International Journal of Distributed Sensor Networks

Wireless Sensor Networks for Structural Health Monitoring

Guest Editors: Yujin Lim, Gianluigi Ferrari, Hideyuki Takahashi, and Màrius Montón

Wireless Sensor Networks for StructuralHealth Monitoring

International Journal of Distributed Sensor Networks

Wireless Sensor Networks for StructuralHealth Monitoring

Guest Editors: Yujin Lim, Gianluigi Ferrari,Hideyuki Takahashi, and Màrius Montón

Copyright © 2015 Hindawi Publishing Corporation. All rights reserved.

This is a special issue published in “International Journal of Distributed Sensor Networks.” All articles are open access articles distributedunder the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, pro-vided the original work is properly cited.

Editorial Board

Jemal H. Abawajy, AustraliaMiguel Acevedo, USACristina Alcaraz, SpainAna Alejos, SpainMohammod Ali, USAGiuseppe Amato, ItalyHabib M. Ammari, USAMichele Amoretti, ItalyChristos Anagnostopoulos, UKLi-Minn Ang, AustraliaNabil Aouf, UKFrancesco Archetti, ItalyMasoud Ardakani, CanadaMiguel Ardid, SpainMuhammad Asim, UKStefano Avallone, ItalyJose L. Ayala, SpainJavier Bajo, SpainN. Balakrishnan, IndiaPrabir Barooah, USAFederico Barrero, SpainPaolo Barsocchi, ItalyPaolo Bellavista, ItalyOlivier Berder, FranceRoc Berenguer, SpainJuan A. Besada, SpainGennaro Boggia, ItalyAlessandro Bogliolo, ItalyEleonora Borgia, ItalyJanos Botzheim, JapanFarid Boussaid, AustraliaArnold K. Bregt, The NetherlandsRob Brennan, CanadaRichard R. Brooks, USATed Brown, USADavide Brunelli, ItalyJames Brusey, UKCarlos T. Calafate, SpainTiziana Calamoneri, ItalyJosé Camacho, SpainJuan Carlos Cano, SpainXianghui Cao, USAJoão Paulo Carmo, BrazilRoberto Casas, SpainLuca Catarinucci, Italy

Michelangelo Ceci, ItalyYao-Jen Chang, TaiwanNaveen Chilamkurti, AustraliaWook Choi, KoreaHyunseung Choo, KoreaKim-Kwang R. Choo, AustraliaChengfu Chou, TaiwanMashrur A. Chowdhury, USATae-Sun Chung, KoreaMarcello Cinque, ItalySesh Commuri, USAMauro Conti, ItalyAlfredo Cuzzocrea, ItalyDonatella Darsena, ItalyDinesh Datla, USAAmitava Datta, AustraliaIyad Dayoub, FranceDanilo De Donno, ItalyLuca De Nardis, ItalyFloriano De Rango, ItalyPaula de Toledo, SpainMarco Di Felice, ItalySalvatore Distefano, ItalyLongjun Dong, ChinaNicola Dragoni, DenmarkGeorge P. Efthymoglou, GreeceFrank Ehlers, ItalyMelike Erol-Kantarci, CanadaFarid Farahmand, USAMichael Farmer, USAF. Fdez-Riverola, SpainSilvia Ferrari, USAGianluigi Ferrari, ItalyGiancarlo Fortino, ItalyLuca Foschini, ItalyJean Y. Fourniols, FranceDavid Galindo, SpainEnnio Gambi, ItalyWeihua Gao, USAA.-J. García-Sánchez, SpainPreetam Ghosh, USAAthanasios Gkelias, UKIqbal Gondal, AustraliaFrancesco Grimaccia, ItalyJayavardhana Gubbi, Australia

Song Guo, JapanAndrei Gurtov, FinlandMohamed A. Haleem, USAKijun Han, Republic of KoreaQi Han, USAZdenek Hanzalek, Czech RepublicShinsuke Hara, JapanWenbo He, CanadaPaul Honeine, FranceFeng Hong, ChinaHaiping Huang, ChinaXinming Huang, USAChin-Tser Huang, USAMohamed Ibnkahla, CanadaSyed K. Islam, USALillykutty Jacob, IndiaWon-Suk Jang, KoreaAntonio Jara, SwitzerlandShengming Jiang, ChinaYingtao Jiang, USANing Jin, ChinaRaja Jurdak, AustraliaKonstantinos Kalpakis, USAIbrahim Kamel, United Arab EmiratesJoarder Kamruzzaman, AustraliaRajgopal Kannan, USAJohannes M. Karlsson, SwedenGour C. Karmakar, AustraliaMarcos D. Katz, FinlandJamil Y. Khan, AustraliaSherif Khattab, EgyptSungsuk Kim, Republic of KoreaHyungshin Kim, Republic of KoreaAndreas König, GermanyGurhan Kucuk, TurkeySandeep S. Kumar, The NetherlandsJuan A. L. Riquelme, SpainYee W. Law, AustraliaAntonio Lazaro, SpainDidier Le Ruyet, FranceYong Lee, USASeokcheon Lee, USAJoo-Ho Lee, JapanStefano Lenzi, ItalyPierre Leone, Switzerland

Shuai Li, USAShancang Li, UKWeifa Liang, AustraliaYao Liang, USAQilian Liang, USAI-En Liao, TaiwanJiun-Jian Liaw, TaiwanAlvin S. Lim, USAAntonio Liotta, The NetherlandsDonggang Liu, USAYonghe Liu, USAHai Liu, Hong KongLeonardo Lizzi, FranceJaime Lloret, SpainKenneth J. Loh, USAManel López, SpainJuan Carlos López, SpainPascal Lorenz, FranceChun-Shien Lu, TaiwanJun Luo, SingaporeMichele Magno, ItalySabato Manfredi, ItalyAthanassios Manikas, UKPietro Manzoni, SpainÁĄlvaro Marco, SpainJose R. Martinez-de Dios, SpainAhmed Mehaoua, FranceNirvana Meratnia, The NetherlandsChristian Micheloni, ItalyLyudmila Mihaylova, UKPaul Mitchell, UKMihael Mohorcic, SloveniaJosé Molina, SpainAntonella Molinaro, ItalyJose I. Moreno, SpainSalvatore Morgera, USAKazuo Mori, JapanLeonardo Mostarda, ItalyV. Muthukkumarasamy, AustraliaKshirasagar Naik, CanadaKamesh Namuduri, USAAmiya Nayak, Canada

George Nikolakopoulos, SwedenAlessandro Nordio, ItalyMichael J. O’Grady, IrelandGregory O’Hare, IrelandGiacomo Oliveri, ItalySaeed Olyaee, IranLuis Orozco-Barbosa, SpainSuat Ozdemir, TurkeyVincenzo Paciello, ItalySangheon Pack, Republic of KoreaM. Palaniswami, AustraliaMeng-Shiuan Pan, TaiwanSeung-Jong J. Park, USAMiguel A. Patricio, SpainLuigi Patrono, ItalyRosa A. Perez-Herrera, SpainPedro Peris-Lopez, SpainJanez Perš, SloveniaDirk Pesch, IrelandShashi Phoha, USARobert Plana, FranceCarlos Pomalaza-Ráez, FinlandNeeli R. Prasad, DenmarkAntonio Puliafito, ItalyHairong Qi, USAMeikang Qiu, USAVeselin Rakocevic, UKNageswara S.V. Rao, USALuca Reggiani, ItalyEric Renault, FranceJoel Rodrigues, PortugalPedro P. Rodrigues, PortugalLuis Ruiz-Garcia, SpainM. Saad, United Arab EmiratesStefano Savazzi, ItalyMarco Scarpa, ItalyArunabha Sen, USAOlivier Sentieys, FranceSalvatore Serrano, ItalyZhong Shen, ChinaChin-Shiuh Shieh, TaiwanMinho Shin, Korea

Pietro Siciliano, ItalyOlli Silven, FinlandHichem Snoussi, FranceGuangming Song, ChinaAntonino Staiano, ItalyMuhammad A. Tahir, PakistanJindong Tan, USAShaojie Tang, USALuciano Tarricone, ItalyKerry Taylor, AustraliaSameer S. Tilak, USAChuan-Kang Ting, TaiwanSergio Toral, SpainVicente Traver, SpainIoan Tudosa, ItalyAnthony Tzes, GreeceBernard Uguen, FranceFrancisco Vasques, PortugalKhan A. Wahid, CanadaAgustinus B. Waluyo, AustraliaYu Wang, USAJianxin Wang, ChinaJu Wang, USAHonggang Wang, USAThomas Wettergren, USARan Wolff, IsraelChase Wu, USANa Xia, ChinaQin Xin, Faroe IslandsYuan Xue, USAChun J. Xue, Hong KongGeng Yang, ChinaTheodore Zahariadis, GreeceMiguel A. Zamora, SpainXing Zhang, ChinaHongke Zhang, ChinaJiliang Zhou, ChinaTing L. Zhu, USAXiaojun Zhu, ChinaYifeng Zhu, USADaniele Zonta, Italy

