Artificial intelligence based handover decision and network selection in heterogeneous internet of vehicles

Internet of vehicles (IoV) is an emerging area that gives support for vehicles via internet assisted communication. IoV with 5G provides ubiquitous connectivity due to the participation of more than one radio access network. The mobility of vehicles demands to make handover in such heterogeneous network. The vehicles at short range uses dedicated short range communication (DSRC), while it has to use better technology for long range and any type of traffic. Usually, the previous work will directly select the network for handover or it connects with available radio access. Due to this, the occurrence of handover takes place frequently.  In this paper, the integration of DSRC, LTE and mmWave 5G on IoV is incorporated with novel handover decision making, network selection and routing. The handover decision is to ensure whether there is a need for vertical handover by using Dynamic Q-learning algorithm that uses entropy function for threshold prediction as per the current characteristics of the environment. Then the network selection is based on fuzzy-convolution neural network (F-CNN) that creates fuzzy rules from signal strength, distance, vehicle density, data type and line of sight. V2V chain routing is proposed to select V2V pairs using jellyfish optimization algorithm (JOA) that takes in account of channel, vehicle and transmission metrics. This system is developed in OMNeT++ simulator and the performances are evaluated in terms of success probability, handover failure, unnecessary handover, mean throughput, delay and packet loss.

[1]  Emmanuel Ndashimye,et al.  A network selection method for handover in vehicle-to-infrastructure communications in multi-tier networks , 2018, Wirel. Networks.

[2]  R. Ganiga,et al.  A novel approach to sensor implementation for healthcare systems using internet of things , 2019 .

[3]  Yuliang Tang,et al.  Network Selection in Heterogeneous Vehicular Network: A One-to-Many Matching Approach , 2020, 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring).

[4]  Ming Xiao,et al.  Machine Learning-Based Handovers for Sub-6 GHz and mmWave Integrated Vehicular Networks , 2019, IEEE Transactions on Wireless Communications.

[5]  Abdulraqeb Alhammadi,et al.  An intelligent spectrum handoff scheme based on multiple attribute decision making for LTE-A network , 2019 .

[6]  Abderrahim Sekkaki,et al.  A novel modeling approach for vertical handover based on dynamic k-partite graph in heterogeneous networks , 2019 .

[7]  Sungoh Kwon,et al.  Vertical Handover Analysis for Randomly Deployed Small Cells in Heterogeneous Networks , 2020, IEEE Transactions on Wireless Communications.

[8]  Senthilnathan Palaniapan,et al.  Real time implementation of embedded devices as a security system in intelligent vehicles connected via Vanets , 2019 .

[9]  Hybridization of Monarch Butterfly and Grey Wolf Optimization for Optimal Routing in VANET , 2019 .

[10]  Yanjun Shi,et al.  An Effective Order-Aware Hybrid Genetic Algorithm for Capacitated Vehicle Routing Problems in Internet of Things , 2019, IEEE Access.

[11]  Fatima de L. P. Duarte-Figueiredo,et al.  A 5G V2X Ecosystem Providing Internet of Vehicles † , 2019, Sensors.

[12]  Zuriati Ahmad Zukarnain,et al.  An Adaptive Relay Selection Scheme for Enhancing Network Stability in VANETs , 2020, IEEE Access.

[13]  Imad Mahgoub,et al.  Fuzzy Logic-Based Geographic Routing for Urban Vehicular Networks Using Link Quality and Achievable Throughput Estimations , 2019, IEEE Transactions on Intelligent Transportation Systems.

[14]  Shaik Mazhar Hussain,et al.  A Review of Interoperability issues in Internet of Vehicles (IoV) , 2019 .

[15]  Yang Yang,et al.  Emerging Technologies for 5G-Enabled Vehicular Networks , 2019, IEEE Access.

[16]  Antonio F. Gómez-Skarmeta,et al.  Empowering the Internet of Vehicles with Multi-RAT 5G Network Slicing , 2019, Sensors.

[17]  Abdelhamid Mellouk,et al.  Data dissemination for Internet of vehicle based on 5G communications: A survey , 2020, Trans. Emerg. Telecommun. Technol..

[18]  Lei Liu,et al.  Delay-Aware Grid-Based Geographic Routing in Urban VANETs: A Backbone Approach , 2019, IEEE/ACM Transactions on Networking.

[19]  Muhammad Khurram Khan,et al.  Intelligent Technique for Seamless Vertical Handover in Vehicular Networks , 2018, Mob. Networks Appl..

[20]  Matteo Drago,et al.  Toward Standardization of Millimeter-Wave Vehicle-to-Vehicle Networks: Open Challenges and Performance Evaluation , 2020, IEEE Communications Magazine.

[21]  Jason J. Jung,et al.  ACO-based Approach on Dynamic MSMD Routing in IoV Environment , 2020, 2020 16th International Conference on Intelligent Environments (IE).

[22]  Khaled M. Rabie,et al.  Smart Handoff Technique for Internet of Vehicles Communication using Dynamic Edge-Backup Node , 2020 .

[23]  Andrea Zanella,et al.  On the Feasibility of Integrating mmWave and IEEE 802.11p for V2V Communications , 2018, 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall).

[24]  Ting Chen,et al.  An Optimal Game Approach for Heterogeneous Vehicular Network Selection with Varying Network Performance , 2019, IEEE Intelligent Transportation Systems Magazine.

[25]  Saiful Izwan Suliman,et al.  Q-Learning vertical handover scheme in two-tier LTE-A networks , 2020 .

[26]  Zhengguo Sheng,et al.  Intelligent 5G Vehicular Networks: An Integration of DSRC and mmWave Communications , 2018, 2018 International Conference on Information and Communication Technology Convergence (ICTC).

[27]  James J. Q. Yu,et al.  Online Vehicle Routing With Neural Combinatorial Optimization and Deep Reinforcement Learning , 2019, IEEE Transactions on Intelligent Transportation Systems.

[28]  Carlos Renato Storck,et al.  A Survey of 5G Technology Evolution, Standards, and Infrastructure Associated With Vehicle-to-Everything Communications by Internet of Vehicles , 2020, IEEE Access.