which the user is coming

When a path fragment is identified using 119901119903119890V119891 it is possibleto identify not only the path fragment located by the user butalso the direction alongwhich the user hasmovedGiven thatthe identified path fragment (119894119889119891119889

119906119901) is defined as follows

119894119889119891119889

119906119901= arg min119891119894isin119865

dist (119891119894 119906119901) cap119862119865

119901119903119890V119891 (4)

where 119901119903119890V119891 is the previous path fragment (119891119889119894) before

reaching the current path fragment (119894119889119891119889119906119901)

34 Collective Behavior Pattern and Weight MeasurementThe proposed path prediction method aims at enabling SRSto provide effective and stable sensor information SRS shouldhave an acceptable performance in a mobile environmentwhere resources are limited Also the path prediction algo-rithm for sensor filtering does not need to predict entire-range user paths because the path prediction is used onlyin an unstable network connection area where the networkconnection of a mobile device might be intermittently dis-connectedTherefore the algorithm should be able to predictclose-range path predictionwith fast performance in amobileenvironment

There exist several personalized path prediction algo-rithms (see Section 61) but they are not suitable for theabove requirements To satisfy the requirements we proposea path prediction algorithm based on Collective BehaviorPattern (CBP) [21] CBP is a concept that a collective behaviorinfluences personal behavior (eg Point of Interest) TheCBP-based path prediction algorithm (CBP-PP) has loweraccuracy than the personalized path prediction algorithmsHowever CBP-PP is able to measure weights and predictpaths on the server side and it has high performance in amobile environment CBP-PP also supports the case wherethe user has no history in a specific path

To predict paths based on CBP we need to learn andmeasure weights for path fragments In the preprocessing ofmeasuringweight we allocate aweight to each path fragmentIn this paper we have the frequency of using a path fragmentto be the weight The weight 119908119889

119894of a path fragment 119891119889

119894is

defined as follows

119908119889

119894= the number of moves along 119891119889

119894 (5)

That is 119908119889119894indicates how frequently a user has passed

along the 119894th path fragment in the direction of 119889 SinceCBP-PP takes into account all the users measuring weightinvolves the move history of all users to be used and thesame value is used for 119908119889

119894for all the users If an individual

weight is measured for each user it requires additional costsfor storing different weights for all the path fragments foreach user Therefore one path fragment has the same weightdetermined by the path fragment history of all the users

Algorithm 1 presents a weight-measuring algorithmEach user (119906) has a sequential set of user points (119880119875

119906) and

acquires the identified path fragment (119894119889119891119889119906119901) for a user point

(119906119901) If 119894119889119891119889119906119901

is equal to the previous path fragment (119901119903119890V119891)it indicates that the user has not moved into the next path


weight measuring(1) User [] larr get all users ( )(2) for each User u do(3) 119880119875 [] larr get user points (u)(4) prevf larr null(5) for each UP up do(6) idf larr get identified path fragment (up)(7) if prevf = idf then when user moves another path fragment(8) 119894 larr get path fragment number (idf )(9) 119889 larr get path fragment direction (idf )(10) 119908[119894][119889] larr 119908[119894][119889] + 1(11) prevf larr idf(12) endif(13) endfor(14) endfor(15) return 119908[][]

Algorithm 1 Weight-measuring algorithm

fragment and the algorithm returns and processes the next119906119901 to be identified If it is not equal it indicates that the userhas moved into the next path fragment and thus the weight(119908119889119894) of 119894119889119891119889

119906119901is increased by 1The algorithm assigns 119894119889119891119889

119906119901to

119901119903119890V119891

35 CBP-Based Path Prediction Algorithm The presentedpath prediction method produces the next path fragmentto which the user moves after the currently located pathfragment is evaluated for prediction The method is basedon a greedy algorithm that determines heuristic solutionsusing empirical knowledge The finding mechanism for alocal solution in the greedy algorithm is suitable for close-range path prediction The use of empirical knowledgein the greedy algorithm can satisfy the requirement thatpath prediction must be based on collective behaviors notpersonal behaviors

The presented path prediction algorithm compares pathfragments by weight and selects one that has the maximumweight as the predicted path fragment using the greedyalgorithmThe compared path fragments are then connectedto the currently located path fragment of the user Thepredicted path fragment 119901119891119889

119894is defined as follows

119901119891119889

119894= arg max119891119889

119895isin119862119865119889

119894

119908119889

119895 (6)

119901119891119889

119894represents the path fragment 119891119889

119895that has the maxi-

mumweight119908119889119895The path fragment is selected from the set of

path fragments connected to 119891119889119894which may be the identified

path fragment for the current user pointFigure 6 shows an example applying the CBP-PP algo-

rithm In the figure a user 119906 has made a sequential move119880119875119906= 119906119901

1 1199061199012 1199061199013 and is currently located at 119906119901

3 From

the current location the user may move to 1199061199014 1199061199015 or 1199061199016

At all the points of 1199061199011 1199061199012 and 119906119901

3 the user identifies

1198911

1as the identified path fragment using 1198941198891198911

119906119901 The next path

f11 (20)

fminus12 (30)

f14 (10)

fdi (wd

i )

f13 (15)

up1up2

up3

up4

up5

up6

Figure 6 An example of CBP-PP

fragment is selected from1198621198651

1= 119891minus1

2 1198911

3 1198911

4which is the set

of path fragments connected to 11989111 The weights of the path

fragments in 11986211986511are 30 15 and 10 and the fragment 119891minus1

2has

the highest weightThus 119891minus12

is selected as the predicted pathfragment (119901119891119889

119894)

Algorithm 2 presents the CBP-based path predictionalgorithm This algorithm uses the 119906119901 and 119908

119889

119894measured

in Algorithm 1 and identifies the path fragment (119894119889119891119889119906119901)

currently located by 119906119901 Then the algorithm determines a setof connected path fragments (119862119865119889

119894) with respect to 119894119889119891119889

119906119901and

selects the path prediction that has the maximum weight 119908119889119894

in 119862119865119889119894as the predicted path fragment (119901119891119889

119894)

The approach predicts one path fragment at a time Thealgorithm takes into account mobile computing power andhuman walking speed for accurate results The approachis effective for predicting short paths supported by thefragmentation of paths In the case that the amount of sensorinformation provided by SRS is overly large a dynamic pathrevision is required for correct prediction


path prediction (119906119901 119908[][])(1) idf larr get identified path fragment (up)(2) 119862119865[] larr get connected fragments (idf )(3) 119901119891 larr null predicted path fragment(4) maxweight larr 0(5) for each CF cf do(6) 119894 larr get path fragment number (cf )(7) 119889 larr get path fragment direction (cf )(8) if maxweight lt 119908[119894][119889] then set pf by maximum weight(9) maxweight larr 119908[119894][119889]

(10) 119901119891 larr 119888119891

(11) endif(12) endfor(13) return 119901119891

Algorithm 2 CBP-based path prediction algorithm

Table 1 Time elements and time duration

119879 Time duration1199051 0600sim08591199052 0900sim11591199053 1200sim12591199054 1300sim16591199055 1700sim18591199056 1900sim21591199057

2200sim0559

36 CBP-PP with a Time Feature CBP which is used as thebase for the path prediction algorithm has a limitation thatits accuracy is lower than personalized path prediction Toimprove accuracy we consider time in the algorithm Theimproved algorithm is namedCBP-PP119905 A usermakesmovesto different locations on certain patterns throughout a dayFor example a user goes to work in the morning moves outfor lunch during the lunch hour and comes back to homeafter work in the evening A similar behavior is observed inmany people This is a type of collective behavior patternsby time We analyze such patterns in terms of relevant timeduration to improve the accuracy of prediction