Contents

Wireless Sensor Networks for Structural Health Monitoring, Yujin Lim, Gianluigi Ferrari,Hideyuki Takahashi, and Màrius MontónVolume 2015, Article ID 425683, 1 page

An Intelligent Monitoring System for the Safety of Building Structure under the W2T Framework,Haiyuan Wang, Zhisheng Huang, Ning Zhong, Jiajin Huang, Yuzhong Han, and Feng ZhangVolume 2015, Article ID 378694, 16 pages

An Emergency Adaptive Communication Protocol for Driver Health Monitoring inWSN BasedVehicular Environments, Young-Duk Kim, Soon Kwon, Woo Young Jung, and Dongkyun KimVolume 2015, Article ID 704253, 8 pages

A Random Compressive Sensing Method for Airborne ClusteringWSNs, Wei Zhou, Bo Jing,and Yifeng HuangVolume 2015, Article ID 502853, 12 pages

A Robust Home Alone Faint Detection Based onWireless Sensor Networks, Zhen-hai Wang and Bo XuVolume 2015, Article ID 534980, 5 pages

Improved Route Discovery Based on Constructing Connected Dominating Set in MANET, Zifen Yang,Deqian Fu, Lihua Han, and Seong Tae JhangVolume 2015, Article ID 612102, 7 pages

Architecture of Wireless Vehicle Weight Measurement System for Structural Health Monitoring in CivilEngineering Application, Artur Andrzejczak, Paweł Łȩczycki, Michał Wojtera, Piotr Pietrzak,Bartosz Pȩkosławski, and Andrzej NapieralskiVolume 2015, Article ID 202545, 10 pages

EditorialWireless Sensor Networks for Structural Health Monitoring

Yujin Lim,1 Gianluigi Ferrari,2 Hideyuki Takahashi,3 and Màrius Montón4

1Department of Information Media, University of Suwon, Hwaseong 445-743, Republic of Korea2Department of Information Engineering, University of Parma, 43121 Parma, Italy3Research Institute of Electrical Communication, Tohoku University, Sendai 980-8577, Japan4Innovation Unit, WorldSensing, 08013 Barcelona, Spain

Correspondence should be addressed to Yujin Lim; yujin@suwon.ac.kr

Received 4 June 2015; Accepted 8 June 2015

Copyright © 2015 Yujin Lim et al.This is an open access article distributed under theCreativeCommonsAttributionLicense, whichpermits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Structural health monitoring is an innovative method ofmonitoring structural safety, integrity, and performancewithout otherwise affecting the structure itself. Structuralhealthmonitoring utilizesWireless Sensor Networks (WSNs)to detect the presence, location, severity, and consequence ofdamage. In many monitoring allocations, the conventionalusages of WSNs are cases with low data rate, small datasize, low duty cycle, and low power consumption. However,structural health monitoring requires high data rate, largedata size, and a relatively high duty cycle. The scope of thisspecial issue is in line with recent WSN for structural healthmonitoring.

For the current issue, we are pleased to introduce a collec-tion of papers covering a range of topics as follows: (i) designof a road scale system inwireless vehicle weightmeasurementsystem, (ii) design of a routing function in WSNs to obtaina stable routing path and prolong lifetime, (iii) design ofa method to collect information about the behavior andposition of event in the monitoring environment, (iv) designof an emergency adaptive communication protocol to treatthe data packet in a discriminatory manner, (v) design ofa monitoring system for the safety of building structure byusing the semantic and the data fusion technologies, and(vi) design of a sensing data acquisition scheme for airborneclustering WSNs.

As always, we appreciate the high quality submissionsfrom authors and the support of the community of reviewers.

Yujin LimGianluigi Ferrari

Hideyuki TakahashiMàrius Montón

Hindawi Publishing CorporationInternational Journal of Distributed Sensor NetworksVolume 2015, Article ID 425683, 1 pagehttp://dx.doi.org/10.1155/2015/425683

http://dx.doi.org/10.1155/2015/425683

Research ArticleAn Intelligent Monitoring System for the Safety of BuildingStructure under the W2T Framework

Haiyuan Wang,1 Zhisheng Huang,1,2 Ning Zhong,1,3 Jiajin Huang,1

Yuzhong Han,4 and Feng Zhang4

1 International WIC Institute, Beijing University of Technology, Beijing 100124, China2Knowledge Representation and Reasoning Group, Vrije University Amsterdam, 1081 HV Amsterdam, Netherlands3Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi 371-0816, Japan4China Academy of Building Research, Beijing 100013, China

Correspondence should be addressed to Ning Zhong; zhong.ning.wici@gmail.com

Received 3 December 2014; Revised 29 March 2015; Accepted 29 March 2015

Academic Editor: Gianluigi Ferrari

Copyright © 2015 Haiyuan Wang et al.This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Monitoring systems for the safety of building structure (SBS) can provide people with important data related to main supportingpoints in a building and then help people to make a reasonable maintenance schedule. However, more and more data bring achallenge for data management and data mining. In order to meet this challenge, under the framework of WisdomWeb of Things(W2T), we design a monitoring system for the SBS by using the semantic and the multisource data fusion technologies. Thissystem establishes a dynamical data cycle among the physical world (buildings), the social world (humans), and the cyber world(computers) and provides various services in the monitoring process to alleviate engineers’ workload. Furthermore, all data in thecyber world are organized as the raw data, the semantic information, and the multisource knowledge. Based on this organization,we can concentrate on the data fusion from the viewpoints of time, space, and multisensor. At last, a prototype system powered bythe semantic platform LarKC is tested from the aspects of sample performance and time consumption. In particular, noisy data (i.e.,inconsistent, abnormal, or error data) are detected through the fusion of multisource knowledge, and some rule-based reasoningis conducted to provide personalized service.

1. Introduction

Todaymany landmark buildings with new building technolo-gies or new buildingmaterials have gradually emerged, whichalways have novel appearance and unique structure. Relativeto these new buildings, lots of old buildings in the city havealso stepped into the stage of maintenance. Meanwhile, thelarge-scale urban construction will influence the safety ofexisting buildings. In the architectural engineering field, thesafety of buildings structure (SBS), related to the safety ofeveryone who works and lives there, is the concern of thestructural engineers and it is also an important aspect ofbuilding structural health. Building structure is a complexmechanical structure system, which is a space force systemand is designed to withstand various loads. Each buildinghas its own characteristics and responds to some kinds ofstimulation inside or outside.The traditional way of ensuring

the SBS is to arrange the regular manual test. Accordingto the testing results, the maintenance and repairment ofthe building can be arranged. However, the test done bythe engineers will cause some problems: (1) because of thedifferent personnel experience among the engineers, thedifferences exist in these testing results; (2) the testing datacannot be obtained continuously, so the real-time warningcannot be realized; (3) the cost of manpower will grow higheras time goes by.

The emergence of the Internet of Things (IoT) makesit possible to monitor the real-time changes of buildingstructure. All kinds of the data acquisition instruments(DAIs) can be connectedwith each other through the Internetin differentways (e.g.,Wi-Fi, GPRS, or 3G), so a huge networkis formed for collecting any data about things and people,and the big data about building structure is emerging stepby step. As Gross [1] says, Internet will become an electronic

Hindawi Publishing CorporationInternational Journal of Distributed Sensor NetworksVolume 2015, Article ID 378694, 16 pageshttp://dx.doi.org/10.1155/2015/378694

http://dx.doi.org/10.1155/2015/378694

2 International Journal of Distributed Sensor Networks

“skin” on the earth, which connects millions of embeddedelectronic measuring sensors together. These will probe andmonitor the cities, the endangered species, the atmosphere,the ships, the highways, the fleets of trucks, our bodies, andeven our dreams.Themonitoring system for the SBS is a kindof the “skin” which is installed in a building. Each sensor inthis system can sense the slight change of force supportingpoint.The Internet ofThings connects these scattered sensorstogether effectively to formanetwork, and the building healthstatus can be monitored as a whole.

At present, some landmark skyscrapers or some bridgesin the important traffic roads have been or are ready to beinstalled in the monitoring systems. But the different build-ings oftenuse the differentmonitoring systems.Moreover, themechanical features of a building cannot be reflected by onlyone type of sensors. The types of sensors and the monitoringplans are alsomultifarious. Generally, themonitoring processfor a building will last for several years, even the wholelife time of the building. These objective factors lead toa result that the monitoring system for the SBS has thecharacteristics of long service time, complex monitoringobjects, and diversified data types. When these sensed datacome out continuously and momently, the engineers have todo a lot of heavy work, such as sorting and classifying data,numerical analysis, and exception handling. The buildings,the computers, and the users are three separated parts, andthe intelligent level of the whole system is still relatively low.As for the informatization of city, more andmoremonitoringsystems will be used, which will aggravate the engineer’sburden. So the traditional technology has some problemsin sensor management, data management, operational effi-ciency, and system intelligence. How to integrate these data,manage these sensors, fuse this information, and ease theengineers’ burden? All these questions appear in front of us.Therefore, it needs a systematic method that can make themonitoring system more reliable, efficient, and intelligent.

