[논문]Modeling Geographical Anycasting Routing in Vehicular Networks

Amirshahi, Alireza; Romoozi, Morteza; Raayatpanah, Mohammad Ali; Asghari, Seyyed Amir

doi:10.3837/tiis.2020.04.012

Modeling Geographical Anycasting Routing in Vehicular Networks 원문보기

KSII Transactions on internet and information systems : TIIS, v.14 no.4, 2020년, pp.1624 - 1647

Amirshahi, Alireza (Department of Computer Engineering, Qom Branch, Islamic Azad University) , Romoozi, Morteza (Department of Computer Engineering, Kashan Branch, Islamic Azad University) , Raayatpanah, Mohammad Ali (Faculty of Mathematical Sciences and Computer, Kharazmi University) , Asghari, Seyyed Amir (Computer and Electrical Engineering Department, Kharazmi University of Tehran)

Abstract ▼ AI-Helper

Vehicular network is one of the most important subjects for researchers in recent years. Anycast routing protocols have many applications in vehicular ad hoc networks. The aim of an anycast protocol is sending packets to at least one of the receivers among candidate receivers. Studies done on anycast protocols over vehicular networks, however, have capability of implementation on some applications; they are partial, and application specific. No need to say that the lack of a comprehensive study, having a strong analytical background, is felt. Mathematical modeling in vehicular networks is difficult because the topology of these networks is dynamic. In this paper, it has been demonstrated that vehicular networks can be modeled based on time-expanded networks. The focus of this article is on geographical anycast. Three different scenarios were proposed including sending geographic anycast packet to exactly-one-destination, to at-least-one-destination, and to K-anycast destination, which can cover important applications of geographical anycast routing protocols. As the proposed model is of MILP type, a decentralized heuristic algorithm was presented. The evaluation process of this study includes the production of numerical results by Branch and Bound algorithm in general algebraic modeling system (GAMS) software and simulation of the proposed protocol in OMNET++ simulator. The comprehension of the result of proposed protocol and model shows that the applicability of this proposed protocol and its reactive conformity with the presented models based on presented metrics.

주제어

표/그림 (38)

표 Table 1. NOTATIONS AND DESCRIPTIONS
그림 Fig. 1. A vehicular network with source 1 to destination 6
그림 Fig. 3. Packet broadcasting to the best destination among all other destinations
그림 Fig. 2. Time-expanded network of Fig. 1
그림 Fig. 4. Sending the packet to a subset of destinations D.
그림 Fig. 5. city map in SUMO.
그림 Fig. 6. Flowchart of the algorithm of selecting scenario(s)
표 Table 2. SIMULATION PARAMETERS
그림 Fig. 7. Average link variations with alteration of the numbers of nodes.
그림 Fig. 8. Average link change with various mobility speed
그림 Fig. 9. Average hop count against the numbers of nodes (exactly-one-destination scenario)
그림 Fig. 11. Average hop count against the numbers of nodes (at-least-one-destination scenario)
그림 Fig. 13. Average hop count against the numbers of nodes (K-anycast destination scenario)
그림 Fig. 10. Average hop count against the mobility speed (exactly-one-destination scenario)
그림 Fig. 12. Average hop count against the mobility speed (at-least-one-destination scenario)
그림 Fig. 14. Average hop count against the mobility speed (K-anycast destination scenario)
그림 Fig. 15. Average Delivery Delay with different numbers of nodes (exactly-one-destination scenario)
그림 Fig. 17. Average Delivery Delay with different numbers of nodes (at-least-one-destination scenario)
그림 Fig. 19. Average Delivery Delay with different numbers of nodes (K-anycast destination scenario)
그림 Fig. 16. Average Delivery Delay with different mobility speed (exactly-one-destination scenario)
그림 Fig. 18. Average Delivery Delay with different mobility speed (at-least-one-destination scenario)
그림 Fig. 20. Average Delivery Delay with different mobility speed (K-anycast destination scenario)
그림 Fig. 21. Average Delivery Cost with different numbers of nodes (exactly-one-destination scenario)
그림 Fig. 23. Average Delivery Cost with different numbers of nodes (at-least-one-destination scenario)
그림 Fig. 22. Average Delivery Cost with various mobility speed (exactly-one-destination scenario)
그림 Fig. 24. Average Delivery Cost with different mobility speed (at-least-one-destination scenario)
그림 Fig. 25. Average Delivery Cost with different numbers of nodes (K-anycast destination scenario)
그림 Fig. 27. Packet Delivery Ratio with various numbers of nodes (exactly-one-destination scenario)
그림 Fig. 26. Average Delivery Cost with different mobility speed (K-anycast destination scenario)
그림 Fig. 28. Packet Delivery Ratio with various mobility speed (exactly-one-destination scenario)
그림 Fig. 29. Packet Delivery Ratio with different numbers of nodes (at-least-one-destination scenario)
그림 Fig. 31. Packet Delivery Ratio with different numbers of nodes (K-anycast destination scenario)
그림 Fig. 30. Packet Delivery Ratio with different numbers of nodes (at-least-one-destination scenario)
그림 Fig. 32. Packet Delivery Ratio with different mobility speed (K-anycast destination scenario)
그림 Fig. 33. Packet Delivery Ratio in different scenarios
그림 Fig. 35. Average Packet Delivery Delay in different scenarios.
그림 Fig. 34. Average Hop Count in different scenarios.
그림 Fig. 36. Average Packet Delivery Cost in different scenarios.

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표제어: PCR

동의어: Packet Collision Rate

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