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Journal articles on the topic 'ROUTING STRATEGIES'

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Published: 11 September 2023

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1

Scheideler, C., and B. Vöcking. "Universal Continuous Routing Strategies." Theory of Computing Systems 31, no. 4 (July 1, 1998): 425–49. http://dx.doi.org/10.1007/s002240000096.

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2

Hose, Katja, Christian Lemke, and Kai-Uwe Sattler. "Maintenance strategies for routing indexes." Distributed and Parallel Databases 26, no. 2-3 (July 9, 2009): 231–59. http://dx.doi.org/10.1007/s10619-009-7048-5.

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3

Tarhini, Hussein, and Douglas R. Bish. "Routing Strategies Under Demand Uncertainty." Networks and Spatial Economics 16, no. 2 (May 14, 2015): 665–85. http://dx.doi.org/10.1007/s11067-015-9293-7.

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4

Atar, Y., J. Naor, and R. Rom. "Routing strategies for fast networks." IEEE Transactions on Computers 45, no. 2 (1996): 165–73. http://dx.doi.org/10.1109/12.485380.

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5

Davis, Russ E., Naomi E. Leonard, and David M. Fratantoni. "Routing strategies for underwater gliders." Deep Sea Research Part II: Topical Studies in Oceanography 56, no. 3-5 (February 2009): 173–87. http://dx.doi.org/10.1016/j.dsr2.2008.08.005.

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6

Yu, Song Sen, Xiao Peng Huang, and Jian Yang. "Quantization Strategies of RFID Routing in Internet of Things." Applied Mechanics and Materials 203 (October 2012): 148–52. http://dx.doi.org/10.4028/www.scientific.net/amm.203.148.

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This paper researches wireless RFID routings in Internet of Things based on the idea of quantization. The researches include the obtaining of routing contexts, the measurement of contexts in evaluating the performance of routing, methods for controlling the quantification of contexts, the realization of a perceptive computational model, the mergence of the security policies. Its goal is to construct a precise lightweight and perceptive model of wireless RFID routing protocols in Internet of Things, and to lay a foundation for accelerating the developments in Internet of Things.
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7

Amin, Ifrah, Gulzar Ahmad dar, and Hrdeep singh Saini. "Routing Strategies in Survivable Optical Networks." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 9, no. 2 (July 30, 2013): 1055–62. http://dx.doi.org/10.24297/ijct.v9i2.4169.

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Routing and wavelength assignment problem is one of the main problem in optical networks. The foremost problem is the routing problem after which the wavelength assignment is to be decided. In this paper we have proposed a routing strategy for optimization of the performance of the optical network in terms of blocking probability. The strategy proposed is better than the conventional algorithm in terms of blocking.Â
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8

Tyagi, Shivam, and Mohammad Ayoub Khan. "Topologies and routing strategies in MPSoC." International Journal of Embedded Systems 5, no. 1/2 (2013): 27. http://dx.doi.org/10.1504/ijes.2013.052154.

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9

Branke, Jürgen, Martin Middendorf, Guntram Noeth, and Maged Dessouky. "Waiting Strategies for Dynamic Vehicle Routing." Transportation Science 39, no. 3 (August 2005): 298–312. http://dx.doi.org/10.1287/trsc.1040.0095.

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10

Gui, Bo, Lin Dai, and Leonard J. Cimini. "Routing strategies in multihop cooperative networks." IEEE Transactions on Wireless Communications 8, no. 2 (February 2009): 843–55. http://dx.doi.org/10.1109/twc.2009.071030.

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11

Awerbuch, Baruch, Amotz Bar-Noy, Nathan Linial, and David Peleg. "Improved routing strategies with succinct tables." Journal of Algorithms 11, no. 3 (September 1990): 307–41. http://dx.doi.org/10.1016/0196-6774(90)90017-9.

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12

Zhen, Lu, Shuaian Wang, and Dan Zhuge. "Analysis of three container routing strategies." International Journal of Production Economics 193 (November 2017): 259–71. http://dx.doi.org/10.1016/j.ijpe.2017.07.011.

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13

?ftar, Altu?, and Edward J. Davison. "Decentralized control strategies for dynamic routing." Optimal Control Applications and Methods 23, no. 6 (2002): 329–55. http://dx.doi.org/10.1002/oca.717.