Zhong et al. proposed the notion of Wisdom Web ofThings (W2T) that represents a holistic intelligence method-ology for realizing the harmonious symbiosis of humans,computers, and things using the big data in a wisdom city[2]. In a wisdom city the data of every professional field isconnected together with the Internet, and every field suchas the energy management of a building [3], the monitoringwater resource [4], and the control of traffic [5] will form anintelligent node. All these applications and their knowledgeshould not be limited within the specific areas, and thesharing and interconnectivity of the data among the differentfields within the city scope should be taken into account.TheW2T is an extension of the WisdomWeb in the IoT age. The“Wisdom” means that each of the things in the IoT can beaware of both itself and others to provide the right servicefor the right object at a right time and context based onmultisource heterogeneous knowledge.

This paper presents a monitoring system for the SBSunder the W2T framework, which can effectively perceivethe structural status of various buildings in a city andintegrate the real-time sensed data with the various existingknowledge. This system contains a W2T data cycle, which isnot a simple combination of the social world (engineers or

building owner), the physical world (buildings or sensors),and the cyber world (computers). Data, service, and actionconnect these threeworlds.Data is the epitomeof the physicalworld and represents the changes of it. Service derived fromthe fusion of sensed data and knowledge serves the humansin the social world. The social world here does not meanthat the monitoring process needs manual intervention. Ithas three contributions in this data cycle: (1) the source ofknowledge; (2) the object of service; (3) the influencer of thephysical world. Finally, people can perform some operationsthat influence the buildings, and then the results will bereflected automatically again. The data cycle is formed, andthe physical world (buildings) and the social world (humans)are connected together by the cyber world (computers).

The rest of this paper is organized as follows. Section 2introduces the related work on the monitoring system forthe SBS. Section 3 presents the monitoring system for theSBS under the framework of W2T and analyzes its wholecomposition and the relationship among humans, things, andcomputers. Section 4 gives the hierarchical organization ofthe raw data, the semantic information, and the multisourceknowledge. All kinds of real-time data and multisourceknowledge can be fused to provide personalized intelligentservices. Section 5 tests the prototype system from samplingperformance and query response time and provides somerules to detect the abnormal data andmeet user requirements.Section 6 gives some security considerations as a knowledgesharing node. Section 7 gives the conclusions and the futurework.

2. Related Work

With the economic development, the importance of SBS isbecoming more and more apparent and the standard aboutSBS should be increased reasonably and economically [6].The argument on reasonable settlement of SBS has lastedfor a long time [7]. Experimental data is one of the basesthat are used to formulate and revise the standards andis also a part of the health indicator of a building [8].Many monitoring systems have been deployed in differentbuildings for the SBS. Moriconi and Naik introduced acomputerized monitoring system which has been used fora few years [9]. The applications described in this paper arefor concrete structures, including one steel structure thatis exposed to the sea breeze on the Adriatic Coast of theCity of Ancona, Marche, Italy. The system monitors anddocuments by means of the embedded electrodes and sendsthe records to a computer via a communication line both forthe structure and for the weather. The collected data on thecomputer are saved on backup files, analyzed, and finally sentvia modem to a monitoring station for further processingand dissemination to the architect, engineer, and owner. Inthis way, the early evaluation of the structure is possibleand, consequently, the maintenance costs could be reduced,increasing both the durability and service life of the structure.Kubo et al. introduced a monitoring case in Japan [10]. Theyapply a combination of earthquake early warning system andreal-time strong motion monitoring system to emergencyresponse for two high-rise buildings in Shinjuku Ward,

International Journal of Distributed Sensor Networks 3

Tokyo. The buildings have the sensors of 42 channels, whichconsist of the servo-type accelerometer where the samplingrate is 100Hz, the frequency range is between 0.1 and 30Hz,and the data measurement is between 0.05 and 1000 gal.Theyimprove these systems and emergency response manual andeducate people in these buildings on how to use these systemseffectively. Su et al. introduced a monitoring system usedin Shanghai tower that consists of more than 400 sensorsand is designed for both in-construction and in-service real-time monitoring of the skyscraper [11]. Preliminary monitor-ing data, including vertical settlement, levelness, horizontaldisplacement, and strain/stress, are presented and discussed.The 1-year monitoring exercise during the construction stageshows the satisfactory performance of the strain sensors andthe data acquisition system.

Meanwhile, new sensing technologies such as wirelesssensor technology and new sensing technology have comeinto service to improve the practicability of system. Jang etal. showed a wireless sensor network based on ZigBee tech-nology is used for data acquisition, and then at the computerthe software is written in Java to check for the messagestransmitted over a radio node’s UART port which is designedto receive the wireless sensor data [12]. They use MySQL tosave these monitoring data. A web-based system is developedthat allows user to mine the database using parameters suchas the type of data, the location of sensor, and the time ofdata acquisition. Niu et al. designed a wireless sensor networkbased structural healthmonitoring (SHM) system [13]. Accel-eration data, synchronously sampled in each sensor node, aretransported to a data processing computer through a basestation. In order to achieve a high network throughput, atime division multiple access (TDMA) approach is proposedto reduce the packet collision and energy consumption. Thetest results present that the approach can reduce the signalcollision and increase data throughput. A sensing systemwhich can conduct continuous monitoring of a buildingstructure and generate a monitoring report was developed byLu et al. [14]. This system employing wireless sensing systemhas been developed for the purpose of continuousmonitoringand experimentally verified on the new 8-floor isolationreinforced concrete building. The emphasis in this paper ison the procedures for error check and pass in this continuoussystem. The locations of smart sensors can be modified dueto the irregularity of the building structure. Zhang and Lidesigned modular structural health monitoring system forlarge span spacial structures [15]. Wireless stress and strainmeasurement facility for steel structure, cable tensile forcemeasurement technology, membrane stress measurementequipment, structural displacement instrument, and windenvironment monitoring equipment as well as the finallocal and remote monitoring software were introduced. Allthe modules mentioned above are mutually independent ofeach other and can be chosen and combined in accordancewith project demands. Goldoni and Gamba presented W-TREMORS, a newwireless sensing system for high-frequencydistributed data acquisition [16]. Their sensor network isbased on the low-power and low-data rate standard IEEE802.15.4. They developed a complete software architectureto integrate the wireless sensor network (WSN) with an

existing measurement system. Their prototype WSN wastested through shaking tests in a controlled environment tovalidate the approach and to identify problems. Preliminaryresults show that their solution can effectively monitor seis-mic events providing high reliability and good performance.

Almost all of the above systems pay attention to theseaspects, such as aiming at specific application, more accuratedata collection, faster data transmission, and more effectivenetwork design. With the rapid growth of SBS data, howto standardize, manage, and mine these data become a bigproblem. The utilization of semantic technology does well insolving the problem of interoperability between IoT devices[17]. Some semantic annotation methods are introduced asthe description methods of these devices within the IoT [18].Based on the semantic correlation of data, the semantic-awareinformation can be provided for the users as their demands[19]. This paper paves the way for a new approach to makefull use of these data and the semantic technology that fusesmultisource knowledge to provide customized services.

3. The Monitoring System Architecture underthe W2T Framework

The monitoring system architecture for the SBS under theW2T framework is shown in Figure 1. The physical world isthe world that surrounds us and should be sensed. The socialworld is the world where humans act and think. The cyberworld is the world in computers, and it is a bridge betweenthe other two worlds. More specifically, in the cyber worldthe services can be derived from the data collected by thesensors in the physical world and the knowledge created bythe humans in the social world. The humans can act andaffect the building according to these services, so the socialworld, the physical world, and the cyber world are connectedtogether through data, service, and action, and the effectivedata cycle is formed.