Suppose the time elements and time durations in Table 1We appropriately divide 24 hours into 7 elements by behaviorpatterns of users For time analysis the expression of thepath fragment set the connected path fragment set andthe weight defined above are modified to take into accounttime The expression of the predicted path fragment is alsomodified The following redefine 119865 119862119865119889119905

119894 119908119889119905119894 and 119901119891119889119905

119894in

consideration of time

119865 = 119891119889119905

119894| 1le 119894 le 119899 119889 isin 1 minus 1 119905 isin 119879

119862119865119889119905

119894= 119891119889119905

119895| 119891119889

119894= 119888119901119886997888rarr119888119901

119887 119891119889

119895= 119888119901119887997888rarr119888119901

119888 119891119889

119895

isin119865 119889 isin 1 minus 1 119905 isin 119879

119908119889119905

119894

= the number of moves along a path fragment (119891119889119894)

at a time (119905)

119901119891119889119905

119894= arg max119891119889119905

119895isin119862119865119889119905

119894

119908119889119905

119895

(7)

Algorithm 3 presents the weight-measuring algorithmwith time The algorithm is similar to the algorithm inAlgorithm 1 However the addition of time 119905 details theweight 119908119889119905

119894which further elaborates the prediction

Algorithm 4 describes the CBP-based path predictionalgorithm with time The algorithm also is similar to thealgorithm in Algorithm 2 but it uses the time-consideredweight (119908119889119905

119894)

4 Implementation and Experiment

41 System Implementation To implement the proposed pathpredictionmethod we have developed several applications tobe run on the server and mobile devices On the server sideapplications are developed for managing path fragments anduser locations predicting path fragments and returning theprediction results On themobile device side applications aredeveloped for tracking user locations displaying identifiedpath fragments from user locations and verifying pathprediction Table 2 specifies the development environmentfor the implementation

Figure 7 shows the data model for implementing the pathprediction algorithm The table User is created to identifyusers and the tableUserPoint is created to store and track userlocations and times To represent roads crossroad points andpath fragments are created in the table CrossroadPoint andPathFragment respectively The table PathFragmentWeightstores weights for path fragments with a direction and time

Figure 8 presents screenshots of the implementation ona mobile device Figure 8(a) displays crossroad points for


weight measuring with time(1) User [] larr get all users ( )(2) for each User u do(3) 119880119875 [] larr get user points (u)(4) prevf larr null(5) for each UP up do(6) idf larr get identified path fragment (up)(7) if prevf = idf then when user moves another path fragment(8) 119894 larr get path fragment number (idf )(9) 119889 larr get path fragment direction (idf )(10) 119905 larr get current time ( )(11) 119908[119894][119889][119905] larr 119908[119894][119889][119905] + 1(12) prevf larr idf(13) endif(14) endfor(15) endfor(16) return 119908[][][]

Algorithm 3 Weight-measuring algorithm with time

path prediction with time (119906119901 119908[][][])(1) 119905 larr get current time ( )(2) idf larr get identified path fragment (up)(3) 119862119865[] larr get connected fragments (idf )(4) 119901119891 larr null pf is a predicted path fragment(5) maxweight larr 0(6) for each CF cf do(7) 119894 larr get path fragment number (119888119891)(8) 119889 larr get path fragment direction (119888119891)(9) if maxweight lt 119908[119894][119889][119905] then set pf by maximum weight(10) maxweight larr 119908[119894][119889][119905]

(11) 119901119891 larr 119888119891


Algorithm 4 CBP-based path prediction algorithm with time

Table 2 Development environment

Feature DetailsOS Windows 7 Professional K (x86)Processor Intel(R) Core(TM) i5-2500 330GHzRAM 4GBDevelopment language Android JSPMobile OS Android OSAndroid emulator version 412Web server Apache Tomcat 808Database MySQL 55

a path prediction and path fragments connected to eachcrossroad point Figure 8(b) shows the sequence of actualuser points Figure 8(c) shows the projection results for theuser points on path fragments As shown in Figure 8(c) it can

be confirmed that each user point is correctly identified alongpath fragments

Figure 9 shows the path prediction results The blue linesin the figure represent the path fragment currently occupiedby the user On the other hand the black lines representthe actual path fragments taken after the blue line The redlines represent the predicted path fragment for the currentuser location Figure 9(a) shows the path prediction resultswithout considering time and Figure 9(b) shows the resultswith time considered In the figure we can confirm that timeconsideration obviously influences the prediction results

42 Experiment For the experiment we have also developeda mobile application for tracking user locations collectingactual user GPS points and predicting user paths Five usersparticipated in the experiment They collected user points bymoving around a university campus and near areas for tendaysThe user points that are outside of the experiment areasare removed from the collection


id

id

id

id

idChar(20) Char(20)

Char(20)

Char(20)Char(20)Char(20)

Char(20)

Char(20)

Char(20)

NN NN

NNNNNN

NN

NN

(PK) (PK)

(PK)

(PK)

(PK)

(FK)(FK)

(FK)

(FK)

direction IntIntInt

time

time

weight

cp1 cp2

cp1cp2

fragmentid

fragmentidlat

lat

lon

lonDoubleDouble

DoubleDouble

nametelemailaddressorganization

Varchar(200)Varchar(20)Varchar(200)Varchar(500)Varchar(200)

Datetime

userid

userid

CrossroadPoint

PathFragment

PathFragmentWeight

UserPoint

User

Figure 7 Data model for path prediction

(a) (b) (c)

Figure 8 Screenshots of implementation (a) crossroad points and path fragments (b) sequenced user points and (c) identified pathfragments and projection points

Figure 10 presents the screenshots of the user pointsused in the experiment We collected 5871 user points anddistinguished 117 datasets from the collection as user pathsFigure 10(a) indicates the collected user points within theuniversity area and Figure 10(b) shows user points near tothe university area The collected user points are used formeasuring weights and fed into the path fragment predictionalgorithm

5 Evaluation

This section evaluates the effectiveness of the path prediction-based approach by simulation It also evaluates the imple-mented system and the proposed algorithm using the exper-iment results First we discuss an advantage of the extended

system Path Prediction-based SRS (PP-SRS) in comparisonto the previous version of SRS Then we compare the CBP-based path prediction algorithm (CBP-PP) and the CBP-based path prediction algorithm with the time-consideredalgorithm (CBP-PP119905) in terms of processing time and accu-racy

51 Service Reliability Evaluation This section describes thecomparison SRS and PP-SRS for reliability A mobile devicetries to access SRS or PP-SRS and acquires sensor informationin real-time However if the device fails to access SRS orPP-SRS due to the low quality of the mobile network itis impossible for the device to interpret the semantics ofsensors which further disables a user to provide servicesusing sensors In general the QoS of the mobile network is


(a) (b)

Figure 9 Screenshots of implementation (a) path prediction result without time (b) path prediction result with time

(a) (b)

Figure 10 Screenshots of experiment result (a) user point collection in the university area (b) user point collection near the university area

evaluated in terms of coverage accessibility and audio quality[22] Coverage is the signal strength received by a mobileterminal It indicates the probability of network connectionof the mobile device at the user location Coverage is dividedinto coverage bad coverage and absence of coverage by signalstrength Accessibility is the capacity to successfully establishcommunication calls between two terminals It is the proba-bility of connection failure by an interruption when a mobiledevice attempts to connect to a server Accessibility is dividedinto normal calls release representing successful connectionand abandoned calls representing connection failure Audioquality is the status of conversation perception during asuccessful call It is the probability of receiving unclearanswers from a server concerning requested informationafter the mobile device accesses the server Audio quality isdivided into poor fair and good

A mobile device might fail to access SRS when a useris located in an unstable network connection area In suchan area the QoS of the mobile network is low in terms

