Big Data Analytics for Sensor-Network Collected Intelligence. A volume in Intelligent Data-Centric Systems

307 Pages·2017·17.34 MB·English

by  Hui-Huang Hsu

Big Data Analytics for
Sensor-Network Collected
Intelligence
Big Data Analytics for
Sensor-Network Collected
Intelligence
Edited by
Hui-Huang Hsu
Tamkang University, Taiwan
Chuan-Yu Chang
NationalYunlin University ofScience and Technology, Taiwan
Ching-Hsien Hsu
Chung Hua University, Taiwan
Series Editor Fatos Xhafa
Universitat Polite`cnica de Catalunya, Spain
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List of Contributors
Ahmad Anbar
The GeorgeWashingtonUniversity, Washington, DC, United States
Haytham Assem
IBM, Dublin, Ireland
Christophe Blanchet
CNRS IFB, Orsay, France
Teodora S.Buda
IBM, Dublin, Ireland
JiannongCao
The Hong Kong PolytechnicUniversity, Kowloon, Hong Kong
Chuan-YuChang
NationalYunlinUniversity of Science andTechnology, DouliuCity, Yunlin County, Taiwan
JinjunChen
University ofTechnology Sydney, Broadway, NSW,Australia
Cen Chen
Hunan University, Changsha, China
Szu-Ta Chen
NationalTaiwanUniversity HospitalYun-Lin Branch, Douliu City, Yunlin County, Taiwan
KangChen
Southern Illinois University, Carbondale,IL, United States
ZixueCheng
University ofAizu, Aizuwakamatsu,Japan
Ceesde Laat
University ofAmsterdam, Amsterdam, The Netherlands
Yuri Demchenko
University ofAmsterdam, Amsterdam, The Netherlands
Mingxing Duan
Hunan University, Changsha, China
Tarek El-Ghazawi
The GeorgeWashingtonUniversity, Washington, DC, United States
WeiweiW. Fang
BeijingKey Lab ofTransportation DataAnalysis and Mining, Beijing Jiaotong
University, Beijing, China
Edmond J. Golden III
NationalInstituteofStandards and Technology, Gaithersburg,MD, UnitedStates
Chu-Cheng Hsieh
SliceTechnologies Inc.,San Mateo,CA, United States
xiii
xiv List of Contributors
Ching-Hsien Hsu
Chung HuaUniversity, Hsinchu,Taiwan
Hui-Huang Hsu
Tamkang University, Tamsui, Taiwan
Qian Huang
Southern Illinois University, Carbondale,IL, UnitedStates
Tian-Hsiang Huang
NationalSunYat-sen University, Kaohsiung,Taiwan
Chih-Chieh Hung
Tamkng University, NewTaipei City, Taiwan
Pravin Kakar
Institute for InfocommResearch, Agency for Science,Technology and
Research (A*STAR), Singapore
ShonaliKrishnaswamy
Institute for InfocommResearch, Agency for Science,Technology and
Research (A*STAR), Singapore
Chung-Nan Lee
NationalSunYat-sen University, Kaohsiung,Taiwan
Kenli Li
Hunan University, Changsha,China
Keqin Li
Hunan University, Changsha, China; State University of New York, New Paltz,
NY, United States
Xiao-Li Li
Institute for InfocommResearch, Agency for Science,Technology and
Research (A*STAR), Singapore
QingyongY. Li
Beijing Key Lab ofTransportation Data Analysis and Mining, Beijing Jiaotong
University, Beijing, China
Hai-NingLiang
Xi’an Jiaotong-Liverpool University, Suzhou, China
Chen Lin
NationalYunlin University ofScience and Technology, Douliu City, Yunlin County,Taiwan
Xuefeng Liu
The Hong Kong PolytechnicUniversity, Kowloon, Hong Kong
Ming Liu
Beijing Key Lab ofTransportation Data Analysis and Mining, Beijing Jiaotong
University, Beijing, China
CharlesLoomis
SixSq Sàrl,Geneva, Switzerland
Chao Lu
Southern Illinois University, Carbondale,IL, UnitedStates
List of Contributors xv
Ka L. Man
Xi’anJiaotong-Liverpool University, Suzhou,China
Martial Michel