3.1. The W2T Framework. The existing monitoring systemfor the SBS has focused on the collection and conversionof data to provide the engineer with lots of redundancydata. This leads to the result that the engineer must spendso much time in detecting and disposing the invalid data.This is one of the phenomena that effect system efficiencyand impede the data exchange among humans, computers,and things. The W2T is a holistic intelligence framework,leading the system designer to think from the view of datasharing and data cycle. A W2T data cycle system is designedto implement such a cycle, namely, “from things to data,information, knowledge, wisdom, services, and humans, andthen back to things.”This data cycle not only focuses on somedetails but also covers the need of different users. So, theW2T is required to provide a right dynamic service for a rightdynamic object at a right dynamic time and in a dynamiccontext to satisfy a dynamic need in the dynamically changinghyper world [2]. Under theW2T framework, it is not enoughthat the data and the knowledge within this system meet theneeds of this application. All the data and the knowledgeshould be put into the vast Internet and form a resource

4 International Journal of Distributed Sensor Networks

Data server

DAI 2

DAI 3

DAI n

DAI 1

Wirelessrouter

Local area network

AccelerometerDisplacement

sensor

Strain gage

Pressuregage

Monitoring system in a building

Data

W2T

Physicalworld

Cyberworld

Socialworld

BridgeLandmarkbuilding

High-risebuilding

Online serving

RDF

Ontology

Expert system

Engineer Government

Information

Knowledge

Wisdom

Action

Service

Offline mining

Data collecting

Data analysis

Data fusion

Internet

General user

Figure 1: The monitoring system under the W2T framework.

node providing the professional knowledge and enjoying theknowledge from somewhere else. From the external userperspective, everything on the Web is alive [20] and obtainsthe embedded intelligence [21].

The monitoring system for the SBS is a multifield fusionproject, including engineering mechanics, electronic tech-nique, and software engineering and involving a series ofstandards of construction, electrical instrument, and testing.All this knowledge cannot be derived from one person.The W2T provides a design idea where we can fuse theknowledge of engineers in different fields and form aneffective data cycle among these three worlds dynamically.From service perspective, the design of this system targets onthe requirement of different users.

3.2. The Physical World. Within the scope of a building, themonitoring systemwedeveloped ismainly composed of threeparts: the data server in the monitoring center, the sensorsat the measuring points, and the DAIs developed by ChinaAcademy of Building Research, which are the keys that makethese sensors work harmonically.

Sensor. Different monitoring parameters need the differenttypes of sensors. The selection of sensor should follow thesesteps: (1) select the right type of sensor for the monitoringparameter; (2) choose the sensor with range, precision, andfrequency response in the rated scope estimated in advance;(3) check the size and weight of the sensor whether it issuitable for the installation position; (4) consider the datainterface and power supply with the DAIs synthetically.The sensors that are commonly used for monitoring thebuilding structure are shown in Figure 2 ((a) accelerometer,(b) inclinometer, (c) thermohygrometer, and (d) vibratingwire strain sensor).These sensors are selected and installed insome important positions which are decided by the structuralcharacteristics of the building and connected together by theDAIs.

Data Acquisition Instrument. Data acquisition instrument(DAI) is responsible for receiving commands, performingspecific data collection, and detecting exceptional situation.The DAI should match the sensor in the monitoring field; forexample, if the sensor has a digital bus such as UART, SPI, or

International Journal of Distributed Sensor Networks 5

(a) (b) (c)

(d)

Figure 2: Commonly used sensors.

I2C, the DAI must have the same one; if the sensor output isanalog, DAI should be equipped with A/D conversion mod-ular, and the precision and the sampling rate should meet therequirement. As the DAI is a bridge between the data serverand the sensors, the DAI must have the following character-istics: (1) access Internet or local area network via a wired orwireless way; (2) accept the commands from the data server;(3) convert the different sensed data format into the standardone. At the measuring point with the local area networkcables, a wired transmission can be chosen, but at some spe-cial points of the structure, where there is no proper conditionfor laying the cables, Zigbee, Wi-Fi, or 3G can be used forconnecting these DAIs with the data server [22].The wirelessway overcomes the shortcomings of the wired ones and issimple and easy to install and debug the field apparatus. Thechoice of wireless transmission protocol depends on the dis-tance and rate of data transmission;meanwhile, systempowerconsumption and radio interference sources around shouldbe considered. As shown in Figure 3, this DAI (device type:BETC-DY) has four channelswhere four strain sensors can beconnected and true 16-bit peak-to-peak resolution is achiev-able with each channel sample frequency of 1 kHz, making itideally suitable for high resolutionmultiplexing applications.

Data Server. The major functions of data server includeonline sensor identification, DAI parameter setting, start-ing/stopping data acquisition, abnormal state check, anddata storage, display, and analysis. While the collected datais continuously stored in a data server, the data serverprovides personalized services to the remote users or the

Figure 3: Wireless data acquisition instrument with four channels.

application programs through a certain port and will becomean important service node in a wisdom city.The basic processof communication between the data server and the DAIsis shown in Figure 4. There exist two types of commands:broadcast and point-to-point. “Search instruments,” “Startsampling,” and “Stop sampling” are broadcast commandsand “Set instruments” belongs to point-to-point command.“Search instruments” is used for detecting the new DAI,and each DAI with hardware connected successfully shouldrespond to this command and report its registration infor-mation. “Start sampling” and “Stop sampling” are designedto start all the DAIs in this local monitoring system at a givensampling frequency or stop this process. These data collectedfrom the scattered points have the time synchronization andcan be used in the modal analysis of the whole building. Inaddition, point-to-point start and stop commands are also

6 International Journal of Distributed Sensor Networks

DAI_1 DAI_2 DAI_nData server

Search instruments

Set instruments

Start sampling

Stop sampling

Register instruments

Respond setting

Upload data

Respond stop· · ·

· · ·

· · ·

· · ·

· · ·· · ·

· · ·

· · ·

· · ·

Figure 4: Basic data collecting process.

available. “Set instruments” aims at specific DAI and sets flex-ible sampling parameters in accordance with its applicationenvironment.There are other control commands such as DAIbattery check, time synchronization, sleep command, andsetting triggering threshold for the engineers to master theseDAIs.

3.3. The Cyber World. Not only does the cyber world playa role in controlling the data sampling process according tothe monitoring plan, but also under the W2T framework itis a key part to form the data cycle, which contains a varietyof techniques and knowledge. The stress status of the wholebuilding can be sensed completely by a variety of sensorswhich are arranged at the different places reasonably andform the raw data.These raw data from the sensors should beput into an application environment, marked with semanticinformation. By the same marking method, the data with thedifferent expression format [23] from the different sensorsor monitoring systems can be shared. The knowledge aboutthe SBS can be organized by fusing this information with thesensor and building domain ontologies [24] and can form therule-based reason mechanism automatically. Other knowl-edge such as geospatial ontologies [25] also can be integratedinto this system. Supported by the expert system and theprofessional knowledge, the services such as data query, real-time warning, maintenance plan, and even the predictionof damaged structure remaining life can be provided to thedifferent users who may influence the building. This datacycle aims to discover hidden structural dangers, locate theirplaces, determine the degree of these dangers, and eliminatethese hidden dangers. This system plays a positive role inimproving the efficiency of building maintenance, makingreasonable schedule, and avoiding major disaster.

3.4. The Social World. All the instruments in the physicalworld and all the data in the cyber world aim to serve the

people in the social world. The social world here is theknowledge supplier, the service demander, and the physicalworld’s influencer, not the cyber world’s intervenor. Theknowledge in the social world is effectively extracted andsummarized, represented in the form of ontologies. Whenthe knowledge and the sensed data are combined, the servicebecomes an interface between the social world and the cyberworld. Providing the right service for the right people ata right time and context is an important question, so thepersonalized service model can be created for the differentusers [26]. For most structural engineers, they do not carehow this system works and what the characteristics of theDAIs are. The things they really want to know are what thedata is and when and where the data was collected, and thefinal goal is to know how the health status is and what kindof action should be taken. The general users maybe onlycare about some safety tips, and the Government requiresproviding more macroscopical suggestions.

In a word, this system can collect a large number ofdata about the SBS in the physical world, fuse with theexisting knowledge in the cyber world, and serve people inthe social world who will react to the physical world again.So the humans, the computers, and the things will work in adynamic state. The basis of all these services is the data andtheir relationships. The data organization is the key effectivepreprocessing before the data mining.

4. Data, Information, and KnowledgeOrganization

The background information of the sensed data from a givenbuilding can be easily found by retrieving the relationaldatabase. However, within the scope of a wisdom city, thedifferent fields or knowledge systems have customized dataorganization.Thismakes a remote user unable to get effectivecontext about the sensed data easily. In order to realize thedata sharing and interoperating among these systems, a lotof work needs to unify their respective data organization(mainly relational database), and this will cost a lot of time.On the premise of keeping the original data organizationunchanged, the semantic technology is a way to solve theabove problems [27]. In the cyberworld the data from sensorsshould be organized, marked with the semantic backgroundinformation, and fused with the related knowledge. So theprocess can be divided into the raw data, the semanticinformation, and the multisource knowledge three layers.

4.1.TheRawData. Beforemonitoring a building, itsmechan-ical structure should be analyzed according to its designdrawings, and then the measuring points can be selectedreasonably. All of these steps are needed in order to reflectthe actual characteristics of the building structure by usingthe limited sensor resources. The diagram about modeldecomposition and data organization is shown in Figure 5.

