PERFORMANCE MEASUREMENT OF ROUTING ALGORITHMS IN AD-HOC NETWORKS
الموضوعات :
فرهاد بهادری جهرمی
1
(گروه مهندسی برق،واحد فسا،دانشگاه آزاد اسلامی،فسا،ایران)
الکلمات المفتاحية: BF, AODVR, DSR,
ملخص المقالة :
The main aim of this article is to show the differences performance between ad-hoc routing algorithms. Here we consider the Bellman-Ford (BF), Ad hoc On Demand Distance Vector (AODV) and Dynamic Source Routing (DSR) algorithms facing different node mobility speed. The study covers the following topics: the algorithms, simulation environment and the comparison of measured values. we can state that the modularity speed of the nodes in ad-hoc networks plays a significant role in the overall performance. We can see that different algorithms have different throughput parameters. For example the DSR is very strong at low speed but at higher speeds it is worse than the AODVR. It is seen that the AODVR and DSR are better at high speed than the BF.
[1] S. Sharma and A. Kaul, “A survey on intrusion detection systems and honeypot based proactive security mechanisms in VANETs and VANET cloud,” Vehicular Communications, vol. 12. Elsevier Inc., pp. 138–164, 01-Apr-2018.
[2] M. Singh and J. Sharma, “Performance analysis of secure & efficient AODV (SE-AODV) with AODV routing protocol using NS2,” in Proceedings - 2014 3rd International Conference on Reliability, Infocom Technologies and Optimization: Trends and Future Directions, ICRITO 2014, 2015.
[3] L. Pan, “An improved the DSR routing protocol in mobile ad hoc networks,” in Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS, 2015, vol. 2015-November, pp. 591–594.
[4] M. Manjunath and D. H. Manjaiah, “Spatial DSDV (S-DSDV) routing algorithm for mobile ad hoc network,” in Proceedings of 2014 International Conference on Contemporary Computing and Informatics, IC3I 2014, 2014, pp. 625–629.
[5] A. M. Pushpa, “Trust based secure routing in AODV routing protocol,” in 2009 IEEE International Conference on Internet Multimedia Services Architecture and Applications, IMSAA 2009, 2009.
[6] D. Zhang, F. Richard Yu, R. Yang, and H. Tang, “A deep reinforcement learning-based trust management scheme for softwaredefined vehicular networks,” in DIVANet 2018 - Proceedings of the 8th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications, 2018, pp. 1–7.
[7] C. Dai, X. Xiao, Y. Ding, L. Xiao, Y. Tang, and S. Zhou, “Learning based security for VANET with blockchain,” in 2018 IEEE International Conference on Communication Systems, ICCS 2018, 2018, pp. 210–215.
[8] L. Xiao, X. Lu, D. Xu, Y. Tang, L. Wang, and W. Zhuang, “UAV relay in VANETs against smart jamming with reinforcement learning,” IEEE Trans. Veh. Technol., vol. 67, no. 5, pp. 4087–4097, May 2018.
[9] D. Zhang, F. R. Yu, and R. Yang, “A machine learning approach for software-defined vehicular Ad Hoc networks with Trust Management,” in 2018 IEEE Global Communications Conference (GLOBECOM), 2018, pp. 1–6.
[10] M. Naderi, F. Zargari, and M. Ghanbari, “Adaptive beacon broadcast in opportunistic routing for VANETs,” Ad Hoc Networks, vol. 86, pp. 119–130, Apr. 2019.