Table 3 Mobile network QoS factors and statuses

QoS factor High quality Low quality

Coverage Coverage Bad coverage absence ofcoverage

Accessibility Normal callsrelease Abandoned calls

Audio quality Good fair Poor

of coverage accessibility and audio quality Table 3 showsexamples of the QoS factors for high and low quality Lowquality QoS causes frequent failures of access to SRS That isa mobile device receives incomplete sensor information fromSRS or PP-SRS when it requests An access failure may occurwhen any of the QoS factors is of low quality A ratio of accessfailure (119877AF) is calculated by dividing the number of accessfailures by the number of access requests


8993

6999

4006

9487

8026

5172

9687

8451

5663

98908868

6134

2030405060708090

100

10 30 60

Serv

ice r

eliab

ility

rate

()

Access failure rate ()

SRS PP-SRS with accuracy 50PP-SRS with accuracy 70 PP-SRS with accuracy 90

Figure 11 Service reliability rate of SRS and PP-SRS

Service reliability rate (119877SR) is the probability of success-fully providing services to a mobile device when they arerequested To measure 119877SR we have developed a simulator togenerate access failures when services are requested and wecount the number of successful services In the case of SRS amobile device is able to receive immediately necessary sensorinformation according to 119877AF and provide the requestedservice to the user In PP-SRS the mobile device is also ableto receive necessary sensor information according to 119877AF Ifthe mobile device cannot receive sensor information due tothe access failures it can use preloaded sensor informationaccording to a path prediction accuracy (119877PA)Therefore119877SRis measured as follows

119877SR =the number of Service Successesthe number of Service Requests

= (1 minus119877AF) + (119877AF times119877PA)

(8)

where 119877AF is the access failure rate and 119877PA is the pathprediction accuracy Since PP-SRS only uses a path predictionmethod 119877PA is set to zero in SRS evaluation

119877SR is the ratio of the number of service successes to thenumber of service requests It can be also calculated by theequation about the access successes rate and the predictionsuccess rate after the access failure as shown in (8) If a mobiledevice successfully accesses PP-SRS the requested servicesare provided to the user on the other hand if it failed serviceproviding depends on the rate of path prediction accuracy

For comparison evaluation we use a simulator for mea-suring119877SR and counting provided services for amobile devicewhen services are requested 106 service requests were usedand the simulator stochastically decides by (8) the success orfailure of the services

Figure 11 shows119877SR for SRS and PP-SRS when119877AF is 1030 and 60 We compare SRS with three cases of PP-SRSwith different 119877PA of 50 70 and 90 for each case As aresult each system has the highest 119877SR at 119877AF 10 and all thethree cases of the PP-SRS have a higher 119877SR than SRS Thehigher the 119877PA of the PP-SRS is the higher the 119877SR is If anaccess failure occurs the service fails in SRS whereas PP-SRSis able to successfully provide services using preloaded sensor

Table 4 Processing time evaluation result

Path fragment CBP-PP (ms) CBP-PP119905 (ms) Difference (ms)119891001 4152 4303 151119891002 4211 4540 330119891003 4465 4658 193119891004 4530 4672 142119891005 4818 5005 188119891006 4102 4101 minus001119891007 4420 4847 426119891008 3593 4079 486119891009 4102 4206 103119891010 3928 3997 069Average 4232 4441 209

information through the path prediction The experimentshows that the proposed PP-SRS is more reliable than SRS

52 Processing Time Evaluation We evaluate the processingtime of CBP-PP and CBP-PP119905 with ten path fragments witha direction selected from the collected path fragments Wealso compare the results of identifying paths and predictiontime of CBP-PP andCBP-PP119905This also shows the overheadscaused by the time consideration in CBP-PP119905 Table 4 showsthe processing time of CBP-PP and CBP-PP119905 and the timedifference for the ten selected path fragments The resultsshow that CBP-PP is faster than CBP-PP119905 in all pathfragments except one ldquof006rdquo The average processing time ofCBP-PP is measured as 4232ms while that of CBP-PP119905 ismeasured as 4441ms which results in a 209ms differenceThe difference reflects the overhead (466 decline) causedby time consideration in CBP-PP119905

53 Accuracy Evaluation The evaluation of accuracy isconcerned with measuring the accuracy of the predictedpath fragment using the datasets collected by the five usersFigure 12 presents the accuracy comparison of CBP-PP andCBP-PP119905 The user path for the prediction test is notconsidered in the evaluation

Figure 12(a) shows the accuracy of CBP-PP andCBP-PP119905for 50 datasets CBP-PP shows 248 accuracy on averageand CBP-PP119905 shows 43 accuracy on average Figure 12(b)indicates the accuracy for 116 datasets The average accuracyof CBP-PP is 556 while that of CBP-PP119905 is 874 In bothcases CBP-PP119905 shows a higher accuracy thanCBP-PP whichconfirms that time consideration improves the accuracy ofpath predication Table 5 shows the accuracy of CBP-PP andCBP-PP119905 and the difference rate for 116 datasets The resultconfirms that the accuracy of CBP-PP119905 is 646 on averagesuperior to CBP-PP

6 Related Work

This section presents related work about path predictionresearch We describe personalized pattern-based path pre-diction research using personal location tracking data and


020406080

100

u001 u002 u003 u004 u005

38 40

13 825

45 40 38 4250

Accu

racy

()

User

CBP-PPCBP-PPt

(a)

020406080

100

u001 u002 u003 u004 u005

4570

3850

7589 90

7583

100

Accu

racy

()

User

CBP-PPCBP-PPt

(b)

Figure 12 Accuracy evaluation result (a) user paths = 50 datasets (b) user paths = 116 datasets

Table 5 Accuracy evaluation result table for 116 datasets

User CBP-PP () CBP-PP119905 () Difference (pp)119906001 45 89 98119906002 70 90 29119906003 38 75 97119906004 50 83 66119906005 75 100 33Average 556 874 646

discuss problems of the existing work in applying them toextending SRSWe also discuss thework onCollective Behav-ior Pattern- (CBP-) based path prediction using locationtracking data of groups

61 Personalized Pattern-Based Prediction Numerous tech-niques have been studied for predicting locations or pathsusing user mobility [23ndash25] The majority of the exist-ing research uses probabilistic models along with context-awareness and datamining techniquesThey also use person-alized path prediction using variable user information

Samaan and Karmouch [23] proposed an architecturefor predicting personal mobility using contextual knowledgeand a spatial conceptual map Given a user context and anarea of interest defined on a map the system predicts auser location using the Dempster-Shafer theory The systemreturns a predicted path created by searching a path fromthe current location of the user to the predicted locationThe prediction result is only influenced by user profiles anddefined rules So the prediction result cannot be improvedby data collection such as the user mobility data and systemexperiences

Chen et al [24] presented a personal route predictionsystem that stores user location data from GPS and predictspaths by learning the data It defines Regions of Interest (ROI)as a criterion which is the staying time of the user It creates abasic Markov model based on frequency The Markov modelis then used to predict paths from the current location Theydivide a map into cells and provide patterns moving towardsthe ROI of the users Unlike our work they do not predictdetailed paths

Kim et al [25] described a probabilistic graphical modelthat acquires user location data fromGPS It uses a predictionapproach similar to that in the work by Chen et alThemodelincludes processes for combining several paths that have highsimilarity in path learning

The existing research is based on user data for predictionIf a user moves to a new area (eg touring) personalizedlearning is very hard since there exist no training datasets forthe user

62 Collective Behavior Pattern-Based Prediction There aresome works (eg [21 26]) based on CBP for addressing thepersonal pattern problem in Section 61 CBP is based onthat collective behaviors influence personal behaviors whichenables predicting user locations and moves A CBP-basedmethod can predict paths using the information of peoplethat have visited an area even if there is no history for aspecific user [21]