According to the different functions, four function mod-ules are formed: themeasuring point arrangement, the sensorselection, the warning setting, and the monitoring plan.

International Journal of Distributed Sensor Networks 7

SensorID_Data

DataIDTimeStampSensedValue

InstrumentID_Ch

ChIDSensorIDRangeGainPrecision

Structural mechanics analysis model

Monitoring pointarrangement Sensor selection Warning setting Monitoring plan

WarningValue

SensorIDLevel1L

Level2L

Level3L

Level1H

Level2H

Level3H

Sensor

SensorIDTypeRangeProducerSensitivityProducedDate

Instrument

InstrumentIDChannelNumProducerProducedDateSerialNumberNetworkNode

MonitoringPoint

PointIDPointInfoInstalledSensors

Plan

InstrumentIDSampleFrequencyDurationCycleTimeBackupTime

Model decomposition

Model implementation

· · ·

...

...

Figure 5: Model decomposition and data organization.

(1) The measuring point arrangement: the measuringpoints which bear loads are found and what type ofthe physical parameter should be monitored can bedecided.

(2) The sensor selection: according to the physical param-eters monitored, we could select the appropriatesensors and use the cooperative DAIs.

(3) The warning setting: some absolute ratings such asbearing capacity and fundamental frequency can becalculated, and the threshold of eachmeasuring pointwill be set accordingly.

(4) The monitoring plan: how often will a data collectionloop start and howmuch is the sampling frequency ofeach sensor can be decided.

These four function modules involve a wide variety ofdata and the relations which can be denoted by relationschema like RelationName (𝐴𝑡𝑡𝑟𝑖𝑏𝑢𝑡𝑒

1, . . . , 𝐴𝑡𝑡𝑟𝑖𝑏𝑢𝑡𝑒

𝑛). So

in the implementation stage, the related data can be convertedinto seven kinds of relations as shown in Figure 5.The relation𝑀𝑒𝑎𝑠𝑢𝑟𝑖𝑛𝑔𝑃𝑜𝑖𝑛𝑡 defines the measuring point informationand the sensors installed. The content about the sensorselection is reflected in the relations Sensor and Instrument.The relation𝑊𝑎𝑟𝑛𝑖𝑛𝑔𝑉𝑎𝑙𝑢𝑒 defines the warning value of eachsensor and generally we set three-level warning mechanismto ensure the SBS. The relation 𝑃𝑙𝑎𝑛 defines the samplingplan of each sensor. There are two kinds of special relations:

the InstrumentID Ch and the SensorID Data. Their quantitydepends on the quantity of the DAI used and the sensorsinstalled. The relation InstrumentID Ch defines the detail ofeach channel of the DAI. When the sensed data comes froma sensor, we need to establish relation SensorID Data for thissensor named with its sensor ID.The relation SensorID Datadefines the data value and the sampling time when collectingdata. These real-time changing records reflect the changingcharacteristic of the building structure. The data comescontinuously and momently, so the raw data about theSBS is formed gradually. Each relation can be described asa relational database table. Each attribute of the relationbecomes a column in the table, and the relationships amongthese relations are described by foreign keys or relation name[28]. Different monitoring systems have the different ways ofdatabase design, but the foundation of these relations is thestructural mechanics model, so the nature of these relationsin the different systems is the same.

When a DAI obtains the sensed data and transmits thesedata to the data server, these relations above are needed todetermine what the real meaning of this data is. Figure 6shows the workflow of getting the sensed data meaning.

When a socket is connected, a dataflow which containsthe A/D conversion voltage value (inputValue), the DAI ID(devID), and theDAI channel (chID) can be got through pars-ing data transmission protocol.The following steps should becarried out to get the meaning of this data.

8 International Journal of Distributed Sensor Networks

Input

Extract instrumentand channel

Get channelinformation

Get sensorinformation

Calculatereal voltage

Get pointinformation

Calculatephysical value

Output

Extract voltagevalue

Save indatabase

1

2 3

4

5

6

7

8

Figure 6: Workflow of getting the sensed data meaning.

Step 1. Extract the voltage value (inputValue).

Step 2. Extract the instrument ID (insID) and the channel ID(chID).

Step 3. Get the channel information such as the channel gain(chGain) and the connected sensor (senID) from the relationinsID Ch.

Step 4. Calculate the real voltage, 𝑟𝑒𝑎𝑙𝑉𝑜𝑙 = 𝑖𝑛𝑝𝑢𝑡𝑉𝑎𝑙𝑢𝑒/𝑐ℎ𝐺𝑎𝑖𝑛.

Step 5. Get the sensor information such as the sensor typeand its sensitivity (sen) through the relation Sensor.

Step 6. Obtain the monitoring point information about theplace where the sensor is installed through senID and therelationMonitoringPoint.

Step 7. Calculate the physical value 𝑝ℎ𝑦𝑉𝑎𝑙𝑢𝑒 = 𝑟𝑒𝑎𝑙𝑉𝑜𝑙 ⋅ 𝑠𝑒𝑛.

Step 8. Save the value 𝑝ℎ𝑦𝑉𝑎𝑙𝑢𝑒 in the corresponding table.

As a result, through a series of the steps above, theengineer knows the meaning of the coming data finally.

4.2. The Semantic Information. The relations in differentsystems are organized in different ways, so the specificationof query process could not be unified. With the emergenceof Semantic Web, the semantic technology provides effectivetechnical way for the management of a large number ofsensors, the representation and sharing of the big data. Weuse the semantic technology to deal with the data and formthe semantic data models. The unified expression is formedby adding semantic information layer between the existingrelational database and the knowledge.

The information of monitoring system has been savedin the relational database with multitables. Each recordhas a set of values (𝑎

1, . . . , 𝑎

𝑛) corresponding to the

attributes in the RelationName (𝐴𝑡𝑡𝑟𝑖𝑏𝑢𝑡𝑒1, . . . , 𝐴𝑡𝑡𝑟𝑖𝑏𝑢𝑡𝑒

𝑛)

[29]. Some mapping rules are made for converting therecords into the semantic NTriple format represented as(𝑆𝑢𝑏𝑗𝑒𝑐𝑡, 𝑃𝑟𝑒𝑑𝑖𝑐𝑎𝑡𝑒, 𝑂𝑏𝑗𝑒𝑐𝑡) [30].

(1) Each table corresponds to a NTriple file with the samename.

(2) Each record can form a RDF node.(3) According to the table type, the property type of RDF

node should be added.(4) A unique identification used as the subject should be

formed by the primary key of each record.(5) The attribute of each column is the predicate in RDF

triple.(6) The value of each column is the object in RDF triple.(7) If the value of column is a primary key in this or

another table, the object in this triple should usethe same expression with the subject formed by theprimary key in another triple.

(8) Make sure of the uniqueness and the habituation ofevery node name.

(9) If necessary, other properties should be increased toform clear semantic information.

Every record can form a NTriple set just like{(𝑠𝑢𝑏𝑗𝑒𝑐𝑡

1, 𝐴𝑡𝑡𝑟𝑖𝑏𝑢𝑡𝑒

1, 𝑎1), . . . , (s𝑢𝑏𝑗𝑒𝑐𝑡

1, 𝐴𝑡𝑡𝑟𝑖𝑏𝑢𝑡𝑒

𝑛, 𝑎𝑛)}. A

piece of NTriple code is shown in Box 1.The measuring point information is important in the

monitoring system. An engineer can get the information ofevery point from the measuring point table, and then theunified concept of spatial layout will be formed in his mind.For the computer, the complete semantic context is neededto establish the relationship among the space points. Simplyconverting this table into the NTriple file is not enough; someother information is necessary. So the requirements of thesemanticmodel about themeasuring points are the following:

(1) Users can clearly know the spatial relationship amongthe points.

(2) Starting from any measuring point, all the othermeasuring points can be found.

(3) Adding a property used to represent the anomalies ofthis point and the building.

(4) Through the semantic model, the building can belocated accurately in a city.

Generally speaking, the building has symmetry andhierarchy, so the RDF model of a measuring point can bedesigned as shown in Figure 7.TheNS, which is just a symboland may be different in different applications, stands for the

International Journal of Distributed Sensor Networks 9

.<http://www.w2t-waas.com/WHY#2013B1sensor2>.

“-2.4”∧∧ .

“2013-12-16T07:54:24.406”∧∧ .

Box 1: The NTriple format representation of the sensed data.

