Research Article
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Mobile Data Collection in Smart City Applications: The Impact of Precedence-based Route Planning on Data Latency

Year 2020, , 22 - 34, 15.06.2020

İzzet Fatih Şentürk , Siratigui Coulıbaly

https://doi.org/10.38088/jise.713809
Cited By: 2

Abstract

Data collection is one of the key building blocks of smart city applications. Sheer number of sensors deployed across the city generate huge amount of data continuously. Due to their limited transmission range, sensors form a sensor network with a base station. The base station acts as a gateway between the network and the remote user and the generated data is collected by the base station. However, due to sensor locations and the transmission range the network may consist of several partitions. A typical solution is employing one or more mobile element(s) to collect data from partitions periodically. Mobile data collection enables intermittent connectivity between sensors and the base station. The major drawback of mobile data collection is increased data latency depending on the velocity of the mobiles. Another challenge is specifying importance for individual sensors in a smart city application. This study evaluates the impact of precedence-based routing of mobiles on data latency in a realistic manner through employing spatial data obtained from a geographic information system. Precedence levels for sensors are determined based on the amenity type of the building they monitor. Mobility of the mobiles is restricted with the drivable road network. The impact of the precedence-based routing according to total path length, maximum data collection delay, and the maximum data latency is evaluated. Obtained results indicate an increase in total path length up to 14% when precedence-based routing is applied. The results also suggest that precedence-based routing increases maximum data collection delay unless the amenity type has fewer points of interest to monitor.

Keywords

Smart City Wireless Sensor Network Data Collection Route Planning Data Latency

Supporting Institution

TÜBİTAK

Project Number

117E050

Thanks

This work was supported by the Scientific and Technical Research Council of Turkey (TUBITAK) under Grant No. EEEAG-117E050. Map data copyrighted OpenStreetMap contributors and available from https://www.openstreetmap.org

References

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  • [3] McKinsey Global Institute. (2018). Smart cities: Digital solutions for a more livable future. https://www.mckinsey.com/industries/capital-projects-and-infrastructure/our-insights/smart-cities-digital solutions-for-a-more-livable-future, Accessed: 02/04/2020.
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  • [16] Siriaraya, P., Takumi,K.,, Yukiko, K., and Shinsuke, N. (2018). Using Open Data to Create Smart Auditory based Pervasive Game Environments. In Proceedings of the 2018 Annual Symposium on Computer-Human Interaction in Play Companion Extended Abstracts, pp. 611-617. ACM.
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  • [19] Pasolini, G., Buratti,C., Feltrin, L., Zabini, F., Castro, C.D.,Verdone, R. and Andrisano, O. (2018). Smart city pilot projects using LoRa and IEEE802. 15.4 technologies. Sensors 18, no. 4: 1118.
  • [20] Senturk, I.F. (2017). A prescient recovery approach for disjoint msns. In 2017 IEEE International Conference on Communications (ICC).
  • [21] Gartner. (2020). Forecast: Internet of Things Endpoints and Associated Services, Worldwide, 2017. https://www.gartner.com/en/documents/3840665/forecast-internet-of-things-endpoints-and-associated-ser, Accessed: 02/04/2020.
  • [22] Centenaro, M.,Vangelista, L., Zanlla, A., and Zorzi, M.(2016). Long-range communications in unlicensed bands: The rising stars in the IoT and smart city scenarios. IEEE Wireless Communications, 23(5): 60-67.
  • [23] Rathorea, M.M., Awais, A., Anand, P., and Seungmin, R.(2016). Urban planning and building smart cities based on the internet of things using big data analytics. Computer Networks, 101: 63-80.
  • [24] Taleb, T., Sunny, D., Ksentini,A., Muddesar, I., and Hannu, F.(2017). Mobile edge computing potential in making cities smarter. IEEE Communications Magazine, 55(3): 38-43.
  • [25] Wikipedia (2020).Metropolitan Municipalities in Turkey.https://en.wikipedia.org Metropolitan _municipalities _in_Turkey. Accessed:02/04/2020.
  • [26] OR-Tools. https://developers.google.com/optimization, Accessed: 02/04/2020.