Xiong et al [26] proposed a prediction method basedon collective behavioral patterns This method predicts userlocations based on the cell tower id of a phone They use ahybrid method of CBP and personalized patterns Howeverthe method cannot provide detailed user paths since it canpredict only cell towers

CBP-based methods have two advantages Firstly theycan predict a user path using group location data withoutthe user location data Also their prediction is fast at thegroup level However group-level models often cause lowaccuracy because it does not analyze the personal patternThis motivated the hybrid method of the CBP-based methodand personalized pattern-based model by Xiong et al

7 Conclusion

The Internet of Things (IoT) has emerged and systems forregistering andmanaging sensor information have advancedSRS is developed to dynamically support sensor informa-tion and accurately process the semantics of heterogeneoussensors As the number of sensors in the IoT environmentincreases explosively so does the importance of sensorfiltering in sensor management systems


There have been several sensor filtering problems ariseninmobile computing environments such as low performancelow resource and unstable network status Searching sensorsin real-time requires a rapid connection and process and pro-viding services consistently and immediately regardless usermobility To address this we have presented a path predictionmethod for effective sensor filtering In the method we useSRS as the sensor platform for providing sensor informationWe have described path representation identification andprediction algorithms for path predictionThepresented pathprediction algorithm is based on CBP and takes into accounttime We evaluated the algorithm by implementing it in SRSand PP-SRS and compared the outputsWe also evaluated theprocessing time and accuracy of prediction between the CBP-PP algorithm and CBP-PP119905 algorithmThe evaluation showsthat CBP-PP119905 takes a longer processing time on averagethan CBP-PP which is attributed to the overhead of timeconsideration However the difference is slight On the otherhand CBP-PP119905 demonstrates significantly higher accuracyin prediction over CBP-PP

In the future we plan to implement SRS and evaluate theconnection performance with SRS We also plan to developa hybrid path prediction algorithm including CBP-basedand personalized approaches to improve the accuracy of theprediction

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgment

This research was supported by Basic Science ResearchProgram through the National Research Foundation ofKorea (NRF) funded by the Ministry of Education (NRF-2014R1A1A2058992)

References

[1] O Vermesan and P Friess Internet of Things ConvergingTechnologies for Smart Environments and Integrated EcosystemsRiver Publishers 2013

[2] L Atzori A Iera and G Morabito ldquoThe internet of things asurveyrdquoComputer Networks vol 54 no 15 pp 2787ndash2805 2010

[3] L Luo A Kansal S Nath and F Zhao ldquoSenseWeb sharing andbrowsing environmental changes in real timerdquo in Proceedings ofthe Microsoft eScience Workshop Microsoft Research Decem-ber 2008

[4] C Reed M Botts G Percivall and J Davidson ldquoOGC sensorweb enablement overview and high level architecturerdquo OGCWhite Paper Open Geospatial Consortium 2013

[5] S Nath J Liu and F Zhao ldquoSensorMap for wide-area sensorwebsrdquo Computer vol 40 no 7 pp 90ndash93 2007

[6] B L Gorman D R Resseguie and C Tomkins-Tinch ldquoSensor-pedia information sharing across incompatible sensor sys-temsrdquo in Proceedings of the International Symposium on Col-laborative Technologies and Systems (CTS rsquo09) pp 448ndash454Baltimore Md USA May 2009

[7] M Yuriyama and T Kushida ldquoSensor-cloud infrastructuremdashphysical sensor management with virtualized sensors on cloudcomputingrdquo in Proceedings of the 13th International Conferenceon Network-Based Information Systems (NBiS rsquo10) pp 1ndash8September 2010

[8] The European Unionrsquos Seventh Framework Programme ldquoOpenSource cloud solution for the Internet ofThingsrdquo httpopenioteu

[9] M Compton C Henson L Lefort H Neuhaus and A ShethldquoA survey of the semantic specification of sensorsrdquo inProceedingof the 2nd International Semantic Sensor Networks WorkshopInternational Workshop on Semantic Sensor Networks 2009 pp17ndash32 Washington DC USA October 2009

[10] A Sheth C Henson and S S Sahoo ldquoSemantic sensor webrdquoIEEE Internet Computing vol 12 no 4 pp 78ndash83 2008

[11] Y Shi G Li X Zhou and X Zhang ldquoSensor ontology buildingin semantic sensor webrdquo in Internet of Things vol 312 of Com-munications in Computer and Information Science pp 277ndash284Springer Berlin Germany 2012

[12] M Compton P Barnaghi L Bermudez et al ldquoThe SSN ontol-ogy of theW3C semantic sensor network incubator grouprdquoWebSemantics Science Services and Agents on the World Wide Webvol 17 pp 25ndash32 2012

[13] Digital Enterprise Research Institute Linked Sensor Middle-ware (LSM) httpscodegooglecompderi-lsm

[14] S Mayer D Guinard and V Trifa ldquoSearching in a web-based infrastructure for smart thingsrdquo in Proceedings of the 3rdInternational Conference on the Internet of Things (IOT rsquo12) pp119ndash126 IEEE Wuxi China October 2012

[15] C Perera A Zaslavsky C H Liu M Compton P Christenand D Georgakopoulos ldquoSensor search techniques for sensingas a service architecture for the internet of thingsrdquo IEEE SensorsJournal vol 14 no 2 pp 406ndash420 2014

[16] M Kohne and J Sieck ldquoLocation-based services with iBeacontechnologyrdquo in Proceedings of the 2nd International Conferenceon Artificial Intelligence Modeling and Simulation pp 315ndash321Novemeber 2014

[17] D Jeong ldquoFramework for seamless interpretation of semanticsin heterogeneous ubiquitous sensor networksrdquo InternationalJournal of Software Engineering amp Its Applications vol 6 no 3pp 9ndash16 2012

[18] EEUKCoverageChecker httpeecoukee-and-menetwork4geecoverage-checker

[19] D Jeong and J Ji ldquoA registration and management system forconsistently interpreting semantics of sensor information inheterogeneous sensor network environmentsrdquo Journal of KIISEDatabase vol 38 no 5 pp 289ndash302 2011

[20] ISOIEC JTC 1SC 32 ISOIEC 11179-32013mdashInformationTechnologymdashMetadata Registries (MDR)mdashPart 3 RegistryMetamodel and Basic Attributes 2013

[21] F Calabrese G Di Lorenzo and C Ratti ldquoHuman mobilityprediction based on individual and collective geographicalpreferencesrdquo in Proceedings of the 13th International IEEEConference on Intelligent Transportation Systems (ITSC rsquo10) pp312ndash317 Maderia Island Portugal September 2010

[22] Anacom ldquoGSM mobile networksmdashquality of service surveyrdquoAnacom Quality Report Anacom 2002

[23] N Samaan and A Karmouch ldquoA Mobility prediction archi-tecture based on contextual knowledge and spatial conceptualmapsrdquo IEEE Transactions onMobile Computing vol 4 no 6 pp537ndash551 2005


[24] L Chen M Lv Q Ye G Chen and J Woodward ldquoA personalroute prediction system based on trajectory data miningrdquoInformation Sciences vol 181 no 7 pp 1264ndash1284 2011

[25] J-M Kim H Baek and Y-T Park ldquoProbabilistic graphicalmodel based personal route prediction inmobile environmentrdquoAppliedMathematics amp Information Sciences vol 6 supplement2 pp 651Sndash659S 2012

[26] H Xiong D Zhang D Zhang and V Gauthier ldquoPredictingmobile phone user locations by exploiting collective behavioralpatternsrdquo in Proceedings of the 9th International Conferenceon Ubiquitous Intelligence amp Computing and 9th InternationalConference on Autonomic amp Trusted Computing (UICATC rsquo12)pp 164ndash171 IEEE Fukuoka Japan September 2012

