In this paper author describes his research with the goal to develop and experimentally
verify specific data recording and processing methodologies based on participatory sensing approach implementation in mobile vehicle based sensor networks. To reach this goal, author performed study of literature, testing of hypothesis using general purpose computer devices, adaptation of smartphones for participatory sensing, development of special purpose embedded devices, practical experiments with selected technical equipment and software as well as gathering of experimental results and following statistical analysis. The result of this research are several data acquisition and processing methodologies based on participatory sensing approach and mobile vehicle based sensor networks as well as evaluation of these methodologies

Mednis, Artis

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Baltic J. Modern Computing, Vol. 1 (2013), No. 1-2, pp. 1–8Implementation ofParticipatory Sensing Approachin Mobile Vehicle Based Sensor NetworksArtis MEDNISCyber-Physical Systems Laboratory,Institute of Electronics and Computer Science,14 Dzerbenes Str., Riga, LV 1006, Latviaartis.mednis@edi.lvAbstract. In this paper author describes his research with the goal to develop and experimen-tally verify specific data recording and processing methodologies based on participatory sensingapproach implementation in mobile vehicle based sensor networks. To reach this goal, author per-formed study of literature, testing of hypothesis using general purpose computer devices, adapta-tion of smartphones for participatory sensing, development of special purpose embedded devices,practical experiments with selected technical equipment and software as well as gathering of ex-perimental results and following statistical analysis. The result of this research are several dataacquisition and processing methodologies based on participatory sensing approach and mobilevehicle based sensor networks as well as evaluation of these methodologies.Keywords: participatory sensing, mobile sensor networks, methodologies, microphones, ac-celerometers1 IntroductionThe modern world is characterized by the need to take more and more decisions. Thedecision would be more objective if it would be done based on appropriate amountof information. Further, the information can be obtained by collecting, combining andinterpreting certain amount of data.Decision-making can be based both on directly acquired operational data that de-scribes current situation and indirectly acquired historical data that describes similarsituations in the past. The usage of historical data in current decision-making processtheoretically increases the possibility of more objective assessment of the situation andhelps to take appropriate decision.Humans’ ability to process data and to make decisions without usage of additionaltechnical solutions, unfortunately, has some limits determined by both the physiological2 A. Mednischaracteristics and the amount of available data. Consequently, there is the possibilityfor the following improvements:1. Collection and initial processing of operational data – in this case data volume forhumans’ interpretation is reduced;2. Collection and storage of historical data – in this case the database for later analysisof the situation is created.One of the possible solutions for these improvements is the usage of mobile sensornetworks. To facilitate development of such networks, often already existing mobileobjects, for example, vehicles travelling regular (De Zoysa et al., 2007) or irregular(Eriksson et al., 2008) routes are used for sensor deployment. At the time when researchactivities described in this paper were started, such networks were characterized bysuch aspects as specific hardware platforms, necessity for additional external sensors aswell as transmission of collected data without initial processing. Therefore mentionedapproach was potentially suitable for science oriented data recording and processingexperiments.Another solution for these improvements is the usage of participatory sensing ap-proach characterized by interactive sensor networks formed by everyday mobile equip-ment and allowing collection, processing and sharing of environmental data (Burke etal., 2006). Significant aspects of such approach are general purpose hardware platforms,usage of available internal sensors, initial processing of collected data with the aim toreduce volume of transmitted data and therefore minimize expenses for maintaining ofeach separate data source as well as potential application for automatic execution ofpractically oriented tasks.At the time when research activities described in this paper were started, such par-ticipatory sensing solutions were just emerging and their application was related toother domains but not mobile vehicle based sensor networks. Therefore it was decidedto verify the possibility of combination of both mentioned approaches into one newapproach.The remainder of the paper is organized as follows. Section 2 discusses monitoringof