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14

Yamashita, Hiroshi, Feijia Yin, Volker Grewe, Patrick Jöckel, Sigrun Matthes, Bastian Kern, Katrin Dahlmann, and Christine Frömming. "Analysis of Aircraft Routing Strategies for North Atlantic Flights by Using AirTraf 2.0." Aerospace 8, no. 2 (January 28, 2021): 33. http://dx.doi.org/10.3390/aerospace8020033.

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Climate-optimized routing is an operational measure to effectively reduce the climate impact of aviation with a slight increase in aircraft operating costs. This study examined variations in the flight characteristics among five aircraft routing strategies and discusses several characteristics of those routing strategies concerning typical weather conditions over the North Atlantic. The daily variability in the North Atlantic weather patterns was analyzed by using the European Center Hamburg general circulation model (ECHAM) and the Modular Earth Submodel System (MESSy) Atmospheric Chemistry (EMAC) model in the specified dynamics mode from December 2008 to August 2018. All days of the ten complete winters and summers in the simulations were classified into five weather types for winter and into three types for summer. The obtained frequency for each of the weather types was in good agreement with the literature data; and then representative days for each weather type were selected. Moreover, a total of 103 North Atlantic flights of an Airbus A330 aircraft were simulated with five aircraft routing strategies for each representative day by using the EMAC model with the air traffic simulation submodel AirTraf. For every weather type, climate-optimized routing shows the lowest climate impact, at which a trade-off exists between the operating costs and the climate impact. Cost-optimized routing lies between the time- and fuel-optimized routings and achieves the lowest operating costs by taking the best compromise between flight time and fuel use. The aircraft routing for contrail avoidance shows the second lowest climate impact; however, this routing causes extra operating costs. Our methodology could be extended to statistical analysis based on long-term simulations to clarify the relationship between the aircraft routing characteristics and weather conditions.
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15

Knoop, V. L., S. P. Hoogendoorn, and J. W. C. Van Lint. "Routing Strategies Based on Macroscopic Fundamental Diagram." Transportation Research Record: Journal of the Transportation Research Board 2315, no. 1 (January 2012): 1–10. http://dx.doi.org/10.3141/2315-01.

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16

Korilis, Yannis A., and Ariel Orda. "Incentive Compatible Pricing Strategies for QoS Routing." Networks and Spatial Economics 4, no. 1 (March 2004): 39–53. http://dx.doi.org/10.1023/b:nets.0000015655.31348.1b.

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17

Hemminger, Thomas L., Chris Coulston, and Carlos A. Pomalazza-Raez. "Routing Strategies for Multicast Packet Radio Networks." International Journal of Smart Engineering System Design 4, no. 3 (January 2002): 215–23. http://dx.doi.org/10.1080/10255810213479.

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18

Maublanc, J., D. Peyrton, and A. Quilliot. "Multiple Routing Strategies in a Labelled Network." RAIRO - Operations Research 35, no. 1 (January 2001): 85–106. http://dx.doi.org/10.1051/ro:2001104.

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19

Taylor, Brian. "Contralateral Routing of the Signal Amplification Strategies." Seminars in Hearing 31, no. 04 (November 2010): 378–92. http://dx.doi.org/10.1055/s-0030-1268036.

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20

Zhou, Xian-wei, Zhi-mi Cheng, Yan Ding, Joo-Ghee Lim, and Qian Liu. "Dynamic DTN Routing Strategies Based on Knowledge." Wireless Personal Communications 71, no. 3 (November 9, 2012): 1819–36. http://dx.doi.org/10.1007/s11277-012-0912-z.

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21

Korilis, Y. A., A. A. Lazar, and A. Orda. "Achieving network optima using Stackelberg routing strategies." IEEE/ACM Transactions on Networking 5, no. 1 (1997): 161–73. http://dx.doi.org/10.1109/90.554730.

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22

Vasil'ev, N. S., and V. V. Fedorov. "Equilibrium routing strategies in data transmission networks." Computational Mathematics and Modeling 10, no. 4 (October 1999): 413–25. http://dx.doi.org/10.1007/bf02359091.

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23

Zheng, Hong. "Optimization of bus routing strategies for evacuation." Journal of Advanced Transportation 48, no. 7 (February 24, 2013): 734–49. http://dx.doi.org/10.1002/atr.1224.