NS: 2013BJbuilding1B

117 40

NS: longitudeNS: latitude

NS: Beijing

B

Bridge

NS: buildingName

NS: inCity

NS: 2013B1point1

NS: north

NS: west

ObservationPoint

NS: down

NS: 2013B1sensor1NS: hasStrainSensor

NS: atBuilding

At the middle of the first pileNS: description

3001

NS: pointID

NS: 2013B1point6

NS: 2013B1point5

NS: 2013B1point4

NS: 2013B1point3 NS: 2013B1point2

NS: 2013B1point7NS: 2013B1sensor2

NS: hasTemperatureSensor

NS: 2013B1p5a2

NS: hasAbnormal

AbnormalRecord

2013-12-16T07:54:24.406

NS: startTime

2014-05-10T12:14:44.121

NS: endTime

NS: type

NS: type

NS: type

NS: upNS: east

NS: south

2NS: riskFactor

Figure 7: The RDF graph about a measuring point.

namespace which ensures that the same word has the samemeaning in different systems. In this case, we assume thata measuring point spatially associates with six other pointsaround. If there is nomeasuring point in one direction, stringNULL can be used for this. The measuring point adds theproperty hasAbnormalwhich is used for indicating the healthstatus of this measuring point and may be inherited by somesubproperties (e.g., hasDataAbnormal and hasSensorAbnor-mal). The property atBuilding connects the measuring pointwith the building. So the scattered measuring points forma network through semantic properties. For more detailedinformation, the information in the CAD graphic documentsalso can be converted into the semantic properties.

The static and dynamic data about the application caseare changed into the semantic information. In this conversionprocess, some knowledge represented by ontologies is alsoused and each piece of the semantic information is anindividual of ontology class, an embodiment of conceptualknowledge.

4.3. The Multisource Knowledge. As shown in Figure 8, themultisource knowledge gained through study or investigationcombines facts, truths, or principles together, which can formcase-based or rule-based knowledge. There is no perfect wayto model this knowledge; ontology is one of the feasiblemethods, which is a formal explicit description of the conceptin a domain and can standardize the representation of

this knowledge. The ontologies used here are divided intotwo categories: the commonsense ontology and the domainontology.The commonsense ontologies quoted here are theseontologies which contain the commonsense concepts of dailylife [31] and have been created and maintained by otherengineers.Thedomain ontologies are the standard expressionof the domain knowledge and the related concept includingsemantic sensor network ontology [32], building standards,and design models, which are professional and should bemaintained by the engineers in this project. Each relation inthe database can be mapped to an ontology class.

When the sensed data is delivered into this system and thesemantic information is created correspondingly by semanticannotation, the sensed data is connected with the existedknowledge resources. The sensed data is directly relatedto an individual of the sensor ontology, which containsthe measuring point’s link. With the measuring point, thebuilding ontology can be integrated with the real-time senseddata. Through the sampling time of the sensed data and thegeographic information of the building, the commonsenseontologies such as weather ontology and geographic ontologycan be used. So the real-time sensed data drops into a knowl-edge net; a series of results can be reasoned or concludedbased on this knowledge.

Generally speaking, one group of sensed data cannotillustrate the fault or the error situation adequately whichneeds large numbers of data to make a comprehensive

10 International Journal of Distributed Sensor Networks

SensedDataSensedDataSensedData

SensedDataSensedDataSensedData· · ·

· · ·

......

Building ontology

Sensor ontology

Measuring point

Time fusion Space fusion

Domain ontologies

Geospatial ontology

Weather ontology

Case-based knowledge

Rule-based knowledge

Multisensor fusion

Commonsenseontologies

Mechanical model

Professional processingSensedData1 n

SensedData n n

Figure 8: The multisource knowledge organization.

analysis and judgment. To achieve this goal, three fusionmethods can be proposed from three different viewpoints:time, space, and multisensor [33].

Time Fusion. The time fusion means the analysis of thechanging data about a sensor during a period of time, sothe rationality of the current data can be concluded by thehistorical data.The least square method is often used in time-based analysis to form the fitting equation [34], which aims todetermine the deviation of each sampling data and predict thetrend of the structure deformation. Given a time-based seriesset of sampling data {(𝑡

1, 𝑥1), . . . , (𝑡

𝑘, 𝑥𝑘)} from vibrating wire

strain sensorwhere t is sampling time and𝑥 is strain samplingvalue, the curve-fitting equation can be represented by thefollowing formula:

𝑥𝑖= 𝑏 ⋅ 𝑡

𝑖+ 𝑎, (1)

where (𝑡𝑖, 𝑥𝑖) is the point in the fitting curve whichminimizes

the sum of squares of error among these sampling data.The parameters 𝑎 and 𝑏 can be calculated by

𝑏 =𝑥 ⋅ 𝑡 − 𝑥𝑡

𝑡2

− 𝑡2,

𝑎 = 𝑥 − 𝑏 ⋅ 𝑡,

(2)

where

𝑥 =1

𝑘

𝑘

∑

𝑖=1

𝑥𝑖, 𝑡 =

1

𝑘

𝑘

∑

𝑖=1

𝑡𝑖,

𝑥𝑡 =1

𝑘

𝑘

∑

𝑖=1

(𝑥𝑖𝑡𝑖) , 𝑡2 =

1

𝑘

𝑘

∑

𝑖=1

𝑡2

𝑖.

(3)

Those sampling data which have the biggest distancewith the fitting curve can be found and should be focusedon particularly. From the trend of the fitting curve about amonitoring point, we can analyze the reasons behind the data,find the hidden nature, predict the next value, and so on.

All kinds of analysis methods based on time such as FourierTransform and Wavelet Analysis also can be used in thisfusion processing.

Space Fusion. The space fusion means that the data aboutthe space adjacent points can be analyzed and the spatialmeasuring points can form an interconnected net whichreflects the SBS from the perspective of the whole building.Thewhole building risk factor (brf) is related to the risk factorof each monitoring point (prf):

brf =𝑛

∑

𝑖=1

(𝑘𝑖⋅ prf𝑖) ,

𝑛

∑

𝑖=1

𝑘𝑖= 1, (4)

where 𝑘𝑖is weighting coefficient which depends on the

building structure model. When an exception occurs at ameasuring point, not only the data about this point changes,but also the data of the surrounding points will change atthe same time, which can reflect the scope of abnormal phe-nomenon and indicate the area that should be maintained.

Multisensor Fusion. The multisensor means that the changesof objective physical characteristic could be reflected moreaccurately through many different types of sensors. Forexample, the strain of building structure can be measured bythe strain sensor.The strain value (𝑦) sensed is influenced notonly by the structural change (𝑦1) but also by the strain (𝑦2)caused by the environment temperature (𝑇). So the sensedstrain value can be represented by the following formula:

𝑦 = 𝑦1 + 𝑦2,

𝑦2 = 𝑓 (𝑇) ,

(5)

where 𝑦2 is related to the environment temperature, soa temperature sensor should be used for monitoring thetemperature around the strain sensor. Removing the influ-ence of 𝑦2, the real strain change of structure is presentedin front of us. We can make full use of the redundantand complementary information produced by these multiple

International Journal of Distributed Sensor Networks 11

sensors to obtain the consistency of interpretation and theintegrated description of monitored building. The ultimategoal is to improve the accuracy and effectiveness of the wholesensor system, using the advantage of multiple sensors todeduce more effective information.

The rules base based on the expert’s experience is estab-lished to integrate the mining methods into the practicalapplication. When a set of new data is sampled, the differentprocessingmethodsmatch the different data according to therules and the data type which can be retrieved by SPARQLbased on semantic data model.

Rule 1.

IF(newdata.fromSensor.type==Strain)

THEN Calculate the deviation with the fittedcurve

ELSE IF(newdata.fromSensor.type==Vibration)

THEN Compute architecture modal parameters

ELSE IF(newdata.fromSensor.type==Temperature)

THEN Revise the strain data with this tempera-ture value

......ENDIF

After professional processing, the characteristics of datashould be annotated timely and can be retrieved directlywhen user submits some queries. Establishing independentrules base and separating rules from the general operationsmake the maintenance of system more convenient. Theengineer could adjust the rules at any time according to theapplication without affecting other parts of the system. Themeaning of data determines the next step of data processingmethod, so the semantic annotation of data becomes bridgeamong knowledge, rules, and data. Except for the fusion oftime, space, andmultisensor, a series of intelligent processingmodels including the detection of noisy data, the automaticcorrection of data, and the real-time warning can be con-nected together by semantic technology.

Not only is the engineer’s work burden alleviated, butalso new knowledge (such as case-based knowledge) evencan be extracted from the fusion of different knowledge andthe ontologies can be improved or modified according to theresults. In addition, by adopting the unified namespace orthe mapping rules among different systems, the data fusionamong buildings in a city can be realized.

5. Prototype System Testing

The internal structure diagram of this testing system poweredby the LarKC platform is shown in Figure 9. LarKC (LargeKnowledge Collider) is a semantic platform for scalablesemantic data processing and reasoning (https://gate.ac.uk/projects/larkc/). LarKC was developed by the European

Browser

Internet

Weatherserver

DAI_1

Applicationplatform

Datacollection

Datastorage

Datareading

Semantictranslation

Relationaldatabase

Semanticdata file

Data management

Workflow

Endpoint

Sensor_1

Figure 9: The internal structure diagram of the prototype system.