road surface using mobile sensor networks with microphones. Section 3 introducesthe application of smartphones with Android OS as alternative solution for the sametask. Section 4 is dedicated to special purpose embedded device that implements bothprevious described methodologies. Section 5 reviews the main results of the research.In Section 6 the conclusion about possibilitty of mobile vehicle based sensor networksbuilt as implementation of participatory sensing approach is given.2 Monitoring of road surface using mobile sensor networks withmicrophonesThe analysis of the road surface including damages of the road surface is important forroad maintainers as well as for drivers. One of the options to collect such informationis mobile vehicle based sensor networks.Implementation of Participatory Sensing Approach 3The goal of this particular sub research was verifying of the possibility to collectthe information about road surface and its damages using vehicles equipped with mi-crophones for data collection, Global Navigation Satellite System (GNSS) devices formetadata collection, and general purpose mobile computer devices for data process-ing and storage. In the case of an affirmative result this approach could be extendedusing other sensors for detection of other events. During this sub research sound datarecording and processing was performed using general purpose hardware and softwareas well as influence of particular data processing parameters on results of detection ofroad surface damages analyzed.The result of this particular sub research is the methodology evaluated using 5classes of typical road surface irregularities – big potholes, small potholes, potholeclusters, gaps and drain pits (Figure 1). The detection of previously marked road sur-face damages was performed using thresholding of sound data with different thresholdlevels from 15% to 90%. The results showed that such approach allows to detect up to80% of pothole clusters, up to 90% of small potholes and virtually all big potholes.Fig. 1. Statistics of detected road surface damages based on analysis of recorded sound signalsusing different threshold levels (Mednis et.al., 2010)Gaps and drain pits were detected less successfully (respectively, up to 60% and upto 40%), but these road surface irregularities only occassionally could be classified asreal road surface damages. The acoustic background inside the vehicle has little effecton the application of methodology.4 A. MednisDuring this particular sub research following conclusion was drawn – the usage ofrelatively simple sensors and simple data processing methodology allows the collec-tion of data with practical application that corresponds to implementation of particularsensing approach. Additionally this sub research encouraged the development of spe-cial purpose embedded device described in Section 4 of the paper. The detailed resultsof this particular sub research are published in (Mednis et al., 2010).3 Monitoring of road surface using smartphones withaccelerometersThe importance of the road infrastructure for the society could be compared with im-portance of blood vessels for humans. To ensure road surface quality it should be mon-itored continuously and repaired as necessary. The optimal distribution of resources forroad repairs depends on the availability of comprehensive and objective real time dataabout the state of the roads. Participatory sensing is a promising approach for such datacollection.The goal of this particular sub research was development of a mobile solution forroad surface monitoring based on vehicular sensor networks established through smart-phones with internal accelerometers and Android operating system. During this subresearch development of the data processing algorithms capable to operate using onlyresources available in smartphones, optimization of the parameters of algorithms, andevaluation of the algorithms using real world data collected using different smartphoneswas performed.The results of this particular sub research are 4 accelerometer data processing algo-rithms Z-THRESH, Z-DIFF, STDEV(Z) un G-ZERO intended for usage in devices withlimited hardware and software resources and capable to detect up to 90% of previouslymarked road surface damages (Table 1).Table 1. Statistics of detected road surface damages based on analysis of accelerometer datausing different algorithms (Mednis et al., 2011)Road surface damage class Z-THRESH Z-DIFF STDEV(Z) G-ZEROBig potholes 3 (100%) 3 (100%) 3 (100%) 3 (100%)Small potholes 15 (83%) 16 (89%) 16 (89%) 14 (78%)Pothole clusters 25 (83%) 27 (90%) 27 (90%) 27 (90%)Gaps 31 (78%) 36 (90%) 30 (75%) 27 (68%)Drain pits 10 (59%) 17 (100%) 11 (65%) 8 (47%)Altogether 84 (78%) 99 (92%) 87 (81%) 79 (73%)During this particular sub research following conclusion was drawn – smartphonesas general purpose devices with internal sensors optimally meet requirements for im-plementation of participatory sensing but performance of developed algorithms is anaffirmation for main research goal. The detailed results of this particular sub