Mobility, Residual Energy, and Link Quality Aware Multipath Routing in MANETs with Q-learning Algorithm
To facilitate connectivity to the internet, the easiest way to establish communication infrastructure in areas affected by natural disaster and in remote locations with intermittent cellular services and/or lack of Wi-Fi coverage is to deploy an end-to-end connection over Mobile Ad-hoc Networks (MAN...
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MDPI
2019
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| author | Tilwari, Valmik Dimyati, Kaharudin Hindia, Mhd Nour Fattouh, Anas Amiri, Iraj |
| author_facet | Tilwari, Valmik Dimyati, Kaharudin Hindia, Mhd Nour Fattouh, Anas Amiri, Iraj |
| author_sort | Tilwari, Valmik |
| collection | UM |
| description | To facilitate connectivity to the internet, the easiest way to establish communication infrastructure in areas affected by natural disaster and in remote locations with intermittent cellular services and/or lack of Wi-Fi coverage is to deploy an end-to-end connection over Mobile Ad-hoc Networks (MANETs). However, the potentials of MANETs are yet to be fully realized as existing MANETs routing protocols still suffer some major technical drawback in the areas of mobility, link quality, and battery constraint of mobile nodes between the overlay connections. To address these problems, a routing scheme named Mobility, Residual energy and Link quality Aware Multipath (MRLAM) is proposed for routing in MANETs. The proposed scheme makes routing decisions by determining the optimal route with energy efficient nodes to maintain the stability, reliability, and lifetime of the network over a sustained period of time. The MRLAM scheme uses a Q-Learning algorithm for the selection of optimal intermediate nodes based on the available status of energy level, mobility, and link quality parameters, and then provides positive and negative reward values accordingly. The proposed routing scheme reduces energy cost by 33% and 23%, end to end delay by 15% and 10%, packet loss ratio by 30.76% and 24.59%, and convergence time by 16.49% and 11.34% approximately, compared with other well-known routing schemes such as Multipath Optimized Link State Routing protocol (MP-OLSR) and MP-OLSRv2, respectively. Overall, the acquired results indicate that the proposed MRLAM routing scheme significantly improves the overall performance of the network. © 2019 by the authors. |
| first_indexed | 2024-03-06T05:59:18Z |
| format | Article |
| id | um.eprints-23238 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T05:59:18Z |
| publishDate | 2019 |
| publisher | MDPI |
| record_format | dspace |
| spelling | um.eprints-232382019-12-17T02:18:46Z http://eprints.um.edu.my/23238/ Mobility, Residual Energy, and Link Quality Aware Multipath Routing in MANETs with Q-learning Algorithm Tilwari, Valmik Dimyati, Kaharudin Hindia, Mhd Nour Fattouh, Anas Amiri, Iraj TK Electrical engineering. Electronics Nuclear engineering To facilitate connectivity to the internet, the easiest way to establish communication infrastructure in areas affected by natural disaster and in remote locations with intermittent cellular services and/or lack of Wi-Fi coverage is to deploy an end-to-end connection over Mobile Ad-hoc Networks (MANETs). However, the potentials of MANETs are yet to be fully realized as existing MANETs routing protocols still suffer some major technical drawback in the areas of mobility, link quality, and battery constraint of mobile nodes between the overlay connections. To address these problems, a routing scheme named Mobility, Residual energy and Link quality Aware Multipath (MRLAM) is proposed for routing in MANETs. The proposed scheme makes routing decisions by determining the optimal route with energy efficient nodes to maintain the stability, reliability, and lifetime of the network over a sustained period of time. The MRLAM scheme uses a Q-Learning algorithm for the selection of optimal intermediate nodes based on the available status of energy level, mobility, and link quality parameters, and then provides positive and negative reward values accordingly. The proposed routing scheme reduces energy cost by 33% and 23%, end to end delay by 15% and 10%, packet loss ratio by 30.76% and 24.59%, and convergence time by 16.49% and 11.34% approximately, compared with other well-known routing schemes such as Multipath Optimized Link State Routing protocol (MP-OLSR) and MP-OLSRv2, respectively. Overall, the acquired results indicate that the proposed MRLAM routing scheme significantly improves the overall performance of the network. © 2019 by the authors. MDPI 2019 Article PeerReviewed Tilwari, Valmik and Dimyati, Kaharudin and Hindia, Mhd Nour and Fattouh, Anas and Amiri, Iraj (2019) Mobility, Residual Energy, and Link Quality Aware Multipath Routing in MANETs with Q-learning Algorithm. Applied Sciences, 9 (8). p. 1582. ISSN 2076-3417, DOI https://doi.org/10.3390/app9081582 <https://doi.org/10.3390/app9081582>. https://doi.org/10.3390/app9081582 doi:10.3390/app9081582 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Tilwari, Valmik Dimyati, Kaharudin Hindia, Mhd Nour Fattouh, Anas Amiri, Iraj Mobility, Residual Energy, and Link Quality Aware Multipath Routing in MANETs with Q-learning Algorithm |
| title | Mobility, Residual Energy, and Link Quality Aware Multipath Routing in MANETs with Q-learning Algorithm |
| title_full | Mobility, Residual Energy, and Link Quality Aware Multipath Routing in MANETs with Q-learning Algorithm |
| title_fullStr | Mobility, Residual Energy, and Link Quality Aware Multipath Routing in MANETs with Q-learning Algorithm |
| title_full_unstemmed | Mobility, Residual Energy, and Link Quality Aware Multipath Routing in MANETs with Q-learning Algorithm |
| title_short | Mobility, Residual Energy, and Link Quality Aware Multipath Routing in MANETs with Q-learning Algorithm |
| title_sort | mobility residual energy and link quality aware multipath routing in manets with q learning algorithm |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| work_keys_str_mv | AT tilwarivalmik mobilityresidualenergyandlinkqualityawaremultipathroutinginmanetswithqlearningalgorithm AT dimyatikaharudin mobilityresidualenergyandlinkqualityawaremultipathroutinginmanetswithqlearningalgorithm AT hindiamhdnour mobilityresidualenergyandlinkqualityawaremultipathroutinginmanetswithqlearningalgorithm AT fattouhanas mobilityresidualenergyandlinkqualityawaremultipathroutinginmanetswithqlearningalgorithm AT amiriiraj mobilityresidualenergyandlinkqualityawaremultipathroutinginmanetswithqlearningalgorithm |