NationalInstituteofStandards and Technology, Gaithersburg,MD, UnitedStates
Vijayakumar Nanjappan
Xi’anJiaotong-Liverpool University, Suzhou,China
Minh N. Nguyen
Institutefor InfocommResearch, Agency for Science,Technology and
Research(A*STAR), Singapore
DeclanO’Sullivan
TrinityCollege Dublin, Dublin, Ireland
Phyo P. San
Institutefor InfocommResearch, Agency for Science,Technology and
Research(A*STAR), Singapore
OlivierSerres
The GeorgeWashingtonUniversity, Washington, DC, United States
Kathiravan Srinivasan
NationalIlan University, YilanCity, Yilan County,Taiwan
Ming-Chun Tsai
NationalSun Yat-sen University, Kaohsiung,Taiwan
Fatih Turkmen
University ofAmsterdam, Amsterdam, The Netherlands
Wei Wang
Xi’anJiaotong-Liverpool University, Suzhou,China
Junbo Wang
University ofAizu, Aizuwakamatsu,Japan
YilangWu
University ofAizu, Aizuwakamatsu,Japan
Chen-Ming Wu
NationalSun Yat-sen University, Kaohsiung,Taiwan
Lei Xu
IBM, Dublin, Ireland
Chi Yang
University ofTechnology Sydney, Broadway, NSW,Australia
Jian-BoYang
Institutefor InfocommResearch, Agency for Science,Technology and
Research(A*STAR), Singapore
ZhangduiD.Zhong
BeijingKey Lab ofTransportation DataAnalysis and Mining, Beijing Jiaotong
University, Beijing, China
Preface
Therearethreesourcesofinformationwecancollectabouttheenvironmentandthepeopleintheen-
vironment:environmentalsensors,wearablesensors,andsocialnetworks.Throughintelligentanalysis
ofthehugeamountofsensorydata,wecandevelopvarioussystemstoautomaticallydetectnaturaland
man-madeevents.Moreover,thesystemscanalsotrytounderstandpeople’sbehaviorandeveninten-
tion. Thus better servicescan be provided topeople inan unobtrusivemanner.
Withtheadvancesinsensorandnetworkingtechnologies,wearenowabletocollectsensorydata
easily. These sensory data can be stored and processed in the cloud. Nevertheless, how to properly
utilize such a huge amount of data is another essential issue. We certainly hope that advanced ICT
technologiescanhelpusperformintelligentanalysisonthesedataandprovidebetterservicestopeople
automatically.Excitingnewsystemsandresearchresultshavebeendeveloped.Thisbookaimstoin-
troducetheseambientintelligenceandInternetofThings(IoT)systems,whicharebasedonbigdata
analytics ofcollected sensorydata.
Thethemeofthisbookiscloselyrelatedtotwohottopics:theInternetofThingsandbigdataan-
alytics.Systemsandtechnologiesintroducedinthebookcanbeusedassupplementarymaterialsfor
coursesinvolvingthesetwotopics.Researchers,professionals,andpractitionersinrelatedfieldscan
alsofindusefulinformationandtechnologiesfortheirwork.Therearefourpartsofthisbook:bigdata
architectureandplatforms;bigdataprocessingandmanagement;bigdataanalyticsandservices;and
big data intelligence and IoT systems. Each part includes threeor four chapters. Here we briefly in-
troduce each of the 14 chapters.
PartI:Big Data Architectureand Platforms
1. BigData:AClassificationof Acquisition and GenerationMethods
Vijayakumar Nanjappan, Hai-NingLiang, Wei Wang, Ka L.Man
Thischapterpointsoutthatitisverydifficulttostore,process,andanalyzehugeamountsofdata
using conventional computingmethodologies and resources.The authorsclassify the data into
digitalandanalog,environmentalandpersonal.Datatypesandformatsaswellasinputmechanisms
arealsohighlighted.Thesewillhelpusunderstandtheactiveandpassivemethodsofdatacollection
andproduction.
