{"id":{"repo_id":"carleton","oai_identifier":"oai:carleton.scholaris.ca:20.500.14718/41836"},"canonical_url":"https://search.dev.ndltd.org/etd/carleton/oai:carleton.scholaris.ca:20.500.14718/41836","repository":{"repo_id":"carleton","name":"Carleton University","base_url":"https://carleton.scholaris.ca/server/oai/request"},"display":{"title":"Adaptive Multicast Routing Protocol Based on Reinforcement Learning","abstract":"Current Mobile Ad-hoc Network (MANET) multicasting approaches either suffer from low Packet Delivery Ratio (PDR) or high overhead. These methods rely on metrics like hop count to find the optimal path to the destination. Once the path is selected, all packets are sent over the same path as long as it is available. However, a path that is deemed optimal at a specific instance of time may not retain its optimality at a subsequent moment due to node mobility. Moreover, using a metric like hop count that does not consider link quality can lead to poor PDR, as it can favor an unreliable path over a reliable one just because it is the shortest. To tackle these concerns, Q-Learning Adaptive - Multicast Ad hoc On-Demand Distance Vector Routing (QLA-MAODV) protocol is proposed. It is an adaptive and bandwidth-efficient multicast routing protocol based on Q-learning. Unlike traditional methods, QLA-MAODV prioritizes link reliability over simple metrics like hop count, aiming to build a more stable multicast tree. The protocol utilizes the periodic Group Hello (GRPH) messages to explore the environment, identifying alternative paths for use in case of path degradation. The protocol continuously updates path costs, facilitating proactive switching to more reliable paths. Simulations in Network Simulator 3 (NS-3) reveal the protocol's superiority over the traditional Multicast Ad-hoc On-demand Distance Vector (MAODV) protocol. Additionally, it outperforms a modified version, MAODV-Route Reliability (MAODV-RR), that uses link reliability as the routing metric, demonstrating improvement in PDR and reduced multicast-related overhead.","abstract_html":"Current Mobile Ad-hoc Network (MANET) multicasting approaches either suffer from low Packet Delivery Ratio (PDR) or high overhead. These methods rely on metrics like hop count to find the optimal path to the destination. Once the path is selected, all packets are sent over the same path as long as it is available. However, a path that is deemed optimal at a specific instance of time may not retain its optimality at a subsequent moment due to node mobility. Moreover, using a metric like hop count that does not consider link quality can lead to poor PDR, as it can favor an unreliable path over a reliable one just because it is the shortest. To tackle these concerns, Q-Learning Adaptive - Multicast Ad hoc On-Demand Distance Vector Routing (QLA-MAODV) protocol is proposed. It is an adaptive and bandwidth-efficient multicast routing protocol based on Q-learning. Unlike traditional methods, QLA-MAODV prioritizes link reliability over simple metrics like hop count, aiming to build a more stable multicast tree. The protocol utilizes the periodic Group Hello (GRPH) messages to explore the environment, identifying alternative paths for use in case of path degradation. The protocol continuously updates path costs, facilitating proactive switching to more reliable paths. Simulations in Network Simulator 3 (NS-3) reveal the protocol's superiority over the traditional Multicast Ad-hoc On-demand Distance Vector (MAODV) protocol. Additionally, it outperforms a modified version, MAODV-Route Reliability (MAODV-RR), that uses link reliability as the routing metric, demonstrating improvement in PDR and reduced multicast-related overhead.","abstract_has_math":false,"creators":["Mohammed, Ola Ashour Mohammed"],"institution":"Carleton University","degree_name":"Doctor of Philosophy (Ph.D.)","degree_level":"Doctoral","degree_discipline":"Engineering, Electrical and Computer","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024","date_published":"2024","updated_at":"2026-07-24T01:34:24Z","subjects":[],"languages":["en"],"rights":["Copyright © 2024 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, distribution to students, research and scholarship. Theses may only be shared by linking to the Carleton University Institutional Repository and no part may be copied without proper attribution to the author; no part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner."],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.doi","label":"DOI","values":["10.22215/etd/2024-15939"],"render_values":[{"text":"10.22215/etd/2024-15939","href":"https://doi.org/10.22215/etd/2024-15939","code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/20.500.14718/41836","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Mohammed, Ola Ashour Mohammed"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-04-08T20:26:28Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-04-08T20:26:28Z"]},{"key":"dc:date.issued","label":"Date","values":["2024"]},{"key":"dc:publisher","label":"Institution","values":["Carleton University"]},{"key":"dc:type","label":"Dc Type","values":["thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Engineering, Electrical and Computer"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (Ph.D.)"