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Active and Passive Electronic Components

Control Scienceand Engineering

Journal of



RotatingMachinery


Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design



Shock and Vibration


Civil EngineeringAdvances in

Acoustics and VibrationAdvances in



Electrical and Computer Engineering

Journal of

Advances inOptoElectronics


Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of


Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014


Chemical EngineeringInternational Journal of Antennas and

Propagation




Navigation and Observation



DistributedSensor Networks



The third issue is searching and filtering sensors Sen-sor filtering is based on searching specific sensors in theaforementioned platforms for registering sensors such asSensorCloud and OpenIoT Also sensor ontologies are usedto recognize user contexts and provide user-centric servicesSearching specific sensors especially in a large numberof entities requires effective sensor filtering technologiesLinked Sensor Middleware (LSM) is a web-based sensormanagement platform connected to the Semantic Web [13]LSMuses sensor information such as sensor type and locationfor searching sensors Mayer et al proposed a method forsearching sensor information by location using web-basedstructures of a building [14] Perera et al proposed a context-awareness sensor searching technique [15] Using SSNOthey search relevant sensors for the given context by settingweights of five features including accuracy reliability energyavailability and cost

The existing sensor searching techniques provide context-aware services including tracking user location Howeverthese techniques do not consider architectural problemscaused by real-time searching or efficiency of mobile net-works Considering applications with location-based servicessuch as iBeacon [16] the existing sensor searching techniquesare not sufficient for real-time reaction when the mobiledevice receives information from a sensor close to theuser Because the mobile device needs to receive sensorinformation whenever it is requested the mobile networkQoS influences services to be provided successfully to theuser

In this paper we present a path prediction approach foreffective sensor filtering This approach predicts user pathsbased on the user location and provides sensor informa-tion located nearby the predicted paths to a mobile devicebeforehand The path prediction approach can handle real-time situation or mobile network status The presentedmethod identifies the location of a user using predefinedroad information and predicts user paths using a simplepath prediction algorithm To implement the method weuse Sensor Registry System (SRS) [17] which registers andshares sensors For effective sensor searching the systemprovides sensor information by tracking the user predictinguser paths and identifying sensors located near the predictedpaths We also analyze user patterns using a time feature tovalidate prediction accuracy

The remainder of the paper is organized as followsSection 2 describes the problem being addressed in thispaper and gives an overview of the presented approach forsolving the problem Section 3 describes the path predictionmethod for SRS Section 4 presents the implementation andexperiment of the method and Section 5 gives an overview ofthe existing path prediction algorithms Section 6 evaluatesthe method in comparison to the existing work Section 7concludes the paper

2 Problem Definition and Solution Approach

This section discusses several problems in sensor filtering Asa solution for the problems we present a sensor registeringand sharing system Sensor Registry System (SRS) which is

able to search and filter sensors Then we extend SRS basedon path prediction to resolve the problems

21 Problem Definition The existing sensor filtering tech-niques are not suitable for real-time synchronization Sensorplatforms such as SensorCloud and OpenIoT have severalproblems when they have to support real-time sensor searchin the mobile computing environment One problem is thatthe performance of personalized sensor search is low becauseof limited mobile resources This mandates the sensor filter-ing process to be worked out on the server side instead ofthe mobile device The next problem is that a mobile deviceusing the sensor platforms has to allocate a large amount ofresources for requesting and receiving sensor informationThis problemmakes service process slowwhen the user wantsto provide some services Therefore the sensor platformsshould send sensor information before request and mobiledevice immediately uses the information needed Anotherproblem is that mobile devices are sensitively affected bythe mobile network QoS If a user moves into an unstablenetwork connection area the mobile device of the usercannot access sensor platforms and thus is not able to receivethe necessary sensor information Therefore the sensorfiltering process should recognize the user context predictneeded sensors for the user and receive sensor informationin advance

To detail the last problem Figure 1 shows an exampleof an unstable network connection status situation in theUK network coverage map provided by the EE coveragechecker [18] In Figure 1(a) the green area indicates the 4Gmobile network coverage the purple area indicates the 3Gmobile network coverage and the pink area indicates the 2Gmobile network coverage Figure 1(b) illustrates an exampleof success and failure in obtaining sensor information whena user moves into an unstable network connection areaSuppose that a user is moving and communicating with thesensor platform in real-time using a 4Gmobile device (whichis not supported in a 3G mobile communication system)When the usermoves along the blue arrow in the 4G coveragearea in Figure 1(b) the mobile device successfully connectsto the sensor platform and obtains the sensor informationHowever when the user moves into the 3G coverage areafollowing the red arrow the mobile device fails to obtain thesensor information around the user

This paper proposes a sensor filtering method that canprovide necessary sensors when a mobile device requiressensor information to the sensor platform in real-time whilepredicting the user paths and sending sensor informationnearby predicted paths to the mobile device using the sensorplatform

22 Path Prediction Approach This paper aims at improvingsensor filtering in Sensor Registry System (SRS) [17] whichis a sensor platform for registering and sharing sensors SRSwas proposed for semantic interoperability between sensorsand devices in a heterogeneous sensor network environment[19] Based on ISOIEC 11179 [20] SRS registers andmanagessensor information SRS also shares and provides metadataand sensor information such as locations units types and



(a)



s1

s2

s3

s4

s5

s6

s7

s8

s9

s10s11

s13

s12

s14s15

s16

s17s18

s19

s20

s21

s22

s23

s24

s25

s26


(b)










Userlocation

monitoringPath

predictionPath and

sensor matching

Sensor information

searchingSensor DB

Sensor information

set


Request

Return







Preprocessing









f1

f2

f3 f4 f5

f6

f7

cp1

cp2

up1up2

up3

up5

up4





119906is the



2rarr 119906119901

3rarr 119906119901

4rarr 119906119901

5and

119880119875119906= 119906119901



4= (119888119901

1 1198881199012))


119865 = 119891119889

119894| 1le 119894 le 119899 119889 isin 1 minus 1 (1)




4= 1198881199011rarr 119888119901

2and 119891

minus1

4=

1198881199012rarr 119888119901



of (1198911119894 119891minus1



119894)




to 1198911




4=

119891minus1

2 1198911

5 1198911


119862119865119889

119894= 119891119889

119895| 119891119889

119894= 119888119901119886997888rarr119888119901

119887 119891119889

119895= 119888119901119887

997888rarr119888119901119888 119891119889

119894 119891119889

119895 sube119865 119889 isin 1 minus 1

(2)



1 1199062 119906

119899


119880119875119906 1199061199011 1199061199012 119906119901

119899


1 1198881199012 119888119901

119899


119894= 1198911

119894 119891minus1119894

= (119888119901119886 119888119901119887)

119891119889


1198911

119894 119888119901119886rarr 119888119901

119887 119891minus1119894

= 119888119901119887rarr 119888119901

119886


1 1198911

2 119891minus1

2 119891

1

119899 119891minus1

119899

119862119865119889


119894


upf2

f1

f3

f4

p1

p2l1 l2

lpr

lh2

lh1lf1205791 1205792

cp3

cp1 cp2







of 119906119901 on 1198911and 119897119901119903



1 119897119891) and (119897

2 119897119891)



2


11989712minus 1198972

2+ 119897119891

2

2119897119891

)

2

(3)

where 11989712+ 119897119891

2ge 1198972

2 and 11989722+ 119897119891

2ge 1198971




1

2

+119897119891

2

ge 1198972

2 1198972

2

+119897119891

2

ge 1198971





ℎ1is shorter





119894119889119891119889

119906119901= arg min119891119894isin119865

dist (119891119894 119906119901) cap119862119865

119901119903119890V119891 (4)







