PAM-SAD: Ubiquitous Car Parking Availability Model Based on V2V and Smartphone Activity Detection

  • Walter BalzanoEmail author
  • Fabio Vitale
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 76)


GNSS based systems (like GPS or GLONASS) for outdoor localization are nowadays widespread in common smartphones. Vehicle-2-vehicle technology allows vehicles to communicate via wireless to share any kind of information and detect mutual distances via RSS-to-distance evaluation. Smartphones can be used to detect when an user switches between car driving, walking and several other kind of activities.

In this paper we discuss a novel methodology to discover nearest available street-parking spot using a smart combination of V2V, GNSS systems and smartphone-driven activity detection. In order to achieve system ubiquitousness across large areas (beyond the size of a single city), while still keeping low computation requirements, for localization purposes we only consider a fragment of the whole network of parked cars. Additionally we consider recognition of newly available parking clusters (for instance, in case of a fair or other kind of events) and disqualification of previously available spots (in case of work in progress or permanent street modifications). Smartphone activity detection is therefore used in tandem with V2V to mark new parking availability or occupancy based on user driving or walking away or toward the vehicle. When the user stops the car and starts walking, the vehicle is informed by the user smartphone of the context switch, and shares this positional information with nearby cars; when the user then goes back to his car and starts driving, the vehicle informs the local network of the newly available spot.