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright © 2024 the author(s). 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Once the path is selected, all packets are sent over the same path as long as it is available. However, a path that is deemed optimal at a specific instance of time may not retain its optimality at a subsequent moment due to node mobility. Moreover, using a metric like hop count that does not consider link quality can lead to poor PDR, as it can favor an unreliable path over a reliable one just because it is the shortest. To tackle these concerns, Q-Learning Adaptive - Multicast Ad hoc On-Demand Distance Vector Routing (QLA-MAODV) protocol is proposed. It is an adaptive and bandwidth-efficient multicast routing protocol based on Q-learning. Unlike traditional methods, QLA-MAODV prioritizes link reliability over simple metrics like hop count, aiming to build a more stable multicast tree. The protocol utilizes the periodic Group Hello (GRPH) messages to explore the environment, identifying alternative paths for use in case of path degradation. The protocol continuously updates path costs, facilitating proactive switching to more reliable paths. Simulations in Network Simulator 3 (NS-3) reveal the protocol's superiority over the traditional Multicast Ad-hoc On-demand Distance Vector (MAODV) protocol. Additionally, it outperforms a modified version, MAODV-Route Reliability (MAODV-RR), that uses link reliability as the routing metric, demonstrating improvement in PDR and reduced multicast-related overhead."]},{"key":"dc:title","label":"Title","values":["Adaptive Multicast Routing Protocol Based on Reinforcement Learning"]}]}],"canonical_facts":{"dc:creator":["Mohammed, Ola Ashour Mohammed"],"dc:date.accessioned":["2025-04-08T20:26:28Z"],"dc:date.available":["2025-04-08T20:26:28Z"],"dc:date.issued":["2024"],"dc:description.abstract":["Current Mobile Ad-hoc Network (MANET) multicasting approaches either suffer from low Packet Delivery Ratio (PDR) or high overhead. These methods rely on metrics like hop count to find the optimal path to the destination. Once the path is selected, all packets are sent over the same path as long as it is available. However, a path that is deemed optimal at a specific instance of time may not retain its optimality at a subsequent moment due to node mobility. Moreover, using a metric like hop count that does not consider link quality can lead to poor PDR, as it can favor an unreliable path over a reliable one just because it is the shortest. To tackle these concerns, Q-Learning Adaptive - Multicast Ad hoc On-Demand Distance Vector Routing (QLA-MAODV) protocol is proposed. It is an adaptive and bandwidth-efficient multicast routing protocol based on Q-learning. Unlike traditional methods, QLA-MAODV prioritizes link reliability over simple metrics like hop count, aiming to build a more stable multicast tree. The protocol utilizes the periodic Group Hello (GRPH) messages to explore the environment, identifying alternative paths for use in case of path degradation. The protocol continuously updates path costs, facilitating proactive switching to more reliable paths. Simulations in Network Simulator 3 (NS-3) reveal the protocol's superiority over the traditional Multicast Ad-hoc On-demand Distance Vector (MAODV) protocol. Additionally, it outperforms a modified version, MAODV-Route Reliability (MAODV-RR), that uses link reliability as the routing metric, demonstrating improvement in PDR and reduced multicast-related overhead."],"dc:identifier.doi":["10.22215/etd/2024-15939"],"dc:identifier.uri":["https://hdl.handle.net/20.500.14718/41836"],"dc:language.iso":["en"],"dc:publisher":["Carleton University"],"dc:rights":["Copyright © 2024 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, distribution to students, research and scholarship. Theses may only be shared by linking to the Carleton University Institutional Repository and no part may be copied without proper attribution to the author; no part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner."],"dc:title":["Adaptive Multicast Routing Protocol Based on Reinforcement Learning"],"dc:type":["thesis"],"thesis:degree_discipline":["Engineering, Electrical and Computer"],"thesis:degree_level":["Doctoral"],"thesis:degree_name":["Doctor of Philosophy (Ph.D.)"]},"updated_at":"2026-07-24T01:34:24Z"}