119894is

defined as follows

119908119889


119894 (5)






119906) and


(119906119901) If 119894119889119891119889119906119901







119906119901to

119901119903119890V119891




119901119891119889

119894= arg max119891119889

119895isin119862119865119889

119894

119908119889

119895 (6)

119901119891119889








3 From




1198911



f11 (20)

fminus12 (30)

f14 (10)

fdi (wd

i )

f13 (15)

up1up2

up3

up4

up5

up6



1= 119891minus1

2 1198911

3 1198911

4which is the set



2has



119894)


119889

119894measured



119894) with respect to 119894119889119891119889

119906119901and



119894)




(10) 119901119891 larr 119888119891





2200sim0559



119894 119908119889119905119894 and 119901119891119889119905

119894in


119865 = 119891119889119905


119862119865119889119905

119894= 119891119889119905

119895| 119891119889

119894= 119888119901119886997888rarr119888119901

119887 119891119889

119895= 119888119901119887997888rarr119888119901

119888 119891119889

119895


119908119889119905

119894


at a time (119905)

119901119891119889119905

119894= arg max119891119889119905

119895isin119862119865119889119905

119894

119908119889119905

119895

(7)




119894)









(11) 119901119891 larr 119888119891










id

id

id

id

idChar(20) Char(20)

Char(20)


Char(20)

Char(20)

Char(20)

NN NN

NNNNNN

NN

NN

(PK) (PK)

(PK)

(PK)

(PK)

(FK)(FK)

(FK)

(FK)

direction IntIntInt

time

time

weight

cp1 cp2

cp1cp2

fragmentid

fragmentidlat

lat

lon

lonDoubleDouble

DoubleDouble



Datetime

userid

userid

CrossroadPoint

PathFragment

PathFragmentWeight

UserPoint

User


(a) (b) (c)



5 Evaluation





(a) (b)


(a) (b)











8993

6999

4006

9487

8026

5172

9687

8451

5663

98908868

6134

2030405060708090

100

10 30 60

Serv

ice r

eliab

ility

rate

()







(8)











6 Related Work



020406080

100

u001 u002 u003 u004 u005

38 40

13 825

45 40 38 4250

Accu

racy

()

User

CBP-PPCBP-PPt

(a)

020406080

100

u001 u002 u003 u004 u005

4570

3850

7589 90

7583

100

Accu

racy

()

User

CBP-PPCBP-PPt

(b)













7 Conclusion







Acknowledgment


References






























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation











(a)



s1

s2

s3

s4

s5

s6

s7

s8

s9

s10s11

s13

s12

s14s15

s16

s17s18

s19

s20

s21

s22

s23

s24

s25

s26


(b)










Userlocation

monitoringPath

predictionPath and

sensor matching

Sensor information

searchingSensor DB

Sensor information

set


Request

Return







Preprocessing









f1

f2

f3 f4 f5

f6

f7

cp1

cp2

up1up2

up3

up5

up4





119906is the



2rarr 119906119901

3rarr 119906119901

4rarr 119906119901

5and

119880119875119906= 119906119901



4= (119888119901

1 1198881199012))


119865 = 119891119889

119894| 1le 119894 le 119899 119889 isin 1 minus 1 (1)




4= 1198881199011rarr 119888119901

2and 119891

minus1

4=

1198881199012rarr 119888119901



of (1198911119894 119891minus1



119894)




to 1198911




4=

119891minus1

2 1198911

5 1198911


119862119865119889

119894= 119891119889

119895| 119891119889

119894= 119888119901119886997888rarr119888119901

119887 119891119889

119895= 119888119901119887

997888rarr119888119901119888 119891119889

119894 119891119889

119895 sube119865 119889 isin 1 minus 1

(2)



1 1199062 119906

119899


119880119875119906 1199061199011 1199061199012 119906119901

119899


1 1198881199012 119888119901

119899


119894= 1198911

119894 119891minus1119894

= (119888119901119886 119888119901119887)

119891119889


1198911

119894 119888119901119886rarr 119888119901

119887 119891minus1119894

= 119888119901119887rarr 119888119901

119886


1 1198911

2 119891minus1

2 119891

1

119899 119891minus1

119899

119862119865119889


119894


upf2

f1

f3

f4

p1

p2l1 l2

lpr

lh2

lh1lf1205791 1205792

cp3

cp1 cp2







of 119906119901 on 1198911and 119897119901119903



1 119897119891) and (119897

2 119897119891)



2


11989712minus 1198972

2+ 119897119891

2

2119897119891

)

2

(3)

where 11989712+ 119897119891

2ge 1198972

2 and 11989722+ 119897119891

2ge 1198971




1

2

+119897119891

2

ge 1198972

2 1198972

2

+119897119891

2

ge 1198971





ℎ1is shorter





119894119889119891119889

119906119901= arg min119891119894isin119865

dist (119891119894 119906119901) cap119862119865

119901119903119890V119891 (4)







119894is

defined as follows

119908119889


119894 (5)






119906) and


(119906119901) If 119894119889119891119889119906119901







119906119901to

119901119903119890V119891




119901119891119889

119894= arg max119891119889

119895isin119862119865119889

119894

119908119889

119895 (6)

119901119891119889








3 From




1198911



f11 (20)

fminus12 (30)

f14 (10)

fdi (wd

i )

f13 (15)

up1up2

up3

up4

up5

up6



1= 119891minus1

2 1198911

3 1198911

4which is the set



2has



119894)


119889

119894measured



119894) with respect to 119894119889119891119889

119906119901and



119894)




(10) 119901119891 larr 119888119891





2200sim0559



119894 119908119889119905119894 and 119901119891119889119905

119894in


119865 = 119891119889119905


119862119865119889119905

119894= 119891119889119905

119895| 119891119889

119894= 119888119901119886997888rarr119888119901

119887 119891119889

119895= 119888119901119887997888rarr119888119901

119888 119891119889

119895


119908119889119905

119894


at a time (119905)

119901119891119889119905

119894= arg max119891119889119905

119895isin119862119865119889119905

119894

119908119889119905

119895

(7)




119894)









(11) 119901119891 larr 119888119891










id

id

id

id

idChar(20) Char(20)

Char(20)


Char(20)

Char(20)

Char(20)

NN NN

NNNNNN

NN

NN

(PK) (PK)

(PK)

(PK)

(PK)

(FK)(FK)

(FK)

(FK)

direction IntIntInt

time

time

weight

cp1 cp2

cp1cp2

fragmentid

fragmentidlat

lat

lon

lonDoubleDouble

DoubleDouble



Datetime

userid

userid

CrossroadPoint

PathFragment

PathFragmentWeight

UserPoint

User


(a) (b) (c)



5 Evaluation





(a) (b)


(a) (b)











8993

6999

4006

9487

8026

5172

9687

8451

5663

98908868

6134

2030405060708090

100

10 30 60

Serv

ice r

eliab

ility

rate

()







(8)











6 Related Work



020406080

100

u001 u002 u003 u004 u005

38 40

13 825

45 40 38 4250

Accu

racy

()

User

CBP-PPCBP-PPt

(a)

020406080

100

u001 u002 u003 u004 u005

4570

3850

7589 90

7583

100

Accu

racy

()

User

CBP-PPCBP-PPt

(b)













7 Conclusion







Acknowledgment


References






























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










Preprocessing









f1

f2

f3 f4 f5

f6

f7

cp1

cp2

up1up2

up3

up5

up4





119906is the



2rarr 119906119901

3rarr 119906119901

4rarr 119906119901

5and

119880119875119906= 119906119901



4= (119888119901

1 1198881199012))


119865 = 119891119889

119894| 1le 119894 le 119899 119889 isin 1 minus 1 (1)