researchare published in (Mednis et al., 2011).Implementation of Participatory Sensing Approach 54 Embedded device for road condition monitoringEmbedded device described in (Mednis et al., 2012) was developed with the aim to ob-tain the tool for road surface monitoring using microphone and accelerometers as wellas collection of meteorological data that could be useful for creation of detailed roadmeteorological information maps. Mobile sensing platform in the form of an embeddeddevice allows execution of larger experiments because it can be operatively deployedon different vehicles and therefore suitable for experiments without direct participa-tion of scientific staff. Significant encouragement for development of this embeddeddevice were previously developed road surface monitoring methodologies using gen-eral purpose computer devices and Android smartphones described in the Section 2 andSection 3 of the paper as well as the challenge to implement these methodologies inspecial purpose embedded device.Embedded device (Figure 2) consists of the main module (A) – wireless sensornetwork node Tmote Mini, Inertial Measurement Unit (IMU) Analog Combo Board,sensors SHT15 and TEMT6000, voltage regulators and SD memory card, as well asWi-Fi (B), Bluetooth (C), microphone (D) un GNSS (E) add-on modules.Fig. 2. The main module and add-on modules of developed embedded device6 A. Mednis5 ResultsResearch activities carried out with the aim to verify the possibility of mobile vehiclebased sensor networks built as implementation of participatory sensing approach anddescribed in the thesis led to following results:1. Verified what information about road surface and its damages can be collected us-ing general purpose mobile computer devices and acoustic sensors. As a result,the methodology for road surface monitoring using mobile sensor networks withmicrophones was developed.2. Verified what information about road surface and its damages can be collected usinggeneral purpose devices with internal sensors. As a result, the methodology for roadsurface monitoring using smartphones with accelerometers was developed.3. Using as the basis developed methodologies, implementation of prototypes wasperformed.4. Using as the basis developed prototypes, practical experiments in data collectionand processing were carried out.5. Using as the basis statistical analysis of the data collected during practical experi-ments, evaluation of the developed methodologies was performed.6 ConclusionThe results of the research activities described in this paper led to the conclusion aboutpossibility of mobile vehicle based sensor networks built as implementation of partici-patory sensing approach.The title of this paper consists of two subjects – mobile vehicle based sensor net-works as well as participatory sensing approach for data recording, initial processing,collection and storage. At the time of beginning of research activities it was found thatthere exist individual methodologies with individual attributes, for example, usage ofmobile sensors for data collection, that allow to classify these methodologies as adja-cent for usage in mobile vehicular sensor networks. At the same time implementationof these methodologies contained individual aspects that significantly limited or evenmade impossible their application in the context of participatory sensing, for example,specific hardware platforms, usage of external sensors and transmission of all collecteddata without the initial processing.RoadMic and Potroid methodologies described in this paper are based on generalpurpose hardware platforms – PC and smartphones with Android OS, therefore thenecessity for specific hardware platforms characteristic for BikeNet (Eisenman et al.,2007), BusNet (De Zoysa et al., 2007), Pothole Patrol (Eriksson et al., 2008) method-ologies is eliminated. At the same time developed methodologies can be implementedin special purpose sensor node. The usage of simple internal sensors – microphoneand accelerometer, allows to reduce the number of components necessary for the im-plementation of the methodology compared to BikeNet, BusNet, Pothole Patrol andNericell (Mohan et al., 2008) methodologies where external sensors are used. Method-ologies are oriented towards execution of a practical task – automatic monitoring of roadImplementation of Participatory Sensing Approach 7surface that is out of scope of BikeNet and SoundSense (Lu et al., 2009) methodolo-gies. Further, locally performed initial data processing allows reduction of transmitteddata amount compared to BikeNet, BusNet and NTU (Tai et al., 2010) methodologies.Therefore RoadMic and Potroid methodologies described in this paper comply withinitial statement, given in Section 1, about attributes characteristic for mobile vehiclebased sensor networks built as implementation of participatory sensing approach. De-tailed comparison of the methodologies by mentioned attributes is given in Table 2.Table 2. Comparison of existing and developed mobile monitoring methodologies by their ad-equacy for particular application – road surface monitoring using mobile vehicle based sensornetworksMethodology Hardware platform Used sensorSensorplacementDataprocessinglocationApplication forroad surfacemonitoringBikeNet sensor nodea + smartphoneb microphone external remote –SoundSense smartphonec microphone internal locally –BusNet sensor noded accelerometer external remote +Pothole Patrol embedded computere accelerometer external locally +Nericell smartphonef accelerometer external locally +NTU smartphoneg accelerometer internal loc.