Union’s Seventh Framework Program, aiming to remove thescalability barriers of the currently existing reasoning systemsfor the Semantic Web. The main features of the LarKCplatform are as follows.

(i) Configurability: LarKC provides a flexible and mod-ular environment where users and developers areable to build their own workflows and plug-ins,respectively, in an easy and straightforward manner.This platform has a pluggable architecture in which itis possible to exploit techniques and heuristics fromdiverse areas such as databases, machine learning,cognitive science, Semantic Web, and others [35].

(ii) Scalability: in LarKC, massive, distributed, and nec-essarily incomplete reasoning is performed overweb-scale knowledge sources. Massive inference isachieved by distributing problems across heteroge-neous computing resources and coordinated by theLarKC platform [35].

(iii) Parallelism: LarKC supports parallel reasoning andprocessing by using cloud computing and clustercomputing techniques and is engineered to be ulti-mately scalable to very large distributed computa-tional resources [36].

The various types of data used by the different systems canbe integrated by the unified semantic representation whichcan provide semantic interoperability among them [37].

The monitoring data server in a building is establishedfor the SBS and becomes one of the distributed servers onInternet in a smart city. The personalized services can beprovided through a specific port. The remote user or the userplatform can log in the server and submit queries, and thesystem will return the results to user webpage or application

12 International Journal of Distributed Sensor Networks

Class

SensedData

fromSensor

Sensor

MeasuringPoint

Monitoredbuilding

Individual

SensedData_n

Sensor_n

fromSensor

hasSensor

Measuringpoint_n

atBuilding

Monitoredbuilding_n

isInstanceOf

isInstanceOf

isInstanceOf

isInstanceOf

Commonsense knowledge

Timeontology

Weatherontology

Spatialontology

Professional knowledge

timeStamp

inCity

Date

City

atBuilding

hasSensor

Semantic information

Figure 10: Ontology organization in the prototype system.

program. Through the data and the information returned,the user can understand the meaning of data and combinewith their own applicationmodel.The data source consists ofthe raw sensed data, the semantic RDF information, and theknowledge represented by ontologies. These files are loadedinto the LarKC platform which can provide different dataservices by designing different workflow plug-ins.

The involved ontology classes and individuals are orga-nized as shown in Figure 10. The class SensedData has theproperty fromSensor from which we can get the source ofthose data, then the property hasSensor of the class Measur-ingPoint establishes the relationship between the sensor andthe measuring point, and then the property atBuilding of theclass MeasuringPoint specifies the building monitored. Thecommonsense knowledge used here is represented by threeclasses Time, Weather, and Spatial. Through the propertytimeStamp of the class SensedData and the property inCityof the class MonitoredBuilding, the domain knowledge andthe commonsense knowledge are connected together, andsome new or neglected knowledge could be found out byknowledge fusion technique.

5.1. Sample Performance. The DAI shown in Figure 3 canconnect with four strain sensors, taking it as representative.The average sampling response time from sending startcommand to receiving the first sensed data is about 200mswhich includes the data stability time, the A/D conversiontime of four channels, and the signal transmission time.For high-speed data acquisition, 16-Mbit on-broad ram isprovided for buffering the data before the transmission inevery sampling loop.The sampling precision is one of the keyparameters of DAI, and we carried out this test performedby the National Institute of Metrology in China. The result isshown in Figure 11. The minimum resolution is 1 𝜇𝜀, and themaximum relative error is 5.3% when the standard value is20𝜇𝜀. The sensed data can be corrected to the standard valueby using the following formula:

𝑦 = 0.953 ⋅ 𝑥 + 0.733. (6)The calibration rule for this DAI can be made according

to formula (6). So the data can truly reflect the physical

Testi

ng v

alue

y(𝜇𝜀)

Standard value x (𝜇𝜀)

R2 = 1

y = 0.953x + 0.733

1000

100

10

100010010

Figure 11: Accuracy testing of strain data sampling.

property.The visual transmission distance is 500 meters withthe 4.15 dBi antenna, and it can meet the basic measuringneed of a wide range of applications. If necessary, the wirelessrange can be extended by adding additional repeater units.

5.2. Time Comparison about Data Query. The time compar-ison about data query between the LarKC-based semanticsystem and the traditional SQlite-based database system wascarried out. The testing platform is composed of Intel i5-2430M dual core processor, 2.95G physical memory, andWindows 7 operating system.The SQlite-based database sys-tem is the traditional database system, designed byQtCreatorwhich is a cross platform application and UI framework, andprovides service for data collection and display. Because it is aspecialized design, so it is difficult to realize the data sharing.The time experiment is designed to test the time consumptionfor querying the same data between these two systems. Thesteps are as follows:

(1) Prepare two copies of the same data. One is saved inSQlite database and the other is transformed to thesemantic NT data.

International Journal of Distributed Sensor Networks 13Ti

met

(ms)

SPARQL query number n

0

200

400

600

800

1000

1200

0 1 2 3 4 5 6 7 8 9 10

120000

100000

80000

60000

40000

20000

Figure 12: Query response time about LarKC-based system underdifferent data sizes.

(2) Start the system and load into the respective data.(3) Design the SQL and the SPARQL statements for

querying the same data.(4) Run the query in these two systems and get the

elapsed time.(5) Repeat Step (4) 10 times.(6) Close software and clean the computer memory.

The test was repeated in different datasets from 10000 to120000 points. The query time in the LarKC-based semanticsystem is shown in Figure 12.The first query needs more timeas the LarKC does some internal processing and saves theintermediate results in memory. In the process of subsequentqueries, the average time consumption is about 15ms, and itdoes not change with the expansion of data size observably.The average time consumption is compared in Figure 13. Thetime used in the SQlite-based database system will increasewith the expansion of data size. Aiming at monitoring forthe SBS it produces large amounts of data from sensorsevery day, and the engineer is only interested in those datathat exceed the thresholds. The thresholds will not changeonce the system has been set, so the query is always fixed.Therefore, the LarKC ismore suitable for those systemswhichhave the fixed query schema.

5.3. Knowledge-Based Detection on Noisy Data. Anotheradvantage of semantic data is the ability to realize theinteroperability of data and knowledge on the Web. Theinherent correlation of data on the Web can assist us in thereasonable justification of the new sensed data. A sensedtemperature data from a bridge monitored in Beijing, China,is −23.8∘C on December 16, 2013. According to the engineer’sown life experience, he can get the conclusion that these datahave problem. First of all, in early December the temperatureof Beijing is impossible below −20∘C. Furthermore when

Tim

et(m

s)

0 20000 40000 60000 80000 100000 120000 1400000

10

20

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Qt + SQliteJava + LarKC

Figure 13: Comparison of query response time.

choosing the location of a measuring point, the place wherethe sensor is installed cannot be in a harsh or unstableenvironmental condition such as direct solar radiation or hot-cold source. Once this abnormal data appears, it needs toremind the workers in the field to check the sensor.

In this case we use geographic ontology and weatherontology to detect the abnormal data like the engineer does.When a sensed data (expressed by the variable tempValue)comes into this system, we can get this information of whenthis data was sampled and which sensor did this. Further, theinstallation site information (e.g., building name, longitude,latitude, and city) can get retrieved from the semanticproperties of this sensor. By using the sampling time and thelocation information of the monitored building, the weatherinformation can be obtained through the link between theweather ontology and the geographic ontology. The highesttemperature (expressed by the variable highTem) and thelowest temperature (expressed by the variable lowTem) ofBeijing on December 16, 2013, can be returned back by usingthe following SPARQL queries:

SELECT DISTINCT ?day ?highTem ?lowTemWHERE{?weaUri rdf:type wea:weather.?weaUri wea:City ?city.?weaUri wea:Date ?day.?weaUri wea:HighTemperature ?highTem.?weaUri wea:LowTemperature ?lowTem.FILTER(?day=“2013-12-16” && ?city=“Beijing”).}

In this case the relationship between the weather tem-perature and the temperature of the measuring point isestablished by the people’s experience. The logic rule fordetecting this abnormal phenomenon is shown as follows.

14 International Journal of Distributed Sensor Networks

Rule 2.

fromSensor(?tempValue,?sensor) and hasSensor(?spot,?sensor) and(largeThanOrEqual(?tempValue,?highTem+10) orlessThanOrEqual(?tempValue,?lowTem-10))⇒hasSensorAbormal(?spot)

The user application integrates the sensed data, theweather information, and the related knowledge to judge therationality of the real-time sensed data, so the engineer canreceive an abnormal event as a kind of customized services;another user such as the general user and the Governmentmay not get this massage.

