My City Dashboard: Real-time Data Processing Platform for Smart Cities

• Autorzy: Usurelu C.-C. , Pop C.
• Języki publikacji: EN

Abstrakty

EN

In recent years, with the increasing popularity of IoT, the rapid growth of smartphone usage enabled by the increase adoption of Internet services and the continuously decreasing costs of these devices and services has led to a huge increase in the volume of data that governments can use in the context of smart city initiatives. The need for analytics is becoming a requirement for smart city projects such as city dashboards to provide citizens with an easy to understand overview of the city. As such, data should be analyzed, reduced and presented in such a way that citizens can easily understand various aspects of the city and use this information to increase quality of life. In this paper, we firstly present the context and the start of the design and implementation of proposed solution for real-time data processing in smart cities, mainly an analytics processing pipeline and a dashboard prototype for this solution, named My City Dashboard. We focus on high scalability and modularity of this platform.

Słowa kluczowe

EN

big data; data analytics; real-time processing; smart city; statistics

• Wydawca: Instytut Łączności - Państwowy Instytut Badawczy
• Czasopismo: Journal of Telecommunications and Information Technology
• Rocznik: 2017
• Tom: nr 1
• Strony: 89--100
• Opis fizyczny: Bibliogr. 33 poz., rys., tab.

Twórcy

autor

Usurelu C.-C.

• University Politehnica of Bucharest Faculty of Automatic Control and Computers Computer Science Department Splaiul Independentei 313, Sector 6 Bucharest 060042, Romania

autor

Pop C.

• University Politehnica of Bucharest Faculty of Automatic Control and Computers Computer Science Department Splaiul Independentei 313, Sector 6 Bucharest 060042, Romania
• National Institute for Research and Development in Informatics (ICI) 8-10, Mares¸al Averescu 011455 Bucharest, Romania

