1 Software Development for Acquisition and Data Management in Optical Sensor Networks A. D. F. Santos, M. F. M. Silva, C. S. Sales, C. S. Fernandes, M. J. Sousa and J. C. W. A. Costa Abstract—Due to their unique characteristics, optical sensor networks have found application in many fields, such as in Civil and Geotechnical Engineering, Aerospace, Energy and Oil and Gas Industries. Monitoring solutions based on this technology have proven particularly cost effective when applied to large scale structures where hundreds of sensors must be deployed for long term measurement of different physical parameters. Fiber Bragg gratings (FBG) are the most reliable solution in some harsh environment applications where the sensors are submitted to extreme electromagnetic interference. Acquisition rates increasingly higher have been possible using the latest optical interrogators, which gives rise to a large volume of data whose manipulation, storage, management and visualization can be performed by software applications. This work presents two real-time software applications developed for these purposes: Interrogator Abstraction (InterAB) and Web-based System (WbS). The innovations in this work include the integration, synchronization, independence, security, processing and real-time visualization, data persistence and flexibility provided by joint work of the applications developed. The results showed the use of these softwares in the laboratory and real environments in accordance with the features proposed. Index Terms— Software Development, Optical Sensor Networks, Data Acquisition, Data management. I. I NTRODUTION Currently there has been a significant increase in the use of software systems in sensor networks [1], [2]. The efficient integration of this information from these systems has become a very important task. This led to the growing challenge for sensor technology industry to develop new concepts, techniques and softwares [3]. One of the motivators of this fact is due to the increasing use of systems called standalone, which are systems that work independent of others. If these systems share the same database there not will be issue about this approach, otherwise, data integration will be an essential condition required to share the existing data [4]. In a wireless sensor network with many sensor nodes measuring different quantities, as well as a optical sensor network using multiplexing, the potential to generate a large volume of data that can become computationally intractable is quite plausible. The optical sensing is based on the principle that the measured information (e.g., temperature, strain, acceleration) is wavelength-encoded in the Bragg reflection of the grating [5], [6]. Measurand changes are coded on wavelength shift of a given Bragg sensor, which are processed by optical interrogator. Interrogators in fiber grating sensor systems are the measurand-reading units that extract measurand information from the optical signals coming from the sensor heads. The interrogators usually measure the Bragg wavelength shifts and convert the results to measurand data [7], [8]. This data can be stored in a file or made available to client software that establishes communications with the interrogators in accordance with standard protocols. A WEB controlled interrogation system to determine the peak wavelength of the reflection spectra of Bragg gratings is modeled in [9]. This system is based on a scanning Fabry Perot interferometer, whose electronic control unit has been modified to allow automated operation under external micro-controlled supervision. A micro-controller with embedded Web server capabilities is used to permit the access through the Internet. In [10] is proposed a wireless network sensor information and identification system (WiNS Id) database which archives the data reported by distributed sensors, as well as the implemented support for queries and data presentation. The system features include real-time support for data presentation and visual presentation of sensor nodes reporting in a geographical and temporal context. Additionally, WiNS Id provides support for pattern identification and data mining in sensor systems. A datawarehouse for management of data from wireless sensors to monitor the habitat of bees is shown in [11]. This paper proposes a model to extract, transform and normalize data from sensor networks and load them into a data warehouse. Thus, this data can be easily analyzed by experts to assist in the process of decision making. However, this paper shows no filtering technique of data generated by sensors, which can be large in volume and impair the process of obtaining information. This is on the assumption that the databases to be integrated are already consolidated, but in practice the treatment of large volumes of data ends up being the most costly part of the system. A dynamic model is proposed in [12] that considers the advantages of different storage models such as the local, external and data-centric model. The data transmission management is critical in wireless sensors networks, due to the high impact on overall energy consumption. From the literature review there is not software applications for monitoring in optical sensor networks. This paper proposes a monitoring system, composed of two softwares, capable to collect, process, persist, retrieve, manage, and present data from optical sensor networks. The InterAB System and WbS will be described based on their features and their individual contributions to the overall integrated monitoring system. The paper is organized as follows: the section II demonstrates how the integration is achieved between softwares; the InterAB and WbS features are detailed in Sections III and IV, respectively; the Section V presents and discusses test results; final remarks and future work are presented in Section VI.