5.4. Rule-Based Data Reasoning. Based on the rules base,some data reasoning can be made and a satisfying answercan be provided to meet the complex query. In practicalapplication, the engineer often asks questions directly relatedto their purposes. For example, before doing the regularcheck, they may want to know what are the abnormal pointsthat have happened since the last check? Before answeringthis question, each monitoring point has been marked forabnormal (risk factor is 3, 4, or 5) or normal (risk factoris 1 or 2) in advance in the light of the sensed data. Theabnormality of monitoring point (expressed by the variable𝑈mp) is mirrored by the sensed data, but the abnormality ofsensed data may not be caused by the monitored point only;failed sensor can also lead to this phenomenon. We defineRule 3 to reason the abnormal sensed data; the set formed byall the relevant points is represented as 𝑈data.

Rule 3.

fromSensor(?data,?sensor) andhasSensor(?spot,?sensor) and((warnning1HighLevel(?sensor, ?high) andlargeThanOrEqual(?data, ?high)) or(warnning1LowLevel (?sensor, ?low) andlessThanOrEqual(?data,?low)))⇒hasDataAbormal(?spot)

We define Rule 4 as a way to detect the abnormal datacaused by sensor; the point set formed by this rule isrepresented as 𝑈sen1.

Rule 4.

fromSensor(?data,?sensor) andhasSensor(?spot,?sensor) and((sensorMaxRange(?sensor, ?max) andlargeThanOrEqual(?data,?max)) or(sensorMinRange(?sensor, ?min) andlessThanOrEqual(?data,?min)))⇒hasSensorAbormal(?spot)

The abnormal sensor can be detected by different rules,and Rule 2 is also one of them. The abnormal monitoringpoint usually could be obtained by the point set withabnormal data minus the point set with abnormal sensor

𝑈mp = 𝑈data −𝑛

⋃

𝑖=1

𝑈sen𝑖. (7)

These rules are built and stored in the rules base. Oncedata is sampled, the system calls the corresponding ruleautomatically, determines the current state timely, and marksthe data with semantic information adequately. All kinds ofdata are in a standby state. After user submits the querystatements, the system organizes data in the fastest way andavoids the time consumption caused by the call of somealgorithms.

The time “after the last check” divides the abnormal pointset into two parts. So the new abnormal point set (𝑈new) canbe defined as follows:

𝑈new = {𝑠𝑝 | 𝑠𝑝 ∈ 𝑈mp&&𝑠𝑝.ℎ𝑎𝑠𝐴𝑏𝑛𝑜𝑟𝑚𝑎𝑙.

𝑠𝑡𝑎𝑟𝑡𝑇𝑖𝑚𝑒 ≥ 𝐿𝑎𝑠𝑡𝐶ℎ𝑒𝑐𝑘𝑇𝑖𝑚𝑒} .

(8)

Therefore, on the premise of data organization, it onlyneeds to search two properties of RDF without the additionalcalculations in the process of query.

Users can design the more complex SPARQL querystatements to meet their needs or the intelligent models.According to these data, the engineers can know the currentstate of the building, check the potential danger, and makethe building maintenance schedule.

In a wisdom city, when different monitoring systems inthe buildings use the same namespace or establish the map-ping between different namespaces [38], the data sharing andinteroperability become possible, and then the organizationof big data for the SBS can be basically completed. Until thattime, all kinds of wisdom services under theW2T frameworkcan be provided based on these big data.

6. Security Considerations

Within the coverage area of theWSN, it is possible that otherelectronic devices access the wireless network. Meanwhile,the opened service port on the Web can be accessed mali-ciously and even be attacked [39]. Malicious access can causedata disorder, produce incorrect results, or estimate the SBSunpredictably.

For the WSN and open Web, a single security solutionfor a single layer might not be an efficient solution, andemploying a holistic approach could be the best option[40]. So it is necessary to build a security mechanism, andthree-level security mechanism is considered at differentlevels. (1) Wireless access: without the designated usernameand password, anonymous DAI cannot access the wirelessnetwork. Meanwhile, a special communication frame formatused for communications between the DAI and the dataserver is designed. Only the format of datameets the commu-nication format, and then this data frame is valid. (2) Service

International Journal of Distributed Sensor Networks 15

interface: the remote user must register to get the service.Different services with access permission can be provided todifferent users according the registration information, andprofessional model can also be built targeting specific user.(3) Knowledge authentication: referencing the knowledgeon the Web the knowledge authentication and the approvalmechanism are needed, and wrong knowledge reasoning orwrong service information can be avoided.

7. Conclusion and Future Work

Today, more and more buildings begin to monitor the SBS,and the importance of data will gradually appear. The acq-uisition and transmission of the sensed data will no longerbe problems in the general monitoring case. Instead, theproblems will concentrate on these aspects, such as how tomanage this coming data stream and how to use these dataeffectively.

This paper designs an intelligent monitoring systemfor the SBS under the W2T framework, which organizesthe data, the information, and the knowledge by usingthe semantic and the data fusion technologies. A series ofoperable tables are got from the model of building structure,and some mapping rules are proposed for converting thesetables into semantic information. The real-time sensed data,the semantic information, and the related knowledge areconnected together to evaluate the reasonability of the dataautomatically. At last, the prototype system was tested fromsampling performance and knowledge fusion application.

In the future, semantic model about building structurewill be optimized, and the ontologies of building structureand sensor will be improved. More methods about dataanalysis and data fusion can be used in these sensed data,so the system is intended to grow more intelligent. Someresearches have been done about the OGC’s Sensor WebEnablement (SWE) standards [41] which can make it easyto integrate this information into thousands of geospatialapplications that implement the OGC’s other standards [42].The standardized models of the OGC also can be consideredto join into this system, and thus more sensor models andsensed data can be reused.

Disclosure

This paper is original and has been written by the statedauthors who are all aware of its content and approve itssubmission.

Conflict of Interests

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

Acknowledgments

The work is supported by National Natural Science Foun-dation of China (61420106005, 61272345), National BasicResearch Program of China (2014CB744600), International

Science & Technology Cooperation Program of China(2013DFA32180), Beijing Municipal Commission of Educa-tion, and Beijing Key Laboratory of Magnetic ResonanceImaging and Brain Informatics.

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Research ArticleAn Emergency Adaptive Communication Protocol for DriverHealth Monitoring in WSN Based Vehicular Environments

Young-Duk Kim,1 Soon Kwon,1 Woo Young Jung,1 and Dongkyun Kim2

1Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu 711-873, Republic of Korea2School of Computer Science and Engineering, Kyungpook National University, Daegu 702-701, Republic of Korea

Correspondence should be addressed to Dongkyun Kim; dongkyun@knu.ac.kr

Received 19 December 2014; Accepted 23 March 2015

Academic Editor: Hideyuki Takahashi

Copyright © 2015 Young-Duk Kim et al. This is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properlycited.

Driver health and activity monitoring is one of the principal design issues for the safety provision in vehicular environments.Recently, the wireless sensor network technology is widely used to address the concerns in such applications. However, only fewconventional protocols have dealt with reliable and prompt delivery of emergency packets considering the vehicular specifications.In this paper, we propose an emergency adaptive communication protocol, which treats the data packet in a discriminatorymannerby investigating whether it is emergency or not. Hence, the proposed protocol defines an emergency factor for each data packet andexploits it for both route establishment and channel access procedures. In route establishment, the proposed protocol chooses a routewith low delay and high reliability among the candidates by periodic calculation of emergency factor. Then, it dynamically adjustsback-off parameters before participating in the channel contention among the neighbors. In addition, an emergency aware queuemanagement scheme and packet drop policy are proposed to improve the reliability of emergency data traffic during transmission.Our simulation results show that the proposed protocol provides a better performance comparedwith the existing protocol in termsof packet delivery ratio, end-to-end packet delay, and number of dropped packets.

1. Introduction

A wireless sensor network (WSN) is a network of sensordevices used in various application fields such as medicine,military, agriculture, and industry. One of the most popularapplications of WSN is in development and deployment ofhealth monitoring system to provide people with suitable,timely, and efficient safety services anytime and anywhere. Inthe case of vehicular environments, driver heath monitoringis significantly crucial for designing the vehicular safetybecause a delay in processing driver’s abnormal health datamay result in serious traffic accidents. However, to developWSN based driver health monitoring systems, there areseveral problems. The most general problems are describedas follows.

First, according to the previous research [1], between 13%and 50% of vehicular crashes are due to driver distraction andabnormal conditions. Hence, by adopting WSNs, an efficient

identification of a driver’s health condition and related emer-gency situations can mitigate the occurrence of traffic acci-dents. This implies that the adopted WSN should be capableof immediate packet processing and delivery for the entireemergency bionic conditions to prevent critical accidents.