2. Cloud Computing Infrastructure for Data Intensive Applications
Yuri Demchenko, Fatih Turkmen, Cees de Laat, Ching-Hsien Hsu, Christophe Blanchet,
CharlesLoomis
This chapter proposes acloud-based bigdata infrastructure(BDI). Thegeneral architecture and
functionalcomponentsofBDIaredescribedindetail.BDIissupportedbythedefinitionofthebig
data architecture framework (BDAF).Two case studiesin bioinformatics are illustrated inthe
chapter to provide examples ofrequirements analysis and implementation.
3. Open Source PrivateCloud Platforms for Big Data
MartialMichel, Olivier Serres, Ahmad Anbar, Edmond J.Golden III, Tarek El-Ghazawi
Thischaptertellsusthatitisbeneficialtouseprivateclouds,especiallyopensourceclouds,for
bigdata.Security,privacy,andcustomizationarethemajorconcerns.Thechapterintroducesthemost
prominentopensourcecloudsinviewofbigdataprocessing.AcasestudyusinganOn-Premise
PrivateCloudisalsopresentedtodemonstratetheimplementationofsuchanenvironment.
xvii
xviii Preface
Part II: BigDataProcessing and Management
4. Efficient NonlinearRegression-Based Compression of BigSensing Data on Cloud
ChiYang,Jinjun Chen
Thischapterproposesacompressionmethodforbigsensingdatabasedonanonlinearregression
model. It improves the effectiveness and efficiencyfor processing real-worldbigsensingdata.
Regressiondesign, least squares, andtriangular transform are discussedin thischapter. It is
demonstratedthat the model achieves significant storage and time performance gains over other
compression models.
5. BigDataManagement onWireless Sensor Networks
Chih-Chieh Hung, Chu-Cheng Hsieh
This chapter gives anoverviewof data management issues andsolutionsin wireless sensor
networks. There are two possible models:centralized and decentralized. Data management can
becentralized for the benefit of computation,or decentralized for energysaving.Three major
issues for data management in both models are introduced: storage, query processing, and data
collection. Some case studies are also discussed.
6. Extreme Learning Machine and Its Applications in BigData Processing
Cen Chen, Kenli Li,Mingxing Duan, Keqin Li
Thischapterfirstreviewstheextremelearningmachine(ELM)theoryanditsvariants.Duetoits
memory-residency and high space/time complexity,the traditional ELM cannot trainbigdata
efficiently.Optimization strategiesare necessary tosolvethis problem. Thus, parallel ELM
algorithms based on MapReduce andSpark are described.Finally,practical applications ofthe
ELM for bigdata are also presented inthis chapter.
Part III: BigData Analyticsand Services
7. Spatial BigData Analyticsfor Cellular Communication Systems
JunboWang, Yilang Wu,Hui-Huang Hsu, Zixue Cheng
This chapter surveys methodologies of spatial bigdata analytics andpossible applicationsto
supportthecellularcommunication(CC)system.TheCCsystemprovidesthemostpopularwayto
connect people. However,it stillfaceschallenges, such asunbalanced crowd communication
behaviorandvideotransmissioncongestion.SpatialbigdataanalyticscanhelptheCCsystemto
provide services with better quality ofservice (QoS).Challenging issues are highlighted inthis
chapter.
8. Cognitive Applications and Their SupportingArchitecture for Smart Cities
Haytham Assem, Lei Xu,Teodora S. Buda, Declan O’Sullivan
This chapter proposes acognitivearchitecture to enable bigdata applicationswith sensory
data for smart cities. It deals with organization, configuration, security,and optimization. This
chapter also reviewsrelatedworkon location-based social networksand presentsa novel
approachtodetecturbanpatterns,especiallyanomalies.Thisisessentialforbetterunderstanding
ofhumanactivities and behaviors.