Transcript
1
Software Development for Acquisition and DataManagement in Optical Sensor Networks
A. D. F. Santos, M. F. M. Silva, C. S. Sales, C. S. Fernandes, M. J. Sousa and J. C. W. A. Costa
Abstract—Due to their unique characteristics, optical sensornetworks have found application in many fields, such as in Civiland Geotechnical Engineering, Aerospace, Energy and Oil andGas Industries. Monitoring solutions based on this technologyhave proven particularly cost effective when applied to largescale structures where hundreds of sensors must be deployedfor long term measurement of different physical parameters.Fiber Bragg gratings (FBG) are the most reliable solution insome harsh environment applications where the sensors aresubmitted to extreme electromagnetic interference. Acquisitionrates increasingly higher have been possible using the latestoptical interrogators, which gives rise to a large volume of datawhose manipulation, storage, management and visualization canbe performed by software applications. This work presents tworeal-time software applications developed for these purposes:Interrogator Abstraction (InterAB) and Web-based System(WbS). The innovations in this work include the integration,synchronization, independence, security, processing and real-timevisualization, data persistence and flexibility provided by jointwork of the applications developed. The results showed the useof these softwares in the laboratory and real environments inaccordance with the features proposed.
Index Terms— Software Development, Optical SensorNetworks, Data Acquisition, Data management.
I. INTRODUTION
Currently there has been a significant increase in the useof software systems in sensor networks [1], [2]. The efficientintegration of this information from these systems has becomea very important task. This led to the growing challengefor sensor technology industry to develop new concepts,techniques and softwares [3]. One of the motivators of thisfact is due to the increasing use of systems called standalone,which are systems that work independent of others. If thesesystems share the same database there not will be issue aboutthis approach, otherwise, data integration will be an essentialcondition required to share the existing data [4].
In a wireless sensor network with many sensor nodesmeasuring different quantities, as well as a optical sensornetwork using multiplexing, the potential to generate a largevolume of data that can become computationally intractableis quite plausible. The optical sensing is based on theprinciple that the measured information (e.g., temperature,strain, acceleration) is wavelength-encoded in the Braggreflection of the grating [5], [6]. Measurand changes arecoded on wavelength shift of a given Bragg sensor, whichare processed by optical interrogator. Interrogators in fibergrating sensor systems are the measurand-reading units thatextract measurand information from the optical signals comingfrom the sensor heads. The interrogators usually measure theBragg wavelength shifts and convert the results to measuranddata [7], [8]. This data can be stored in a file or made available
to client software that establishes communications with theinterrogators in accordance with standard protocols.
A WEB controlled interrogation system to determine thepeak wavelength of the reflection spectra of Bragg gratingsis modeled in [9]. This system is based on a scanningFabry Perot interferometer, whose electronic control unithas been modified to allow automated operation underexternal micro-controlled supervision. A micro-controller withembedded Web server capabilities is used to permit the accessthrough the Internet.
In [10] is proposed a wireless network sensor informationand identification system (WiNS Id) database which archivesthe data reported by distributed sensors, as well as theimplemented support for queries and data presentation. Thesystem features include real-time support for data presentationand visual presentation of sensor nodes reporting in ageographical and temporal context. Additionally, WiNS Idprovides support for pattern identification and data mining insensor systems.
A datawarehouse for management of data from wirelesssensors to monitor the habitat of bees is shown in [11].This paper proposes a model to extract, transform andnormalize data from sensor networks and load them into adata warehouse. Thus, this data can be easily analyzed byexperts to assist in the process of decision making. However,this paper shows no filtering technique of data generated bysensors, which can be large in volume and impair the processof obtaining information. This is on the assumption that thedatabases to be integrated are already consolidated, but inpractice the treatment of large volumes of data ends up beingthe most costly part of the system.