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24

Lun, Weicheng, Qun Li, Zhi Zhu, and Can Zhang. "Routing Strategies for Isochronal-Evolution Random Matching Network." Entropy 25, no. 2 (February 16, 2023): 363. http://dx.doi.org/10.3390/e25020363.

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In order to abstract away a network model from some real-world networks, such as navigation satellite networks and mobile call networks, we proposed an Isochronal-Evolution Random Matching Network (IERMN) model. An IERMN is a dynamic network that evolves isochronally and has a collection of edges that are pairwise disjoint at any point in time. We then investigated the traffic dynamics in IERMNs whose main research topic is packet transmission. When a vertex of an IERMN plans a path for a packet, it is permitted to delay the sending of the packet to make the path shorter. We designed a routing decision-making algorithm for vertices based on replanning. Since the IERMN has a specific topology, we developed two suitable routing strategies: the Least Delay Path with Minimum Hop (LDPMH) routing strategy and the Least Hop Path with Minimum Delay (LHPMD) routing strategy. An LDPMH is planned by a binary search tree and an LHPMD is planned by an ordered tree. The simulation results show that the LHPMD routing strategy outperformed the LDPMH routing strategy in terms of the critical packet generation rate, number of delivered packets, packet delivery ratio, and average posterior path lengths.
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25

Sampath, O., and P. Suryanarayana Babu. "Energy Efficient Routing Strategies for Large Scale Wireless Sensor in Heterogeneous Networks." International Journal of Trend in Scientific Research and Development Volume-3, Issue-2 (February 28, 2019): 80–85. http://dx.doi.org/10.31142/ijtsrd20281.

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26

CAVALIERI, S., A. DI STEFANO, and O. MIRABELLA. "NEURAL STRATEGIES TO HANDLE ROUTING IN COMPUTER NETWORKS." International Journal of Neural Systems 04, no. 03 (September 1993): 269–89. http://dx.doi.org/10.1142/s0129065793000225.

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In this paper, the authors adopt a neural approach to deal with the problem of routing in a packet switching network. The aim is to define a routing strategy which will combine the advantages of both the centralized and the distributed approaches. The neural approach presented is based on the idea of inserting a neural network (N/N) into each node in the computer network which will be responsible for computing the route between its node and the immediately adjacent one. Two distributed routing solutions are presented in the paper based on an optimizing network and a mapping network. The routing obtainable and the implementation resources needed for the two approaches are evaluated. Finally, the performance offered by the neural strategies proposed is compared with that offered by classical distributed and centralized routing solutions. As a parameter of merit, the effect of overloading caused by the additional traffic present in each solution is used.
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27

GUO, XUAN, and HONGTAO LU. "TRAFFIC CONGESTION ANALYSIS IN COMPLEX NETWORKS BASED ON VARIOUS ROUTING STRATEGIES." Modern Physics Letters B 21, no. 15 (June 20, 2007): 929–39. http://dx.doi.org/10.1142/s0217984907013389.

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Networks, acting as infrastructure for information communication, play an important role in modern society, therefore, the elements affecting the efficiency of network traffic are worthy of deep research. In this paper, we investigate numerically the problem of traffic congestion in complex networks through the use of various routing strategies. Three types of complex networks structures, namely Poisson random networks, small-world networks and scale-free networks, are considered. Three different routing strategies are used on networks: deterministic routing strategy, preferential routing strategy and shortest path routing strategy. We evaluate the efficiency of different routing strategies on different network topologies and show how the network structures and routing strategies influence the traffic congestion status.
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28

de Almeida Amazonas, José Roberto, Xavier Hesselbach, and William Ferreira Giozza. "Low complexity nano-networks routing scenarios and strategies." Nano Communication Networks 28 (June 2021): 100349. http://dx.doi.org/10.1016/j.nancom.2021.100349.

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29

Delavar, Arash Ghorbannia, Somayyeh Hoseyny, and Rouhollah Maghsoudi. "Bco-based Optimized Heuristic Strategies For Qos Routing." Journal of Mathematics and Computer Science 05, no. 02 (August 30, 2012): 105–14. http://dx.doi.org/10.22436/jmcs.05.02.05.