Year 2020, , 22 - 34, 15.06.2020

İzzet Fatih Şentürk , Siratigui Coulıbaly

https://doi.org/10.38088/jise.713809
Cited By: 2

Abstract

Project Number

117E050

References

  • [1] Senturk, I., F, and Gaoussou, Y. K. (2019). A New Approach to Simulating Node Deployment for Smart City Applications Using Geospatial Data, International Symposium on Networks, Computers and Communications (ISNCC), pp. 1-5. IEEE, 2019.
  • [2] Forbes. (2018). 5 areas where smart city technology improves quality of life. https://www.forbes.com/sites/insights-inteliot/2018/10/24/5-areas-where-smart-city-technology-improves quality-of-life/#dbe97e710f86, Accessed: 02/04/2020.
  • [3] McKinsey Global Institute. (2018). Smart cities: Digital solutions for a more livable future. https://www.mckinsey.com/industries/capital-projects-and-infrastructure/our-insights/smart-cities-digital solutions-for-a-more-livable-future, Accessed: 02/04/2020.
  • [4] Intel. (2018). Smart cities technologies give back 125 hours to citizens every year. https://newsroom.intel.com/wp-content/uploads/sites/11/2018/03/smart-cities-whats-in-it-for-citizens.pdf, Accessed: 02/04/2020.
  • [5] Senturk, I.F., and Coulibaly, S. (2019). Priority-based Data Collection Framework for Smart Cities, 2nd International Conference on Data Science and Applications (ICONDATA’19), October 3-6, 2019, Edremit, Turkey, Proceedings 2019, pp. 192-196.
  • [6] Hadi, H., Boggio-Dandry,A., Qin, Z., Soyata, T., Kantarci, B., and Mouftah. H. T. (2018). Soft sensing in smart cities: Handling 3Vs using recommender systems, machine intelligence, and data analytics. IEEE Communications Magazine 56(2): 78-86. [7] Şentürk, İ. F., and Bilgin., M. (2018) Network Connectivity and Data Quality in Crowd-Assisted Networks. In Crowd Assisted Networking and Computing, pp. 137-159. CRC Press,
  • [8] Voigt, P., and Axel., V.D. B. (2017). The eu general data protection regulation (gdpr). A Practical Guide, 1st Ed., Cham: Springer International Publishing.
  • [9] Zhang., X. (2018). Design of a Novel Map POI Data Collection Model. In 2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018). Atlantis Press. [10] OpenStreetMap contributors. Planet dump retrieved from https://planet.osm.org. https://www.openstreetmap.org, Accessed: 02/04/2020.
  • [11] Google Maps Platform. https://developers.google.com/maps/documentation, Accessed: 02/04/2020.
  • [12] ArcGIS. https://www.arcgis.com/index.html, Accessed: 02/04/2020.
  • [13] Anahid, B., Haklay, M., Foody,G., and Mooney, P. (2019). Crowdsourced geospatial data quality: challenges and future directions. Pp: 1-6.
  • [14] Younis, M., Senturk, İ.F., Akkaya,K., Sookyoung, L., and Senel. F. (2014). Topology management techniques for tolerating node failures in wireless sensor networks: A survey. Computer Networks 58(2014): 254-283.Materials, 16(3):273–283.
  • [15] Vargas-Munoz, John E.,Marcos,D., Lobry, S., A. dos Santos, J., Falcão, A. X.., and Tuia,. D. (2018). Correcting misaligned rural building annotations in open street map using convolutional neural networks evidence. In IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, pp. 1284-1287. IEEE, 2018.
  • [16] Siriaraya, P., Takumi,K.,, Yukiko, K., and Shinsuke, N. (2018). Using Open Data to Create Smart Auditory based Pervasive Game Environments. In Proceedings of the 2018 Annual Symposium on Computer-Human Interaction in Play Companion Extended Abstracts, pp. 611-617. ACM.
  • [17] Queiroz, R., Thorsten, B., and Krzysztof, C. (2019). GeoScenario: An open dsl for autonomous driving scenario representation. In 2019 IEEE Intelligent Vehicles Symposium (IV), pp. 287-294. IEEE, 2019. [18] Boeing, G. (2017). OSMnx: New methods for acquiring, constructing, analyzing, and visualizing complex street networks. Computers, Environment and Urban Systems 65: 126-139.
  • [19] Pasolini, G., Buratti,C., Feltrin, L., Zabini, F., Castro, C.D.,Verdone, R. and Andrisano, O. (2018). Smart city pilot projects using LoRa and IEEE802. 15.4 technologies. Sensors 18, no. 4: 1118.
  • [20] Senturk, I.F. (2017). A prescient recovery approach for disjoint msns. In 2017 IEEE International Conference on Communications (ICC).
  • [21] Gartner. (2020). Forecast: Internet of Things Endpoints and Associated Services, Worldwide, 2017. https://www.gartner.com/en/documents/3840665/forecast-internet-of-things-endpoints-and-associated-ser, Accessed: 02/04/2020.
  • [22] Centenaro, M.,Vangelista, L., Zanlla, A., and Zorzi, M.(2016). Long-range communications in unlicensed bands: The rising stars in the IoT and smart city scenarios. IEEE Wireless Communications, 23(5): 60-67.
  • [23] Rathorea, M.M., Awais, A., Anand, P., and Seungmin, R.(2016). Urban planning and building smart cities based on the internet of things using big data analytics. Computer Networks, 101: 63-80.
  • [24] Taleb, T., Sunny, D., Ksentini,A., Muddesar, I., and Hannu, F.(2017). Mobile edge computing potential in making cities smarter. IEEE Communications Magazine, 55(3): 38-43.
  • [25] Wikipedia (2020).Metropolitan Municipalities in Turkey.https://en.wikipedia.org Metropolitan _municipalities _in_Turkey. Accessed:02/04/2020.
  • [26] OR-Tools. https://developers.google.com/optimization, Accessed: 02/04/2020.
There are 23 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