A dynamic model is proposed in [12] that considersthe advantages of different storage models such as thelocal, external and data-centric model. The data transmissionmanagement is critical in wireless sensors networks, due tothe high impact on overall energy consumption.
From the literature review there is not software applicationsfor monitoring in optical sensor networks. This paper proposesa monitoring system, composed of two softwares, capable tocollect, process, persist, retrieve, manage, and present datafrom optical sensor networks. The InterAB System and WbSwill be described based on their features and their individualcontributions to the overall integrated monitoring system. Thepaper is organized as follows: the section II demonstrates howthe integration is achieved between softwares; the InterAB andWbS features are detailed in Sections III and IV, respectively;the Section V presents and discusses test results; final remarksand future work are presented in Section VI.
2
II. FLEXIBILITY, INDEPENDENCE AND INTEGRATIONBETWEEN INTERAB AND WEB-BASED SYSTEM
The interAB and WbS are applications that can workindependently or jointly. The interaction between softwaresare possible by sharing the same database, as shown in Figure1. The InterAB System performs the acquisition, processingand data persistence, at the same time populating a updatetable that records data inserts in the database. These updatesare often checked by the WbS in order to update the databeing made real-time available to the user. In the applicationscontext, Hibernate [13] is characterized as a communicationinterface between the database and the systems, since allpersistence operations and data reading is actually performedby the queries generated by this framework that maps objectsautomatically for the relational database.
Interrogator
InterAB
Database
Sensor
TCP/IP
JDBC
Web ApplicationLaptop
JDBC
Fig. 1. Integration between InterAB and Web-based System.
InterAB and WbS can ensure high flexibility in the opticalsensor networks monitoring. This flexibility is made feasibledue to the fact that these applications easily adapt to themost types of interrogators and sensors. For interrogators isnecessary to know its communication protocol and samplingrate. And for optical sensors must be known its referencewavelength and calibration equation.
III. ACQUISITION, PROCESSING AND PERSISTENCE:INTERAB SYSTEM
The InterAB System is an Java application responsiblefor communication, acquisition, filtering and data persistence.The InterAB System implements the optical interrogator’scommunication protocol. The InterAB operation is shown inFigure 2. The application interacts via TCP/IP socket andthreads with optical interrogators, which collects the samplesgenerated by the sensor network, in order to receive thereal-time monitoring data. The samples are wavelengths shiftsthat indicate changes in the measured quantity by the sensor.The InterAB then processes the data that is transmitted bythe interrogator in a format SCPI (Standard Commands forProgrammable Instruments) and the system communicateswith the database through JDBC connection (Java DatabaseConnectivity) to persist the filtered samples from each sensor.Additionally, InterAB can apply, before database persistencestep, filtering techniques depending on the quantity measuredby the sensor: filtering technique based on the variations inwavelength that proposed for data from FBG temperature
sensors or filtering technique based on activity that suggestedfor data from FBG acceleration and strain sensors. A reviewabout the filtering techniques is presented in [14].
Interrogator 1
InterAB
Database
Interrogator 2
Sensor
Thread 1
Thread 2
Interrogator NThread N
JDBC
Fig. 2. InterAB System performing data acquisition from multiple opticalinterrogators.
Since the data are stored in the database any desktop or webapplication can retrieve this information for various purposes,such as visualization, decision making and diagnosis.
IV. SECURITY, PROCESSING AND REAL-TIMEVISUALIZATION: WEB-BASED SYSTEM
For the monitoring of a target structure was developed aWeb-based application totally independent of the acquisition,processing and persistence system, so that only the informationpresent in the database is required. The WbS developmenton the Java programming language in conjunction with theJSF (JavaServer Faces) [15] front-end framework inheritingthe flexibility, integration, mobility and security afforded bysuch platforms, once the application becomes visible from anyequipment that has Internet connection.
