Abstract
Nowadays vehicles become self-organized mobile computers thanks to the emerging solutions that are designed to manage interconnection and interaction with the environment such as the IoV. Moreover, the wide spatial-temporal spread nature of these vehicles attracts cloud service providers to exploit under-used resources in the aim of growing their profitability. Virtual sensors represent a promised solution to optimize the exploitation of sensor resources and to offer on-demand services.
We propose a Cloud IoV architecture that integrates functional blocks of mobile sensor suppliers, Sensor Cloud Service Provider (SCSP), and service consumers. We design a Markov chain-based solution to predict the availability of mobile sensors. Furthermore, we propose a sensor virtualization technique to optimize the exploitation of sensor devices. To allow the SCSPs to maximize their revenue, we model the utility function of SCSP, and deduce the optimal number of allocated sensors. We also propose a sensor selection algorithm to select the most relevant sensors. A simulation is conducted to assess the efficiency of the proposed algorithm in terms of allocation blockage rate.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Fiems, D., Vinel, A.: Connectivity times in vehicular networks. IEEE Commun. Lett. 22(11), 2270–2273 (2018)
Morgul, E.F., et al.: Virtual sensors: web-based real-time data collection methodology for transportation operation performance analysis. Transp. Res. Rec. J. Transp. Res. Board 2442(1), 106–116 (2014). https://doi.org/10.3141/2442-12
Osamy, W., Khedr, A.M., Salim, A.: ADSDA: adaptive distributed service discovery algorithm for internet of things based mobile wireless sensor networks. IEEE Sens. J. 19(22), 10869–10880 (2019)
Rachkidi, E.E., Agoulmine, N., Belaâšid, D., Chendeb, N.: Towards an efficient service provisioning in Cloud of Things (CoT). In: 2016 IEEE Global Communications Conference (GLOBECOM), Washington, DC, USA (2016)
Singh, M., Baranwal, G., Tripathi, A.K.: QoS-aware selection of IoT-based service. Arab. J. Sci. Eng. 45(12), 10033–10050 (2020). https://doi.org/10.1007/s13369-020-04601-8
Sun, G., Boateng, G.O., Ayepah-Mensah, D., Liu, G., Wei, J.: Autonomous resource slicing for virtualized vehicular networks with D2D communications based on deep reinforcement learning. IEEE Syst. J. 14(4), 4694–4705 (2020)
Wong, E., Schneider, T., Schmitt, J., Schmid, F.R., Kolter, J.Z.: Neural network virtual sensors for fuel injection quantities with provable performance specifications. In: 2020 IEEE Intelligent Vehicles Symposium (IV), Las Vegas, NV, USA, pp. 1753–1758 (2020)
Zhang, M.-Z., Wang, L.-M., Xiong, S.-M.: Using machine learning methods to provision virtual sensors in sensor-cloud. Sensors 20(7), 1836 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Abbes, S., Rekhis, S. (2022). Sensor Virtualization and Provision in Internet of Vehicles. In: Barolli, L., Hussain, F., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2022. Lecture Notes in Networks and Systems, vol 450. Springer, Cham. https://doi.org/10.1007/978-3-030-99587-4_33
Download citation
DOI: https://doi.org/10.1007/978-3-030-99587-4_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-99586-7
Online ISBN: 978-3-030-99587-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)