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A new algorithm of clustering AODV based on edge computing strategy in IOV

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Abstract

In the vehicular ad hoc network (VANET), due to the particularity of high-speed movement of vehicle nodes, there are higher challenges in link stability and network topology control overhead. In this paper, a new algorithm of clustering AODV based on edge computing strategy is proposed. Considering the vehicle node energy and speed, the AODV routing protocol based on the minimum hop number is optimized, which divided the communication mode into vehicle to vehicle (V2V) and vehicle to road (V2R) mode. Adding edge server in the road side unit (RSU) and using the idea of clustering, that is, the nodes in the cluster use V2V communication mode, and the nodes between clusters use V2V and V2R combined communication mode to select routes. The algorithm improves the routing efficiency in the high-speed mobile. Experiments show that the algorithm is feasible, reducing the network topology control overhead, lowering the end-to-end delay and improving the packet delivery rate comparing with others in different environment.

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Abbreviations

\(V_{a} \left( {t_{n} } \right)\) :

The speed of vehicle a at the time \(t_{n}\)

\(POS_{a} \left( {t_{n} } \right)\) :

The position of vehicle a at the time \(t_{n}\)

\(\overline{V}_{ab}\) :

The relative speed between a and b

\(L_{RSU}\) :

The distance between adjacent RSU

\(L_{ROAD}\) :

The length of the road

\(L_{CAR}\) :

The communication range of the vehicle

\(T_{CAR}\) :

The vehicle link hold time

\(Cost\left( a \right)\) :

The energy consumption of the vehicle a

\(r_{e } ,r_{w}\) :

The two-way of the lane

\(\rho_{e} ,{ }\rho_{w}\) :

The two-way lane traffic density

\(P_{al}\) :

The unconnected link rate

\(T_{BRE}\) :

The minimum link holding time threshold

\(P_{e|H} \left( h \right)\) :

The link rate between neighbor vehicle

\(P_{H} \left( h \right)\) :

The probability disconnection between vehicles

\(CP_{V2V}\) :

The probability of connectivity between vehicles

\(T_{CTR}\) :

The max communication time between the vehicle and the RSU-MEC

\(CP_{V2R}\) :

The probability of connectivity between vehicle and RSU-MEC

\(P_{lbre}^{b}\) :

The probability of disconnection between vehicle and RSU-MEC

\(f\left( k \right)\) :

The probability density of vehicle number

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 61571328, in part by the Tianjin Key Natural Science Foundation under Grant 18JCZDJC96800, in part by the Training Plan of Tianjin University Innovation Team under Grant TD12-5016, TD13-5025, TD2015-23, in part by the Major Projects of Science and Technology for their Services in Tianjin under Grant 16ZXFWGX00010 and Grant 17YFZCGX00360.

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Authors and Affiliations

  1. Tianjin Key Lab of Intelligent Computing and Novel Software Technology, Tianjin University of Technology, Tianjin, 300384, China

    Degan Zhang, Changle Gong, Ting Zhang & Mingjie Piao

  2. Key Laboratory of Computer Vision and System, Ministry of Education, Tianjin University of Technology, Tianjin, 300384, China

    Degan Zhang, Changle Gong, Ting Zhang & Mingjie Piao

  3. School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, 100093, China

    Jie Zhang

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  1. Degan Zhang
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Correspondence to Changle Gong.

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Zhang, D., Gong, C., Zhang, T. et al. A new algorithm of clustering AODV based on edge computing strategy in IOV. Wireless Netw 27, 2891–2908 (2021). https://doi.org/10.1007/s11276-021-02624-z

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