9. Deep Learning for Human Activity Recognition
PhyoP. San, PravinKakar,Xiao-LiLi, Shonali Krishnaswamy, Jian-Bo Yang, Minh N. Nguyen
This chapter presents a systematic feature learning method for the problem ofhumanactivity
recognition(HAR). It adopts a deep convolutional neural network (CNN) toautomate feature
learning from rawinputs.It isnotnecessary tohandcraft features inadvance.Sucha
Preface xix
unification offeature learning andclassification results inmutual enhancements.Thisis
verified by comparing experimental results with several state-of-the-art techniques.
10. Neonatal Cry Analysisand Categorization SystemVia DirectedAcyclic Graph Support
Vector Machine
Szu-Ta Chen, Kathiravan Srinivasan,ChenLin, Chuan-Yu Chang
This chapterintroduces a neonatal cry analysis andcategorizationsystem. From the cry of
the newborn,the system can identifydifferent types offeelingssuch aspain,sleepiness, and
hunger.Thesequential forwardfloatingselection (SFFS)algorithm isused tochoose the
discriminative features.Theselectedfeaturesare then used toclassifythe neonatal cries by
the directedacyclic graph supportvector machine (DAG-SVM). The system isusefulfor
parentsandnursing staff.
PartIV: BigDataIntelligence and IoT Systems
11. Smart Building Applications and InformationSystemHardware Co-Design
Qian Huang,Chao Lu,Kang Chen
This chapteremphasizes that acomprehensiveunderstandingofinformationsystem hardware
isnecessary when designing efficient smart building applications. The necessityand
importance ofapplication andhardware co-designare discussed inthis chapter. Acase study
isalso givento show thatapplication and hardware co-design optimize the smart building
design from asystem perspective.
12. Smart SensorNetworksfor Building Safety
XuefengLiu, Jiannong Cao
Thischapterpresentsthedesignandimplementationofeffectiveandenergy-efficientstructural
health monitoring(SHM)algorithmsin resource-limited wireless sensornetworks(WSNs).
Comparedtotraditionalwiredtransmission,WSNsarelowcostandeasytodeployforbuilding
monitoring.DistributedversionsofSHMalgorithmscanhelpovercomethebandwidthlimitation.
AWSN-Cloudsystem architecture isalso proposedfor future SHM.
13. The Internetof Things and Its Applications
Chung-NanLee, Tian-Hsiang Huang, Chen-Ming Wu,Ming-Chun Tsai
This chapterfirst comparestwolightweightprotocolsfor the Internet ofThings (IoT):MQ
telemetrytransport(MQTT)and the constrained applicationprotocol (CoAP).Both protocols
reducethe size of the packet andthe over-loading of the bandwidth,thus saving battery power
and storage space.Themajor techniquesfor bigdataanalytics are then introduced. Finally,
intelligent transportation systems and intelligent manufacturing systems are presented as
examples.
14. Smart Railway Basedon the Internet ofThings
Qingyong Y. Li,Zhangdui D.Zhong,MingLiu, Weiwei W. Fang
This chapterdiscussesthe frameworkand technologies for a smart railway based on Internet
ofThings (IoT) and bigdata. The architecture ofa smart railway, including the perception
and action layer,the transferlayer, the data engine layer, andthe application layer, is
presented first. Acase study on intelligent rail inspectionisthen introduced. This chapter
showsthat asmart railway ispromisingin improvingtraditional railway systems.
xx Preface
ACKNOWLEDGMENTS
Thisbookisapartofthebookseries“IntelligentData-CentricSystems.”Firstofall,wewouldliketo
thanktheserieseditor,Prof.FatosXhafa,forhisencouragementandguidanceindevelopingthisbook.
Wegratefullyacknowledgeallthecontributingauthorsofthechapters.Thisbookwouldnothavebeen
possiblewithouttheirgreatefforts.WearealsoindebtedtoMs.AnaClaudiaGarcia,theeditorialpro-
ject manager, and the whole production team at Elsevier for their continuous help in producing this
book. Finally,we thank ourfamiliesfor their love and support.
Hui-Huang Hsu, Chuan-YuChang,Ching-Hsien Hsu
September 2016