A Secured and Efficient Communication Scheme for Decentralized Cognitive Radio-Based Internet of Vehicles

The advancements in hardware technologies have driven the evolution of vehicular ad hoc networks into the Internet of Vehicles (IoV). The IoV is a decentralized network of IoT-enabled vehicles capable of smooth traffic flow to perform fleet management and accident avoidance. The IoV has many commercial applications due to improved security and safety on the roads. However, the rapidly increasing number of wireless applications have challenged the existing spectrum bands allocated to IoV. The IoV has only six communication channels that are congested during the peak hours. The limited number of channels and the presence of congestion on these channels are the challenging issues that affect the safety of vehicles on the road. To mitigate the congestion, Cognitive Radio (CR) can be an optimal solution for the existing IoV Paradigm. In this paper, we propose a secured and efficient communication scheme for a decentralized CR-based IoV (CIoV) network. In this scheme, the Roadside Unit (RSU) senses the spectrum using an energy detection method. Each vehicle independently predicts the Primary User (PU) activity pattern using a hidden Markov model (HMM). Once a vehicle detects a licensed channel free from the PUs, it informs the RSU to store the channel in a database alongside the dedicated direct short-range communication (DSRC) channels for data transmission. The RSU and vehicles are registered with a trusted authority and they mutually authenticate each other. Upon mutual authentication, the RSU assigns communication channels to the vehicles on the road, based on their density. When the density of the vehicles is high, the detected licensed channels are used, otherwise, the DSRC channels are used. We evaluate the performance of CIoV in terms of packet delivery and packet loss ratio, end-to-end delay, and throughput, using NS-2. The simulation results show that the CR-based approach of CIoV outperforms the existing schemes and significantly enhances the performance of the underlying network.

[1]  M. Shamim Hossain,et al.  Secure Enforcement in Cognitive Internet of Vehicles , 2018, IEEE Internet of Things Journal.

[2]  Yunfei Chen,et al.  Analysis of Spectrum Occupancy Using Machine Learning Algorithms , 2015, IEEE Transactions on Vehicular Technology.

[3]  Hariharan Krishnan,et al.  V2V System Congestion Control Validation and Performance , 2019, IEEE Transactions on Vehicular Technology.

[4]  EBENEZER ESENOGHO Primary Users ON / OFF Behaviour Models in Cognitive Radio Networks , 2014 .

[5]  Mohamed Khalgui,et al.  Qualitative and Quantitative Risk Analysis and Safety Assessment of Unmanned Aerial Vehicles Missions Over the Internet , 2019, IEEE Access.

[6]  Ateeq Ur Rehman,et al.  A secured and reliable communication scheme in cognitive hybrid ARQ-aided smart city , 2020, Comput. Electr. Eng..

[7]  Min Chen,et al.  Cognitive Internet of Vehicles , 2018, Comput. Commun..

[8]  Sherali Zeadally,et al.  5G for Vehicular Communications , 2018, IEEE Communications Magazine.

[9]  Joel J. P. C. Rodrigues,et al.  AKM-IoV: Authenticated Key Management Protocol in Fog Computing-Based Internet of Vehicles Deployment , 2019, IEEE Internet of Things Journal.

[10]  Kenji Nakagawa,et al.  Comparative study of spectrum sensing techniques in cognitive radio networks , 2013, 2013 World Congress on Computer and Information Technology (WCCIT).

[11]  Liangmin Wang,et al.  NOTSA: Novel OBU With Three-Level Security Architecture for Internet of Vehicles , 2018, IEEE Internet of Things Journal.

[12]  Geoffrey Ye Li,et al.  Vehicular Communications: A Network Layer Perspective , 2017, IEEE Transactions on Vehicular Technology.

[13]  Sherali Zeadally,et al.  Internet of Vehicles: Architecture, Protocols, and Security , 2018, IEEE Internet of Things Journal.

[14]  Nhan Nguyen-Thanh,et al.  Strategic Surveillance Against Primary User Emulation Attacks in Cognitive Radio Networks , 2018, IEEE Transactions on Cognitive Communications and Networking.

[15]  Lie-Liang Yang,et al.  Performance of Cognitive Stop-and-Wait Hybrid Automatic Repeat Request in the Face of Imperfect Sensing , 2016, IEEE Access.

[16]  Wenchao Xu,et al.  Throughput Analysis of Vehicular Internet Access via Roadside WiFi Hotspot , 2019, IEEE Transactions on Vehicular Technology.

[17]  Zhiyuan Tan,et al.  Performance of Cognitive Radio Sensor Networks Using Hybrid Automatic Repeat ReQuest: Stop-and-Wait , 2018, Mobile Networks and Applications.

[18]  Tigang Jiang,et al.  Blockchain-Based Internet of Vehicles: Distributed Network Architecture and Performance Analysis , 2019, IEEE Internet of Things Journal.

[19]  Lie-Liang Yang,et al.  Performance of Cognitive Selective-Repeat Hybrid Automatic Repeat Request , 2016, IEEE Access.

[20]  Zhigang Chen,et al.  Energy-Harvesting-Aided Spectrum Sensing and Data Transmission in Heterogeneous Cognitive Radio Sensor Network , 2016, IEEE Transactions on Vehicular Technology.

[21]  Hang Liu,et al.  Decentralized Detection of GPS Spoofing in Vehicular Ad Hoc Networks , 2018, IEEE Communications Letters.

[22]  Daxin Tian,et al.  Channel Access Optimization with Adaptive Congestion Pricing for Cognitive Vehicular Networks: An Evolutionary Game Approach , 2020, IEEE Transactions on Mobile Computing.

[23]  Lie-Liang Yang,et al.  Throughput and Delay Analysis of Cognitive Go-Back-N Hybrid Automatic Repeat reQuest Using Discrete-Time Markov Modelling , 2016, IEEE Access.

[24]  George K. Karagiannidis,et al.  Entropy and Energy Detection-based Spectrum Sensing over F Composite Fading Channels , 2018 .

[25]  Robin Doss,et al.  An Improved Authentication Scheme for Internet of Vehicles Based on Blockchain Technology , 2019, IEEE Access.