Two Tier Hybrid Routing Schemes for Vehicular Ad Hoc Networks Based on Reinforcement Learning
| dc.contributor.advisor | Datta, Suprakash | |
| dc.contributor.author | Boroujeni, Narges Haghighati | |
| dc.date.accessioned | 2021-11-15T15:19:25Z | |
| dc.date.available | 2021-11-15T15:19:25Z | |
| dc.date.copyright | 2021-05 | |
| dc.date.issued | 2021-11-15 | |
| dc.date.updated | 2021-11-15T15:19:25Z | |
| dc.degree.discipline | Electrical and Computer Engineering | |
| dc.degree.level | Master's | |
| dc.degree.name | MASc - Master of Applied Science | |
| dc.description.abstract | This research addresses the issue of routing messages from vehicles to specific geographical locations in VANETs. To overcome this challenge we propose a hybrid system consisting of traffic-aware located RSUs and a reinforcement learning routing strategy. Our proposed method includes two main tiers. We first divided the geographical area into equal-sized grids; then using the traffic flow patterns of the area, the protocol will suggest the optimal locations for RSUs with the objective of minimizing the number of RSUs in the system to cut down the cost while improving the delivery ratio at the lowest possible delay. | |
| dc.identifier.uri | http://hdl.handle.net/10315/38668 | |
| dc.language | en | |
| dc.rights | Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests. | |
| dc.subject | Computer science | |
| dc.subject.keywords | Vehicular ad hoc network | |
| dc.subject.keywords | routing | |
| dc.subject.keywords | position-based routing | |
| dc.subject.keywords | Q-learning | |
| dc.title | Two Tier Hybrid Routing Schemes for Vehicular Ad Hoc Networks Based on Reinforcement Learning | |
| dc.type | Electronic Thesis or Dissertation |
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