4= 1198881199011rarr 119888119901

2and 119891

minus1

4=

1198881199012rarr 119888119901



of (1198911119894 119891minus1



119894)




to 1198911




4=

119891minus1

2 1198911

5 1198911


119862119865119889

119894= 119891119889

119895| 119891119889

119894= 119888119901119886997888rarr119888119901

119887 119891119889

119895= 119888119901119887

997888rarr119888119901119888 119891119889

119894 119891119889

119895 sube119865 119889 isin 1 minus 1

(2)



1 1199062 119906

119899


119880119875119906 1199061199011 1199061199012 119906119901

119899


1 1198881199012 119888119901

119899


119894= 1198911

119894 119891minus1119894

= (119888119901119886 119888119901119887)

119891119889


1198911

119894 119888119901119886rarr 119888119901

119887 119891minus1119894

= 119888119901119887rarr 119888119901

119886


1 1198911

2 119891minus1

2 119891

1

119899 119891minus1

119899

119862119865119889


119894


upf2

f1

f3

f4

p1

p2l1 l2

lpr

lh2

lh1lf1205791 1205792

cp3

cp1 cp2







of 119906119901 on 1198911and 119897119901119903



1 119897119891) and (119897

2 119897119891)



2


11989712minus 1198972

2+ 119897119891

2

2119897119891

)

2

(3)

where 11989712+ 119897119891

2ge 1198972

2 and 11989722+ 119897119891

2ge 1198971




1

2

+119897119891

2

ge 1198972

2 1198972

2

+119897119891

2

ge 1198971





ℎ1is shorter





119894119889119891119889

119906119901= arg min119891119894isin119865

dist (119891119894 119906119901) cap119862119865

119901119903119890V119891 (4)







119894is

defined as follows

119908119889


119894 (5)






119906) and


(119906119901) If 119894119889119891119889119906119901







119906119901to

119901119903119890V119891




119901119891119889

119894= arg max119891119889

119895isin119862119865119889

119894

119908119889

119895 (6)

119901119891119889








3 From




1198911



f11 (20)

fminus12 (30)

f14 (10)

fdi (wd

i )

f13 (15)

up1up2

up3

up4

up5

up6



1= 119891minus1

2 1198911

3 1198911

4which is the set



2has



119894)


119889

119894measured



119894) with respect to 119894119889119891119889

119906119901and



119894)




(10) 119901119891 larr 119888119891





2200sim0559



119894 119908119889119905119894 and 119901119891119889119905

119894in


119865 = 119891119889119905


119862119865119889119905

119894= 119891119889119905

119895| 119891119889

119894= 119888119901119886997888rarr119888119901

119887 119891119889

119895= 119888119901119887997888rarr119888119901

119888 119891119889

119895


119908119889119905

119894


at a time (119905)

119901119891119889119905

119894= arg max119891119889119905

119895isin119862119865119889119905

119894

119908119889119905

119895

(7)




119894)









(11) 119901119891 larr 119888119891










id

id

id

id

idChar(20) Char(20)

Char(20)


Char(20)

Char(20)

Char(20)

NN NN

NNNNNN

NN

NN

(PK) (PK)

(PK)

(PK)

(PK)

(FK)(FK)

(FK)

(FK)

direction IntIntInt

time

time

weight

cp1 cp2

cp1cp2

fragmentid

fragmentidlat

lat

lon

lonDoubleDouble

DoubleDouble



Datetime

userid

userid

CrossroadPoint

PathFragment

PathFragmentWeight

UserPoint

User


(a) (b) (c)



5 Evaluation





(a) (b)


(a) (b)











8993

6999

4006

9487

8026

5172

9687

8451

5663

98908868

6134

2030405060708090

100

10 30 60

Serv

ice r

eliab

ility

rate

()







(8)











6 Related Work



020406080

100

u001 u002 u003 u004 u005

38 40

13 825

45 40 38 4250

Accu

racy

()

User

CBP-PPCBP-PPt

(a)

020406080

100

u001 u002 u003 u004 u005

4570

3850

7589 90

7583

100

Accu

racy

()

User

CBP-PPCBP-PPt

(b)













7 Conclusion







Acknowledgment


References






























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










upf2

f1

f3

f4

p1

p2l1 l2

lpr

lh2

lh1lf1205791 1205792

cp3

cp1 cp2







of 119906119901 on 1198911and 119897119901119903



1 119897119891) and (119897

2 119897119891)



2


11989712minus 1198972

2+ 119897119891

2

2119897119891

)

2

(3)

where 11989712+ 119897119891

2ge 1198972

2 and 11989722+ 119897119891

2ge 1198971




1

2

+119897119891

2

ge 1198972

2 1198972

2

+119897119891

2

ge 1198971





ℎ1is shorter





119894119889119891119889

119906119901= arg min119891119894isin119865

dist (119891119894 119906119901) cap119862119865

119901119903119890V119891 (4)







119894is

defined as follows

119908119889


119894 (5)






119906) and


(119906119901) If 119894119889119891119889119906119901







119906119901to

119901119903119890V119891




119901119891119889

119894= arg max119891119889

119895isin119862119865119889

119894

119908119889

119895 (6)

119901119891119889








3 From




1198911



f11 (20)

fminus12 (30)

f14 (10)

fdi (wd

i )

f13 (15)

up1up2

up3

up4

up5

up6



1= 119891minus1

2 1198911

3 1198911

4which is the set



2has



119894)


119889

119894measured



119894) with respect to 119894119889119891119889

119906119901and



119894)




(10) 119901119891 larr 119888119891





2200sim0559



119894 119908119889119905119894 and 119901119891119889119905

119894in


119865 = 119891119889119905


119862119865119889119905

119894= 119891119889119905

119895| 119891119889

119894= 119888119901119886997888rarr119888119901

119887 119891119889

119895= 119888119901119887997888rarr119888119901

119888 119891119889

119895


119908119889119905

119894


at a time (119905)

119901119891119889119905

119894= arg max119891119889119905

119895isin119862119865119889119905

119894

119908119889119905

119895

(7)




119894)









(11) 119901119891 larr 119888119891










id

id

id

id

idChar(20) Char(20)

Char(20)


Char(20)

Char(20)

Char(20)

NN NN

NNNNNN

NN

NN

(PK) (PK)

(PK)

(PK)

(PK)

(FK)(FK)

(FK)

(FK)

direction IntIntInt

time

time

weight

cp1 cp2

cp1cp2

fragmentid

fragmentidlat

lat

lon

lonDoubleDouble

DoubleDouble



Datetime

userid

userid

CrossroadPoint

PathFragment

PathFragmentWeight

UserPoint

User


(a) (b) (c)



5 Evaluation





(a) (b)


(a) (b)











8993

6999

4006

9487

8026

5172

9687

8451

5663

98908868

6134

2030405060708090

100

10 30 60

Serv

ice r

eliab

ility

rate

()







(8)











6 Related Work



020406080

100

u001 u002 u003 u004 u005

38 40

13 825

45 40 38 4250

Accu

racy

()

User

CBP-PPCBP-PPt

(a)

020406080

100

u001 u002 u003 u004 u005

4570

3850

7589 90

7583

100

Accu

racy

()

User

CBP-PPCBP-PPt

(b)













7 Conclusion







Acknowledgment


References






























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation














119906119901to

119901119903119890V119891




119901119891119889

119894= arg max119891119889

119895isin119862119865119889

119894

119908119889

119895 (6)

119901119891119889








3 From




1198911



f11 (20)

fminus12 (30)

f14 (10)

fdi (wd

i )

f13 (15)

up1up2

up3

up4

up5

up6



1= 119891minus1

2 1198911

3 1198911

4which is the set



2has



119894)


119889

119894measured



119894) with respect to 119894119889119891119889

119906119901and



119894)