The WbS is composed of a security mechanismimplemented by Spring Security Web framework that performsauthentication checks and login so that the most critical systemareas are restricted to people with certain privilege types.Once the communication is established the user can edit theoptical sensor network settings and procedes tasks such asdata analysis and visualization, and apply techniques for faultsdetection on the system. The PrimeFaces [16] Web frameworkcoupled to Web application implementing statistical graphs,tables, forms and computational analysis tools.
Through virtual version of the optical sensor networkpersisted in the database by InterAB, the WbS creates aWeb version of the sensor network physical topology, whichis an interactive and automatically updatable graphs thatdescribes the current optical sensor network architecture. Eachsensor type has a specific conversion formula, which is calledcalibration equation, that converts the peak wavelength ofthe FBG sensor to its corresponding measurement. Theseconversions are performed using first or second orderequations that are provided by datasheets.
3
The WbS and all features mentioned above are generallyexemplified in the following features diagram shown inthe Figure 3. The diagram illustrates the four layers inthe WbS: visualization layer for user interaction, processinglayer and business rule layer to perform the treatment anddata management, and communication layer (Hibernate) formanipulation and data persistence in the database.
Interface Processing
Try login
Authentication
Monitoring results
Show results
System management
Sucess/failure
Request query
Request query
Business Rule
Select query
Data
Select query
Data
Insert/Delete query
Answer
DAO Hibernate
Database
Request query
Fig. 3. WbS features diagram.
V. RESULTS
Aiming to present the main features of the InterABSystem and WbS, two tests were proposed in differentenvironments. The first environment is a laboratory test,where temperature is measured under controlled conditions.The second environmentis is a real test performed on metalwalkway with approximately 40 m long. The measurementscollected were temperature, strain and acceleration from 8sensors installed along the metal walkway. Each environmentused different interrogator equipments and set of sensors, asshown in Table I.
A. Test in a laboratoryThe scheme for the test is presented in Figure 4. This
scheme presents a temperature sensor in the three channelsof the optical interrogator and a strain sensor in the otherchannel. The test carried out the temperature monitoring bythree sensors (WTS) and the strain monitoring by one sensor(WSS). The sensor network topology recognized by InterABand visualized by WbS is shown in Figure 5.
Fig. 4. Scheme for test in a laboratory.
Fig. 5. Sensor network topology recognized by InterAB.
The collected, processed and data persisted by InterABcould be visualized by WbS in Figure 6. In this test theInterAB System performs filtering on data generated by opticalsensors. A variation in the peak wavelengths of the temperaturesensors occurs due to a variation in the temperature. Thefiltering was performed only on data that were consistent withcertain wavelength variation set up previously. The graphicsshow the real-time variation of the data collected, beingupdated automatically with the last new one hundred samplessaved in database.
Samples
Tem
pe
ratu
re (
ºC)
WTS_1
WTS_2
WTS_3
26.0
25.8
25.6
25.4
25.2
25.0
24.8
24.6
24.4
WTS_3
WTS_1
WTS_2
Fig. 6. Visualization provided by WbS.
B. Test in a real environment
The real testing environment is characterized by a metalwalkway with approximately 40 m long. The walkway and
4
examples of FBG sensors are shown in Figure 7. An opticalcable of approximatelly 500 m connects the sensor networkinstalled in the walkway with the interrogator located in thelaboratory. The structural monitoring is carried out by a opticalsensor network composed of three temperature sensors, threestrain sensors and two acceleation sensors.
Laboratory
FC/APC conectorProtection
box
Fig. 7. Scheme for test in a real environment.
This test presents the features export and preview dataprovided by WBS. The screen regarding this functionalityis shown in Figure 8. The user selects the sensor types andchooses the intervals to select the data. Using the export buttonthe data are saved in an export file format recognized byOctave, Matlab, R and Weka. The update button shows apreview of data that are available for download, as can beseen in Figure 9.