Parking V2V Activity detection LBS 


  1. 1.
    Xing, S., Tong, H., Ji, P.: Activity recognition with smartphone sensors. Tsinghua Sci. Technol. 19(3), 235–249 (2014)CrossRefGoogle Scholar
  2. 2.
    Ryu, U., Ahn, K., Kim, E., Kim, M., Kim, B., Woo, S., Chang, Y.: Adaptive step detection algorithm for wireless smart step counter. In: 2013 International Conference on Information Science and Applications (ICISA), pp. 1–4, June 2013Google Scholar
  3. 3.
    Amato, F., Moscato, F.: A model driven approach to data privacy verification in e-health systems. Trans. Data Priv. 8(3), 273–296 (2015)Google Scholar
  4. 4.
    Amato, F., De Pietro, G., Esposito, M., Mazzocca, N.: An integrated framework for securing semi-structured health records. Knowl.-Based Syst. 79, 99–117 (2015)CrossRefGoogle Scholar
  5. 5.
    Higgins, J.P.: Smartphone applications for patients’ health and fitness. Am. J. Med. 129(1), 11–19 (2016)CrossRefGoogle Scholar
  6. 6.
    Dhondge, K., Song, S., Choi, B.-Y., Park, H.:. WiFiHonk: smartphone-based beacon stuffed wifi car2x-communication system for vulnerable road user safety. In: 2014 IEEE 79th Vehicular Technology Conference (VTC Spring), pp. 1–5. IEEE (2014)Google Scholar
  7. 7.
    Ebert, A., Feld, S., Dorfmeister, F.: Segmented and directional impact detection for parked vehicles using mobile devices. In: 2016 International Conference on Systems, Signals and Image Processing (IWSSIP), pp. 1–4. IEEE (2016)Google Scholar
  8. 8.
    Aloul, F., Zualkernan, I., Abu-Salma, R., Al-Ali, H., Al-Merri, M.: iBump: smartphone application to detect car accidents. In: 2014 International Conference on Industrial Automation, Information and Communications Technology (IAICT), pp. 52–56. IEEE (2014)Google Scholar
  9. 9.
    Zhaohui, W., Qing, W., Cheng, H., Pan, G., Zhao, M., Sun, J.: ScudWare: a semantic and adaptive middleware platform for smart vehicle space. IEEE Trans. Intell. Transp. Syst. 8(1), 121–132 (2007)CrossRefGoogle Scholar
  10. 10.
    Altintas, O., Dressler, F., Hagenauer, F., Matsumoto, M., Sepulcre, M., Sommery, C.: Making cars a main ict resource in smart cities. In: 2015 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp. 582–587. IEEE (2015)Google Scholar
  11. 11.
    Li, J., An, Y., Fei, R., Wang, H.: Smartphone based car-searching system for large parking lot. In: 2016 IEEE 11th Conference on Industrial Electronics and Applications (ICIEA), pp. 1994–1998. IEEE (2016)Google Scholar
  12. 12.
    Alepis, E., Sakelliou, A.: Augmented car: a low-cost augmented reality rc car using the capabilities of a smartphone. In: 2016 7th International Conference on Information, Intelligence, Systems & Applications (IISA), pp. 1–7. IEEE (2016)Google Scholar
  13. 13.
    Jermsurawong, J., Ahsan, M.U., Haidar, A., Dong, H., Mavridis, N.: Car parking vacancy detection and its application in 24-hour statistical analysis. In: 2012 10th International Conference on Frontiers of Information Technology (FIT), pp. 84–90. IEEE (2012)Google Scholar
  14. 14.
    Houben, S., Komar, M., Hohm, A., Luke, S., Neuhausen, M., Schlipsing, M.: On-vehicle video-based parking lot recognition with fisheye optics. In: 2013 16th International IEEE Conference on Intelligent Transportation Systems-(ITSC), pp. 7–12. IEEE (2013)Google Scholar
  15. 15.
    Sauras-Perez, P., Gil, A., Taiber, J.: ParkinGain: toward a smart parking application with value-added services integration. In: 2014 International Conference on Connected Vehicles and Expo (ICCVE), pp. 144–148. IEEE (2014)Google Scholar
  16. 16.
    Ang, J.T., Chin, S.W., Chin, J.H., Choo, Z.X., Chang, Y.M.: iSCAPS-innovative smart car park system integrated with NFC technology and e-Valet function. In: 2013 World Congress on Computer and Information Technology (WCCIT), pp. 1–6. IEEE (2013)Google Scholar
  17. 17.
    Balzano, W., Vitale, F.: Dig-park: a smart park availability searching method using v2v/v2i and dgp-class problem. In: International workshop on Big Data Processing in Online Social Network (BOSON). IEEE (2017)Google Scholar
  18. 18.
    Amato, F., Moscato, F.: Exploiting cloud and workflow patterns for the analysis of composite cloud services. Future Gener. Comput. Syst. 67, 255–265 (2017)CrossRefGoogle Scholar
  19. 19.
    Amato, F., Moscato, F.: Model transformations of mapreduce design patterns for automatic development and verification. J. Parallel Distrib. Comput. (2016)Google Scholar
  20. 20.
    Amato, F., Moscato, F.: Pattern-based orchestration and automatic verification of composite cloud services. Comput. Electr. Eng. 56, 842–853 (2016)CrossRefGoogle Scholar
  21. 21.
    Balzano, W., Del Sorbo, M.R.: SeTra: a smart framework for gps trajectories’ segmentation. In: 2014 International Conference on Intelligent Networking and Collaborative Systems (INCoS), pp. 362–368. IEEE (2014)Google Scholar
  22. 22.
    Balzano, W., Murano, A., Vitale, F.: WiFACT-wireless fingerprinting automated continuous training. In: 2016 30th International Conference on Advanced Information Networking and Applications Workshops (WAINA), pp. 75–80. IEEE (2016)Google Scholar
  23. 23.
    Balzano, W., Del Sorbo, M.R., Del Prete, D.: SoCar: a social car2car framework to refine routes information based on road events and GPS. In: 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (CIT/IUCC/DASC/PICOM), pp. 743–748. IEEE (2015)Google Scholar
  24. 24.
    Balzano, W., Murano, A., Vitale, F.: V2v-en-vehicle-2-vehicle elastic network. Procedia Comput. Sci. 98, 497–502 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Dipartimento di Ingegneria Elettrica e Tecnologie dell’InformazioneUniversità degli Studi di Napoli Federico IINaplesItaly

Personalised recommendations