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Vehicular Intelligence System: Time-Based Vehicle Next Location Prediction in Software-Defined Internet of Vehicles (SDN-IOV) for the Smart Cities

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Intelligence of Things: AI-IoT Based Critical-Applications and Innovations

Abstract

Background Theory: The development of traditional Vehicular Ad-Hoc Networks (VANETs) into the Internet of Vehicles (IOV) is inspired by the modern era of the Internet of Things (IoT). The smart city technologies can offer suitable services based on the citizens’ demands. The Internet of Things (IoT) technology, that allows a large number of devices to communicate with one another, is one of the key enablers in Smart Cities (SC).

Objective: This chapter is an outline of SC and the next position’s prediction using software-defined IoT-based VANETs, known as IOV networks. This chapter is designed in three-phase: (1) Smart Cities (SC) deployment, (2) improved data forwarding, and (3) location prediction of vehicle. The vehicular intelligence system considers road conditions, traffic density, current position, and cars’ speed.

Methodology: The proposed Artificial Intelligence (AI) Model helps the Software-Defined Controller (SDN), centralized controller to calculate and predict future status inside and outside the area. To evaluate the proposed system, the SUMO simulator with Open Street map (OSM) is taken into account, and the outcome shows the significance of vehicle location prediction for VANET.

Result: The mathematical examination and simulation results on Network Simulator 3 and SUMO shows a higher packet delivery ratio (PDR) as compared to the existing protocol. The proposed protocol ERS-SDN-IOV shows a less delivery transmission time compared to the AODV, GPSR, and SDGR. It is having the 95% delivery ratio and 50% lesser transmission time as compare to mention existing protocols.

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Author information

Authors and Affiliations

  1. SRMIST, Delhi-NCR Campus, Modinagar, Ghaziabad, India

    Preeti Rani

  2. School of Computers, IPS Academy, Indore, India

    Naziya Hussain

  3. G.H. Raisoni University, Amravati, India

    Rais Abdul Hamid Khan

  4. Vishwakarma Institute of Information Technology, Pune, India

    Yogesh Sharma

  5. UIT, RGPV, Bhopal, India

    Piyush Kumar Shukla

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  1. Preeti Rani
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  2. Naziya Hussain
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  3. Rais Abdul Hamid Khan
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  4. Yogesh Sharma
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  5. Piyush Kumar Shukla
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Editor information

Editors and Affiliations

  1. Artificial Intelligence Department, Near East University, Mersin 10, Turkey

    Fadi Al-Turjman

  2. Duy Tan University, Graduate School, Faculty of Information Technology, Da Nang, Vietnam

    Anand Nayyar

  3. AP3 Solutions, Chennai, India

    Ajantha Devi

  4. Rajiv Gandhi Technical University, Bhopal, India

    Piyush Kumar Shukla

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Rani, P., Hussain, N., Khan, R.A.H., Sharma, Y., Shukla, P.K. (2021). Vehicular Intelligence System: Time-Based Vehicle Next Location Prediction in Software-Defined Internet of Vehicles (SDN-IOV) for the Smart Cities. In: Al-Turjman, F., Nayyar, A., Devi, A., Shukla, P.K. (eds) Intelligence of Things: AI-IoT Based Critical-Applications and Innovations . Springer, Cham. https://doi.org/10.1007/978-3-030-82800-4_2

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  • DOI: https://doi.org/10.1007/978-3-030-82800-4_2

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  • Online ISBN: 978-3-030-82800-4

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