Bibliografia

• [1] M. D’Arienzo, M. Iacono, S. Marrone, and R. Nardone, “Estimation of the energy consumption of mobile sensors in WSN environmental monitoring application”, in Proc. 27th Int. Conf. on Adv. Inform. Netw. & Appl. Worksh. WAINA 2013, Barcelona, Spain, 2013, pp. 1588–1593.
• [2] K. Benouaret, R. Valliyur-Ramalingam, and F. Charoy, “CrowdSC: Building smart cities with large-scale citizen participation”, IEEE Internet Comput., vol. 17, no. 6, pp. 57–63, 2013.
• [3] D. Merezeanu, C. Vasilescu, and R. Dobrescu, “Context-aware Control Platform for Sensor Network Integration in IoT and Cloud, Studies in Informatics and Control, vol. 25, no. 4, pp. 489–498, 2016.
• [4] G. Motta, L. You, D. Sacco, and T. Ma, “CITY FEED: A crowdsourcing system for city governance”, in Proc. IEEE 8th Int. Symp. on Service Oriented System Engin. SOSE 2014, Oxford, UK, 2014, pp. 439–445.
• [5] ArcGIS GeoEvent Server, “Real-Time Mapping and Analytics” [Online]. Available: http://www.esri.com/software/arcgis/arcgisserver/ extensions/geoevent-extension (accessed on Jan. 2017).
• [6] A. Moralis, G. Perreas, A. Glaros, and D. Dres, “Search-the-City – A versatile dashboard for searching and displaying Environment and User Generated Content in the context of the future Smart City”, in Proc. Information Access in Smart Cities i-ASC 2014, Amsterdam, The Netherlands, 2014 [Online]. Available: http://dcs.gla.ac.uk/ workshops/iASC2014/papers/iasc2014 moralis.pdf
• [7] S. Suakanto, S. H. Supangkat, and R. Saragih, “Smart city dashboard for integrating various data of sensor networks”, in Proc. Int. Conf. on ICT for Smart Society ICISS 2013, Jakarta, Indonesia, 2013.
• [8] Amsterdam City Dashboard [Online]. Available: http://citydashboard.waag.org (accessed on Jan. 2017).
• [9] C. Fratila, C. Dobre, F. Pop, and V. Cristea, “A transportation control system for urban environments”, in Proc. 3rd In. Conf. on Emerg. Intell. Data and Web Technol. EIDWT 2012, Bucharest, Romania, 2012, pp. 117–124.
• [10] C. Gosman, T. Cornea, C. Dobre, F. Pop, and A. Castiglione, “Controlling and filtering users data in Intelligent Transportation System”, Future Gener. Comp. Syst., in press, 2015 (doi: 10.1016/j.future.2016.12.014).
• [11] CitySDK Linked Data API [Online]. Available: http://dev.citysdk.waag.org (accessed on January 2017).
• [12] CitySDK Linked Data API Source Code [Online]. Available: https://github.com/waagsociety/citysdk-ld (accessed on Jan. 2017).
• [13] Dublin Dashboard [Online]. Available: http://www.dublindashboard.ie (accessed on Jan. 2017).
• [14] The Programable City project [Online]. Available: http://www.maynoothuniversity.ie/ (accessed on Jan. 2017).
• [15] R. Kitchin, Rob, T. P. Lauriault, and G. McArdle, “Knowing and governing cities through urban indicators, city benchmarking and real-time dashboards”, Regional Studies, Regional Science, vol. 2, no. 1, pp. 6–28, 2015 (doi: 10.1080/21681376.2014.983149).
• [16] Dublin data sharing platform [Online]. Available: http://dublinked.ie/ (accessed on Jan. 2017).
• [17] Dublin Dashboard challenges [Online]. Available: http://smartdublin.ie/ smartstories/dublin-dashboard (accessed on Jan. 2017).
• [18] London Dashboard [Online]. Available: http://citydashboard.org (accessed on Jan. 2017).
• [19] F. Roumpani, O. O’Brien, and A. Hudson-Smith, “Creating, visualizing and modelling the realtime city” [Online]. Available: http://casa.oobrien.com/ misc/presentations/roumpani2012a.pdf (accessed on Jan. 2017).
• [20] Smart Cities – City DashBoards DashBoards Lecture [Online]. Available: http://www.spatialcomplexity.info/files/2013/06/ Session-5-Lecture-2.pdf (accessed on Jan. 2017).
• [21] London Dashboard API [Online]. Available: http://oobrien.com/ 2012/06/citydashboard-the-api/ (accessed on Jan. 2017).
• [22] Dubai Personal Dashboard [Online]. Available: https://pdb.24x7dcd.ae/portal/dashboards/personal-dashboard (accessed on Jan. 2017).
• [23] Dubai Civil Defence presentation [Online]. Available: http://24x7dcd.ae/pdf/DCD-Personal-Dashboard-23-Oct-2015English.pdf (accessed on Jan. 2017).
• [24] S. Suakanto, S. H. Supangkat, Suhardi, and R. Saragih, “Smart city dashboard for integrating various data of sensor networks”, in Proc. Int. Conf. on ICT for Smart Society ICISS 2013, Jakarta, Indonesia, 2013.
• [25] D. Lee et al., “CityEye: Real-time visual dashboard for managing urban services and citizen feedback loops”, in Proc. 14th Int. Conf. on Comp. in Urban Plann. & Urban Manag. CUPUM 2015, Cambridge, MA USA, 2015.
• [26] V. Serbanescu, F. Pop, V. Cristea, and G. Antoniu, “Architecture of distributed data aggregation service”, in Proc. IEEE 28th Int. Conf. on Adv. Inform. Netw. & Appl. AINA 2014, Victoria, Canada, 2014, pp. 727–73.
• [27] E. Barbierato, M. Iacono, and S. Marrone, “PerfBPEL: A graphbased approach for the performance analysis of BPEL SOA applications”, in Proc. 6th Int. Conf. on Perform. Eval. Methodol. & Tools VALUETOOLS 2012, Carg`ese, France, 2012, pp. 64–73.
• [28] E. Barbierato, M. Gribaudo, M. Iacono, and S. Marrone, “Performability modeling of exceptions-aware systems in multiformalism tools”, in Proc. Int. Conf. on Anal. & Stoch. Model. Techniq. & Appl. ASMTA 2011, Venice, Italy, 2011, pp. 257–272.
• [29] C. Esposito, A. Castiglione, F. Palmieri, M. Ficco, and K. K. R. Choo, “A publish/subscribe protocol for event-driven communications in the Internet of Things”, in Proc. IEEE 14th Int. Conf. on Depend., Autonom. & Secure Computing DASC 2016, IEEE 14th Int. Con. on Perv. Intelligence & Computing PICom 2016, IEEE 2nd Int. Conf. on Big Data Intelligence & Computing DataCom 2016, IEEE Cyber Sci. & Technol. Congr. CyberSciTech 2016 (DASC-PICom-DataCom-CyberSciTec 2016), Auckland, New Zealand, 2016, pp. 376–383.
• [30] C. Esposito, M. Ficco, F. Palmieri, and A. Castiglione, “A knowledge-based platform for Big Data analytics based on publish/subscribe services and stream processing”, J. of Knowledge-Based Syst., vol. 79, pp. 3–17, 2015 (doi: 10.1016/j.knosys.2014.05.003).
• [31] C. Esposito, A. Castiglione, and K. K. R. Choo, “Challenges in Delivering Software in the Cloud as Microservices”, IEEE Cloud Comput., vol. 3, no. 5, pp. 10–14, 2016.
• [32] J. Kreps, N. Narkhede, and J. Rao, “Kafka: A distributed messaging system for log processing”, in Proc. 6th Int. Worksh. on Netw. Meets Databs. NetDB 2011, Athens, Greece, 2011.
• [33] Cluster Griddy [Online]. Available: http://maplarge.com/ visual/clustering (accessed on Jan. 2017).

Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).