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30

Manikandan, T. T., Rajeev Sukumaran, M. R. Christhu Raj, and M. Saravanan. "Routing strategies for underwater wireless communication: a taxonomy." International Journal of Communication Networks and Distributed Systems 27, no. 2 (2021): 147. http://dx.doi.org/10.1504/ijcnds.2021.116782.

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31

Zhang GuoYin, and Li Heng. "Dynamic Routing Strategies based on Hierarchical Kautz Graph." Journal of Convergence Information Technology 7, no. 11 (June 30, 2012): 1–10. http://dx.doi.org/10.4156/jcit.vol7.issue11.1.

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32

Kolios, Athanasios J., and Stuart Howe. "OPTIMIZATION OF AVIATION EMISSIONS THROUGH ALTERNATIVE ROUTING STRATEGIES." Environmental Engineering and Management Journal 13, no. 2 (2014): 297–304. http://dx.doi.org/10.30638/eemj.2014.034.

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33

Hancock, Terence M. "Effects of Lot‐splitting under Various Routing Strategies." International Journal of Operations & Production Management 11, no. 1 (January 1991): 68–74. http://dx.doi.org/10.1108/01443579110144277.

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34

Bobrowski, Paul M., and Vincent A. Mabert. "Alternate Routing Strategies in Batch Manufacturing: An Evaluation." Decision Sciences 19, no. 4 (December 1988): 713–33. http://dx.doi.org/10.1111/j.1540-5915.1988.tb00297.x.

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35

M, Saravanan, CHRISTHU R. A. J. M. R, Manikandan T. T, and RAJEEV SUKUMARAN. "Routing Strategies For Underwater Wireless Communication: A Taxonomy." International Journal of Communication Networks and Distributed Systems 1, no. 1 (2021): 1. http://dx.doi.org/10.1504/ijcnds.2021.10033365.

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36

Williams, Jason D., and Silke M. Witt. "A Comparison of Dialog Strategies for Call Routing." International Journal of Speech Technology 7, no. 1 (January 2004): 9–24. http://dx.doi.org/10.1023/b:ijst.0000004803.47697.bd.

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37

Singh, Paramjeet, Ajay K. Sharma, and Shaveta Rani. "Routing and wavelength assignment strategies in optical networks." Optical Fiber Technology 13, no. 3 (July 2007): 191–97. http://dx.doi.org/10.1016/j.yofte.2006.10.002.

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38

Anderson, Eric J., Thomas E. Anderson, Steven D. Gribble, Anna R. Karlin, and Stefan Savage. "A quantitative evaluation of traffic-aware routing strategies." ACM SIGCOMM Computer Communication Review 32, no. 1 (January 2002): 67. http://dx.doi.org/10.1145/510726.510741.

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39

G., P., A. R., and S. S. "A Survey on Geographical Routing Strategies in VANET." International Journal of Computer Applications 133, no. 16 (January 15, 2016): 25–29. http://dx.doi.org/10.5120/ijca2016908203.

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40

Göttlich, Simone, and Sebastian Kühn. "Routing strategies in production networks with random breakdowns." Communications in Mathematical Sciences 14, no. 7 (2016): 1799–820. http://dx.doi.org/10.4310/cms.2016.v14.n7.a2.

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41

Zubair, Suleiman, Norsheila Fisal, Yakubu Baguda, and Kashif Saleem. "Assessing Routing Strategies for Cognitive Radio Sensor Networks." Sensors 13, no. 10 (September 26, 2013): 13005–38. http://dx.doi.org/10.3390/s131013005.

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42

Paricio, Alvaro, and Miguel A. Lopez-Carmona. "Urban Traffic Routing Using Weighted Multi-Map Strategies." IEEE Access 7 (2019): 153086–101. http://dx.doi.org/10.1109/access.2019.2947699.

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43

Avena-Koenigsberger, Andrea, Xiaoran Yan, Artemy Kolchinsky, Martijn P. van den Heuvel, Patric Hagmann, and Olaf Sporns. "A spectrum of routing strategies for brain networks." PLOS Computational Biology 15, no. 3 (March 8, 2019): e1006833. http://dx.doi.org/10.1371/journal.pcbi.1006833.

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44

Iwata, A., Ching-Chuan Chiang, Guangyu Pei, M. Gerla, and Tsu-Wei Chen. "Scalable routing strategies for ad hoc wireless networks." IEEE Journal on Selected Areas in Communications 17, no. 8 (1999): 1369–79. http://dx.doi.org/10.1109/49.779920.