+rem. +RoadMic PC/special sensor node microphone internal locally +Potroid smartphoneh/sp. sensor node accelerometer internal locally +aTmote Invent bNokia N80 cApple iPhone dCrossbow MICAz eSoekris 4801 fwith Windows Mobile OS gHTC Diamondhwith Android OSAcknowledgementsThis work was sequentially supported by European Social Fund grants Nr. 2009/0219/1DP/1.1.1.2.0/APIA/VIAA/020 ”R&D Center for Smart Sensors and Networked Em-bedded Systems”, Nr. 2011/0054/1DP/1.1.2.1.2/11/IPIA/VIAA/002 ”Support for Doc-toral Studies at University of Latvia - 2” and Latvian National Research Program ”De-velopment of innovative multi-functional material, signal processing and informationtechnologies for competitive and research intensive products”. Special thanks to myIECS colleagues Atis Elsts, Andris Gordjusins, Georgijs Kanonirs, Martins Liepins,Leo Selavo, Girts Strazdins and Reinholds Zviedris for the collaboration during per-forming of varied research activities.ReferencesBurke, J., Estrin, D., Hansen, M., Parker, A., Ramanathan, N., Reddy, S., Srivastava, M. B. (2006).Participatory sensing, In: Proceedings of the 2006 ACM Workshop on World-Sensor-Web:Mobile Device Centric Sensor Networks and Applications (WSW ’06) (31 Oct. 2006, Boulder,CO, USA), 117–134.8 A. MednisDe Zoysa, K., Keppitiyagama, C., Seneviratne, G. P., Shihan, W. W. A. T. (2007). A public trans-port system based sensor network for road surface condition monitoring, In: Brewer, E., Saif,U. (Eds.), Proceedings of the 2007 ACM Workshop on Networked Systems for DevelopingRegions (NSDR ’07) (27 Aug. 2007, Kyoto, Japan), ACM, 9:1–9:6.Eisenman, S. B., Miluzzo, E., Lane, N. D., Peterson, R. A., Ahn, G.-S., Campbell, A. T. (2007).The BikeNet mobile sensing system for cyclist experience mapping, In: Jha, S. (Ed.), Pro-ceedings of the 5th ACM International Conference on Embedded Networked Sensor Systems(SenSys ’07) (6-9 Nov. 2007, Sydney, Australia), ACM, 87–101.Eriksson, J., Girod, L., Hull, B., Newton, R., Madden, S., Balakrishnan, H. (2008). The PotholePatrol: using a mobile sensor network for road surface monitoring, In: Grunwald, D., Han,R., De Lara, E., Ellis, C. S. (Eds.), Proceedings of the 6th ACM International Conference onMobile Systems, Applications, and Services (MobiSys ’08) (17-20 Jun. 2008, Breckenridge,CO, USA), ACM, 29–39.Lu, H., Pan, W., Lane, N. D., Choudhury, T., Campbell, A. T. (2009). SoundSense: scalablesound sensing for people-centric applications on mobile phones, In: Zielinski, K., Wolisz,A., Flinn, J., LaMarca, A. (Eds.), Proceedings of the 7th ACM International Conferenceon Mobile Systems, Applications, and Services (MobiSys ’09) (22-25 Jun. 2009, Krako´w,Poland), ACM, 165–178.Mednis, A., Elsts, A., Selavo, L. (2012). Embedded solution for road condition monitoring usingvehicular sensor networks, In: Proceedings of the 6th International Conference on Applica-tion of Information and Communication Technologies (AICT 2012) (17-19 Oct. 2012, Tbilisi,Georgia), IEEE, 1–5.Mednis, A., Strazdins, G., Liepins, M., Gordjusins, A., Selavo, L. (2010). RoadMic: Road surfacemonitoring using vehicular sensor networks with microphones, In: Zavoral, F., Yaghob, J.,Pichappan, P., El-Qawasmeh, E. (Eds.), Networked Digital Technologies, Vol. 88 of Commu-nications in Computer and Information Science, Springer Berlin Heidelberg, 417–429.Mednis, A., Strazdins, G., Zviedris, R., Kanonirs, G., Selavo, L. (2011). Real time pothole de-tection using Android smartphones with accelerometers, In: Proceedings of 7th IEEE Inter-national Conference on Distributed Computing in Sensor Systems and Workshops (DCOSS’11) (27-29 Jun. 2011, Barcelona, Spain), IEEE, 1–6.Mohan, P., Padmanabhan, V. N., Ramjee, R. (2008). Nericell: rich monitoring of road and trafficconditions using mobile smartphones, In: Abdelzaher, T. F., Martonosi, M., Wolisz, A. (Eds.),Proceedings of the 6th ACM Conference on Embedded Network Sensor Systems (SenSys ’08)(5-7 Nov. 2008, Raleigh, NC, USA), ACM, 323–336.Tai, Y.-c., Chan, C.-w., Hsu, J. Y.-j. (2010). Automatic road anomaly detection using smart mobiledevice, In: Proceedings of the 2010 Conference on Technologies and Applications of ArtificialIntelligence (TAAI 2010) (18-20 Nov. 2010, Hsinchu, Taiwan), 1–8.Received March 13, 2013 , accepted May 30, 2013

Implementation of participatory sensing approach in mobile vehicle based sensor networks

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Implementation of participatory sensing approach in mobile vehicle based sensor networks

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