İzzet Fatih Şentürk 0000-0002-1550-563X

Siratigui Coulıbaly 0000-0003-4505-2739

Project Number 117E050
Publication Date June 15, 2020
Published in Issue Year 2020

Cite

APA Şentürk, İ. F., & Coulıbaly, S. (2020). Mobile Data Collection in Smart City Applications: The Impact of Precedence-based Route Planning on Data Latency. Journal of Innovative Science and Engineering, 4(1), 22-34. https://doi.org/10.38088/jise.713809
AMA Şentürk İF, Coulıbaly S. Mobile Data Collection in Smart City Applications: The Impact of Precedence-based Route Planning on Data Latency. JISE. June 2020;4(1):22-34. doi:10.38088/jise.713809
Chicago Şentürk, İzzet Fatih, and Siratigui Coulıbaly. “Mobile Data Collection in Smart City Applications: The Impact of Precedence-Based Route Planning on Data Latency”. Journal of Innovative Science and Engineering 4, no. 1 (June 2020): 22-34. https://doi.org/10.38088/jise.713809.
EndNote Şentürk İF, Coulıbaly S (June 1, 2020) Mobile Data Collection in Smart City Applications: The Impact of Precedence-based Route Planning on Data Latency. Journal of Innovative Science and Engineering 4 1 22–34.
IEEE İ. F. Şentürk and S. Coulıbaly, “Mobile Data Collection in Smart City Applications: The Impact of Precedence-based Route Planning on Data Latency”, JISE, vol. 4, no. 1, pp. 22–34, 2020, doi: 10.38088/jise.713809.
ISNAD Şentürk, İzzet Fatih - Coulıbaly, Siratigui. “Mobile Data Collection in Smart City Applications: The Impact of Precedence-Based Route Planning on Data Latency”. Journal of Innovative Science and Engineering 4/1 (June 2020), 22-34. https://doi.org/10.38088/jise.713809.
JAMA Şentürk İF, Coulıbaly S. Mobile Data Collection in Smart City Applications: The Impact of Precedence-based Route Planning on Data Latency. JISE. 2020;4:22–34.
MLA Şentürk, İzzet Fatih and Siratigui Coulıbaly. “Mobile Data Collection in Smart City Applications: The Impact of Precedence-Based Route Planning on Data Latency”. Journal of Innovative Science and Engineering, vol. 4, no. 1, 2020, pp. 22-34, doi:10.38088/jise.713809.
Vancouver Şentürk İF, Coulıbaly S. Mobile Data Collection in Smart City Applications: The Impact of Precedence-based Route Planning on Data Latency. JISE. 2020;4(1):22-34.

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