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45

Wang, Yibing, Markos Papageorgiou, and Albert Messmer. "Predictive Feedback Routing Control Strategy for Freeway Network Traffic." Transportation Research Record: Journal of the Transportation Research Board 1856, no. 1 (January 2003): 62–73. http://dx.doi.org/10.3141/1856-07.

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Available routing strategies for freeway networks may be classified as feedback and iterative strategies. Feedback strategies base their routing decisions on real-time measurable or estimable information only, via employment of simple regulators, while iterative strategies run a freeway network model repeatedly to achieve exact user equilibrium conditions over a future time horizon. A predictive feedback routing control strategy was developed with the aim of incorporating the advantages of both classes of strategies on the one hand and attenuating their disadvantages on the other hand. The new strategy runs a mathematical model only once at each time step and bases its routing decisions on the predicted instead of the currently prevailing traffic conditions. The investigations indicate that satisfactory routing results are achieved by use of this strategy. The corresponding performance evaluation was conducted in detail by comparison with the feedback and iterative strategies.
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46

Karimi, Ahmad. "Multicast Energy Aware Routing ‎in Wireless Networks." Computer Engineering and Applications Journal 5, no. 3 (October 29, 2016): 127–33. http://dx.doi.org/10.18495/comengapp.v5i3.184.

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Multicasting is a service for disseminating data to a group of hostsand it is of paramount importance in applications with a close collabo-ration of network hosts. Due to limited energy available in the wirelessdevices, energy management is one of the most important problems inwireless networks. Energy aware routing strategies help us to mini-mize the energy costs for communication as much as possible and toincrease the network lifetime. In this paper, we address the problemof energy efficient routing to increase the lifetime of the network. Wepresent three new strategies for online multicast energy aware routingin wireless networks to increase the network lifetime.
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47

Khan, Anwar, Ihsan Ali, Abdullah Ghani, Nawsher Khan, Mohammed Alsaqer, Atiq Rahman, and Hasan Mahmood. "Routing Protocols for Underwater Wireless Sensor Networks: Taxonomy, Research Challenges, Routing Strategies and Future Directions." Sensors 18, no. 5 (May 18, 2018): 1619. http://dx.doi.org/10.3390/s18051619.

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48

Wahid, Ishtiaq, Sadaf Tanvir, Masood Ahmad, Fasee Ullah, Ahmed S. AlGhamdi, Murad Khan, and Sultan S. Alshamrani. "Vehicular Ad Hoc Networks Routing Strategies for Intelligent Transportation System." Electronics 11, no. 15 (July 23, 2022): 2298. http://dx.doi.org/10.3390/electronics11152298.

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The upcoming models of vehicles will be able to communicate with each other and will thus be able to share and/or transfer information. A vehicular ad hoc network (VANET) is an application of this vehicular communication that leads to an intelligent transportation system (ITS). Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) are the two distinct types of vehicular ad hoc networks (VANET). V2V and V2I technologies are together known as V2X and are recently being tested. Continuous research to enhance routing considers different characteristics and exciting aspects of VANETs. The proposed schemes are classified based on the operational scenario. A survey of proposed routing schemes in the last eight years is presented to determine the design considerations and the approach used in every proposed system, along with their shortcomings. This survey will assist new scholars in this field to analyze existing state-of-the-art systems. The table at the end of each routing scheme shows the proposed routing scheme’s simulation, routing, and scenario parameters. This paper also reviews VANET technology, its role in the intelligent transportation system, recent development in the field, and the timeline for implementation of the system.
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49

SHAO, Xun, Go HASEGAWA, Yoshiaki TANIGUCHI, and Hirotaka NAKANO. "The Implications of Overlay Routing for ISPs' Peering Strategies." IEICE Transactions on Information and Systems E96.D, no. 5 (2013): 1115–24. http://dx.doi.org/10.1587/transinf.e96.d.1115.

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50

Tsai, Jung Tsung, and Yunghsiang Han. "Alternative Forwarding Strategies for Geographic Routing in Wireless Networks." International Journal of Ad Hoc and Ubiquitous Computing 1, no. 1 (2016): 1. http://dx.doi.org/10.1504/ijahuc.2016.10001854.

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