Fig. 8. Export and data preview.
VI. FINAL REMARKS AND FUTURE WORKS
The paper presented a monitoring system for optical sensornetworks composed of InterAB and WbS which togetherperform the acquisition, processing, persistence, managementand visualization of data obtained from the interrogationsystems. The results obtained during tests in a laboratory andin a real environment demonstrate the efficiency, robustnessand flexibility of the system for different types of sensors,optical interrogators and environments, ensuring atomicity,consistency, isolation and durability of persisted data byInterAB and displayed by WbS. For the next steps we intend tointegrate the system features such as fault detection, structuraldamage detection, new filtering techniques and develop a Webservice that can perform a more robust data management.
Acc
ele
ratio
n (
g)
0.4
0.6
0.8
1.0
-1.0
-0.8
-0.4
-0.6
Samples
ACC_1
Fig. 9. Preview of accelerometer sensor data.
REFERENCES
[1] V. Vaidehi and D. Sharmila Devi. Distributed database management andjoin of multiple data streams in wireless sensor network using queryingtechniques. In International Conference on Recent Trends in InformationTechnology (ICRTIT), pages 583–588, 2011.
[2] H. Kawashima. A database infrastructure for supporting applicationsof ubiquitous sensor networks. In 5th International Conference onNetworked Sensing Systems (INSS), pages 252–252, 2008.
[3] Jiang Dong, Tian Mao, Liu Wen-Chao, and Pan Yong-Cai. Design andimplementation of a novel wireless sensor network system terminalbased on embedded web server and database. In InternationalConference on Automatic Control and Artificial Intelligence (ACAI),pages 772–775, 2012.
[4] S. Madden. Database abstractions for managing sensor network data.Proceedings of the IEEE, 98(11):1879–1886, 2010.
[5] D. Tosi, M. Olivero, G. Perrone, A. Vallan, and L. Arcudi. Simple fiberBragg grating sensing systems for structural health monitoring. In IEEEWorkshop on Environmental, Energy, and Structural Monitoring Systems(EESMS), pages 80–86, 2009.
[6] P. Antunes, H. Lima, H. Varum, and P. Andre. Static and dynamicstructural monitoring based on optical fiber sensors. In 12thInternational Conference on Transparent Optical Networks (ICTON),pages 1–4, 2010.
[7] N. Yang. Technologies for structural test and monitoring: The modernapproach. In IEEE AUTOTESTCON, pages 1–5, 2010.
[8] R. Adhami. Autonomous structural monitoring using fiber bragg grating.In International Conference on Computer Systems and IndustrialInformatics (ICCSII), pages 1–4, 2012.
[9] M.W. Schiller, P. Lopes, J.L. Pinto, and H.J. Kalinowski. Web controlledinterrogation system for fiber bragg gratings. In Microwave andOptoelectronics Conference, IMOC 2003. Proceedings of the 2003SBMO/IEEE MTT-S International, volume 2, pages 647–649, 2003.
[10] P. Morreale and R. Suleski. System design and analysis of aweb-based application for sensor network data integration and real-timepresentation. In 3rd IEEE Systems Conference, pages 201–204, 2009.
[11] R. A. Costa and C. E. Cugnasca. Use of Data Warehouse to Manage Datafrom Wireless Sensors Networks That Monitor Pollinators. In EleventhInternational Conference on Mobile Data Management (MDM), pages402–406, 2010.
[12] Nuno M. F. Goncalves, Aldri L. dos Santos, and Carmem S. Hara.DYSTO - A Dynamic Storage Model for Wireless Sensor Networks.Journal of Information and Data Management, 3(3):147–162, 2012.
[14] A. D. F. Santos, M. J. Sousa, C. S. Sales, M. F. M. Silva, C. S. Fernandes,and J. C. W. A. Costa. Filtering Techniques for Data Persistence in FBGOptical Sensor Networks. In Simposio Brasileiro de Banco de Dados(SBBD), pages 31–36, 2013.
