{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T06:49:01Z","timestamp":1744181341830},"reference-count":12,"publisher":"Engineering and Technology Publishing","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["jcm"],"published-print":{"date-parts":[[2020]]},"abstract":"<jats:p>Vehicular ad-hoc networks is very popular research domain in which research work is going on at various aspects like routing the data without loss end-to-end. Routing in such networks is very tedious task due to frequently changing the position of vehicles location-wise. In this paper an intelligent model has been developed on the basis of adaptive neuro fuzzy system for OLSR routing protocol in VANET. The proposed model is designed based on input parameters average goodput and mac\/phy-overhead. Based on these parameters, transmission power can be predicted. Triangular and Gaussian membership functions have been applied for designing the decision model. A comparison work also has been carried out for Gaussian, triangular functions and NS-3 based results. At the same time, the model is investigated by simulation work carried out on network simulator-3 (NS-3) platform.<\/jats:p>","DOI":"10.12720\/jcm.15.10.768-775","type":"journal-article","created":{"date-parts":[[2020,12,29]],"date-time":"2020-12-29T01:43:57Z","timestamp":1609206237000},"page":"768-775","source":"Crossref","is-referenced-by-count":1,"title":["Transmission Power Based Intelligent Model in VANET"],"prefix":"10.12720","author":[{"name":"Hyderabad Institute of Technology and Management, Hyderabad-501401, India","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pushpender","family":"Sarao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"4977","published-online":{"date-parts":[[2020]]},"reference":[{"key":"ref0","doi-asserted-by":"publisher","unstructured":"[1] M. R. Ghori, K. Z. Zamli, N. Quosthoni, M. Hisyam, and M. Montaser, \"Vehicular ad-hoc network (VANET): Review,\" in Proc. IEEE International Conference on Innovative Research and Development (ICIRD), May 2018.","DOI":"10.1109\/ICIRD.2018.8376311"},{"key":"ref1","unstructured":"[2] Optimized Link State Routing Protocol (OLSR). [Online]. Available: https:\/\/tools.ietf.org\/html\/rfc3626"},{"key":"ref2","doi-asserted-by":"publisher","unstructured":"[3] R. Kaur, A. L. Sangal, and K. Kumar, \"Modeling and simulation of adaptive Neuro-fuzzy based intelligent system for predictive stabilization in structured overlay networks,\" Engineering Science and Technology, an International Journal, pp. 310-320, 2017.","DOI":"10.1016\/j.jestch.2016.06.015"},{"key":"ref3","doi-asserted-by":"publisher","unstructured":"[4] H. Moudnia, M. Er-rouidib, H. Mouncifc, and B. E. Hadadia, \"Black hole attack detection using fuzzy based intrusion detection systems in MANET,\" in Proc. International Workshop on Web Search and Data Mining, 2019, pp. 1176-1181.","DOI":"10.1016\/j.procs.2019.04.168"},{"key":"ref4","doi-asserted-by":"publisher","unstructured":"[5] S. Acharya and C. R. Tripathy, \"An ANFIS estimator based data aggregation scheme for fault tolerant wireless sensor networks,\" Journal of King Saud University - Computer and Information Sciences, vol. 30, pp. 334-348, 2018.","DOI":"10.1016\/j.jksuci.2016.10.001"},{"key":"ref5","unstructured":"[6] Y. V. S. S. Pragathi and S. P. Shetty, \"Design and implementation of ANFIS to enhance the performance of secure LAR routing protocol in MANETs,\" International Journal of Computer Science Trends and Technology, vol. 4, no. 5, pp. 205-208, 2016."},{"key":"ref6","doi-asserted-by":"publisher","unstructured":"[7] V. R. Budyal and S. S. 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Sarao, \"Comparison of AODV, DSR, and, DSDV routing protocols in a wireless network,\" Journal of Communications, vol. 13, no. 4, pp. 175-181, April 2018.","DOI":"10.12720\/jcm.13.4.175-181"},{"key":"ref10","unstructured":"[11] MATLAB for Artificial Intelligence. [Online]. Available: www.mathworks.com"},{"key":"ref11","doi-asserted-by":"publisher","unstructured":"[12] J. S. R. Jang, \"ANFIS: Adaptive-network-based fuzzy inference system,\" IEEE Transactions on Systems, Man, and Cybernetics, vol. 23, no. 3, pp. 665 - 685, 1993.","DOI":"10.1109\/21.256541"}],"container-title":["Journal of Communications"],"original-title":[],"link":[{"URL":"http:\/\/www.jocm.us\/uploadfile\/2020\/0911\/20200911054928469.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,11,24]],"date-time":"2021-11-24T02:34:21Z","timestamp":1637721261000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.jocm.us\/show-245-1602-1.html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":12,"URL":"https:\/\/doi.org\/10.12720\/jcm.15.10.768-775","relation":{},"ISSN":["1796-2021"],"issn-type":[{"type":"print","value":"1796-2021"}],"subject":[],"published":{"date-parts":[[2020]]}}}