(10) 119901119891 larr 119888119891





2200sim0559



119894 119908119889119905119894 and 119901119891119889119905

119894in


119865 = 119891119889119905


119862119865119889119905

119894= 119891119889119905

119895| 119891119889

119894= 119888119901119886997888rarr119888119901

119887 119891119889

119895= 119888119901119887997888rarr119888119901

119888 119891119889

119895


119908119889119905

119894


at a time (119905)

119901119891119889119905

119894= arg max119891119889119905

119895isin119862119865119889119905

119894

119908119889119905

119895

(7)




119894)









(11) 119901119891 larr 119888119891










id

id

id

id

idChar(20) Char(20)

Char(20)


Char(20)

Char(20)

Char(20)

NN NN

NNNNNN

NN

NN

(PK) (PK)

(PK)

(PK)

(PK)

(FK)(FK)

(FK)

(FK)

direction IntIntInt

time

time

weight

cp1 cp2

cp1cp2

fragmentid

fragmentidlat

lat

lon

lonDoubleDouble

DoubleDouble



Datetime

userid

userid

CrossroadPoint

PathFragment

PathFragmentWeight

UserPoint

User


(a) (b) (c)



5 Evaluation





(a) (b)


(a) (b)











8993

6999

4006

9487

8026

5172

9687

8451

5663

98908868

6134

2030405060708090

100

10 30 60

Serv

ice r

eliab

ility

rate

()







(8)











6 Related Work



020406080

100

u001 u002 u003 u004 u005

38 40

13 825

45 40 38 4250

Accu

racy

()

User

CBP-PPCBP-PPt

(a)

020406080

100

u001 u002 u003 u004 u005

4570

3850

7589 90

7583

100

Accu

racy

()

User

CBP-PPCBP-PPt

(b)













7 Conclusion







Acknowledgment


References






























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation











(10) 119901119891 larr 119888119891





2200sim0559



119894 119908119889119905119894 and 119901119891119889119905

119894in


119865 = 119891119889119905


119862119865119889119905

119894= 119891119889119905

119895| 119891119889

119894= 119888119901119886997888rarr119888119901

119887 119891119889

119895= 119888119901119887997888rarr119888119901

119888 119891119889

119895


119908119889119905

119894


at a time (119905)

119901119891119889119905

119894= arg max119891119889119905

119895isin119862119865119889119905

119894

119908119889119905

119895

(7)




119894)









(11) 119901119891 larr 119888119891










id

id

id

id

idChar(20) Char(20)

Char(20)


Char(20)

Char(20)

Char(20)

NN NN

NNNNNN

NN

NN

(PK) (PK)

(PK)

(PK)

(PK)

(FK)(FK)

(FK)

(FK)

direction IntIntInt

time

time

weight

cp1 cp2

cp1cp2

fragmentid

fragmentidlat

lat

lon

lonDoubleDouble

DoubleDouble



Datetime

userid

userid

CrossroadPoint

PathFragment

PathFragmentWeight

UserPoint

User


(a) (b) (c)



5 Evaluation





(a) (b)


(a) (b)











8993

6999

4006

9487

8026

5172

9687

8451

5663

98908868

6134

2030405060708090

100

10 30 60

Serv

ice r

eliab

ility

rate

()







(8)











6 Related Work



020406080

100

u001 u002 u003 u004 u005

38 40

13 825

45 40 38 4250

Accu

racy

()

User

CBP-PPCBP-PPt

(a)

020406080

100

u001 u002 u003 u004 u005

4570

3850

7589 90

7583

100

Accu

racy

()

User

CBP-PPCBP-PPt

(b)













7 Conclusion







Acknowledgment


References






























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation













(11) 119901119891 larr 119888119891










id

id

id

id

idChar(20) Char(20)

Char(20)


Char(20)

Char(20)

Char(20)

NN NN

NNNNNN

NN

NN

(PK) (PK)

(PK)

(PK)

(PK)

(FK)(FK)

(FK)

(FK)

direction IntIntInt

time

time

weight

cp1 cp2

cp1cp2

fragmentid

fragmentidlat

lat

lon

lonDoubleDouble

DoubleDouble



Datetime

userid

userid

CrossroadPoint

PathFragment

PathFragmentWeight

UserPoint

User


(a) (b) (c)



5 Evaluation





(a) (b)


(a) (b)











8993

6999

4006

9487

8026

5172

9687

8451

5663

98908868

6134

2030405060708090

100

10 30 60

Serv

ice r

eliab

ility

rate

()







(8)











6 Related Work



020406080

100

u001 u002 u003 u004 u005

38 40

13 825

45 40 38 4250

Accu

racy

()

User

CBP-PPCBP-PPt

(a)

020406080

100

u001 u002 u003 u004 u005

4570

3850

7589 90

7583

100

Accu

racy

()

User

CBP-PPCBP-PPt

(b)













7 Conclusion







Acknowledgment


References






























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










id

id

id

id

idChar(20) Char(20)

Char(20)


Char(20)

Char(20)

Char(20)

NN NN

NNNNNN

NN

NN

(PK) (PK)

(PK)

(PK)

(PK)

(FK)(FK)

(FK)

(FK)

direction IntIntInt

time

time

weight

cp1 cp2

cp1cp2

fragmentid

fragmentidlat

lat

lon

lonDoubleDouble

DoubleDouble



Datetime

userid

userid

CrossroadPoint

PathFragment

PathFragmentWeight

UserPoint

User


(a) (b) (c)



5 Evaluation





(a) (b)


(a) (b)











8993

6999

4006

9487

8026

5172

9687

8451

5663

98908868

6134

2030405060708090

100

10 30 60

Serv

ice r

eliab

ility

rate

()







(8)











6 Related Work



020406080

100

u001 u002 u003 u004 u005

38 40

13 825

45 40 38 4250

Accu

racy

()

User

CBP-PPCBP-PPt

(a)

020406080

100

u001 u002 u003 u004 u005

4570

3850

7589 90

7583

100

Accu

racy

()

User

CBP-PPCBP-PPt

(b)













7 Conclusion







Acknowledgment


References






























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










(a) (b)


(a) (b)











8993

6999

4006

9487

8026

5172

9687

8451

5663

98908868

6134

2030405060708090

100

10 30 60

Serv

ice r

eliab

ility

rate

()







(8)











6 Related Work



020406080

100

u001 u002 u003 u004 u005

38 40

13 825

45 40 38 4250

Accu

racy

()

User

CBP-PPCBP-PPt

(a)

020406080

100

u001 u002 u003 u004 u005

4570

3850

7589 90

7583

100

Accu

racy

()

User

CBP-PPCBP-PPt

(b)













7 Conclusion







Acknowledgment


References






























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










8993

6999

4006

9487

8026

5172

9687

8451

5663

98908868

6134

2030405060708090

100

10 30 60

Serv

ice r

eliab

ility

rate

()







(8)











6 Related Work



020406080

100

u001 u002 u003 u004 u005

38 40

13 825

45 40 38 4250

Accu

racy

()

User

CBP-PPCBP-PPt

(a)

020406080

100

u001 u002 u003 u004 u005

4570

3850

7589 90

7583

100

Accu

racy

()

User

CBP-PPCBP-PPt

(b)













7 Conclusion







Acknowledgment


References






























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










020406080

100

u001 u002 u003 u004 u005

38 40

13 825

45 40 38 4250

Accu

racy

()

User

CBP-PPCBP-PPt

(a)

020406080

100

u001 u002 u003 u004 u005

4570

3850

7589 90

7583

100

Accu

racy

()

User

CBP-PPCBP-PPt

(b)













7 Conclusion







Acknowledgment


References






























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation














Acknowledgment


References






























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation















RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation









research article path prediction method for effective sensor...

Documents