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A Performance based Routing Classification in Cognitive Radio Networks

Sunita S.Barve, Parag Kulkarni
2012 International Journal of Computer Applications  
Next, a detailed classification of the routing strategies is given according to performance evaluation matrices which are considered according to specific demand and requirements of network users.  ...  First, the overview of the routing with its unique challenges is given under the restriction of interference and fairness to increase overall network throughput.  ...  It is also clear that Dual Reinforcement Routing can improve the performance and time to learn good route with the help of forward and backward exploration.  ... 
doi:10.5120/6370-8762 fatcat:i3hnucj4kbeghkjwcamhddjl5m

Applications of Prediction approaches in Wireless Sensor Networks [chapter]

Felicia Engmann, Kofi Sarpong Adu-Manu, Jamal-Deen Abdulai, Ferdinand Apietu Katsriku
2021 Wireless Sensor Networks - Design, Deployment and Applications [Working Title]  
Wireless Sensor Networks (WSNs) collect data and continuously monitor ambient data such as temperature, humidity and light.  ...  The type of deployment environment is also and the network topology also contributes to the depletion of nodes which threatens the lifetime and the also the performance of the network.  ...  These strategies learn the behavior of the networks and improve on its experiences of the environment without explicit programming.  ... 
doi:10.5772/intechopen.94500 fatcat:b6r7n74bxvcajffbyvoekm5fmq

Wireless Sensor Network as a Mesh: Vision and Challenges

Zhanserik Nurlan, Tamara Zhukabayeva, Mohamed Othman, Aigul Adamova, Nurkhat Zhakiyev
2021 IEEE Access  
Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.  ...  In addition novel AI approaches such as Machine learning, Deep learning, and Reinforcement learning will be investigated to be contributed in new generation sensor networks [224] [225] [226] [227] [228  ...  Cameras capable of creating a mesh network between cameras are available from vendors such as Cuddleback Dual [163] . D.  ... 
doi:10.1109/access.2021.3137341 fatcat:r5cuedgjgbd2dfljgmmdislwly

A Survey on Integrated Access and Backhaul Networks [article]

Yongqiang Zhang, Mustafa A. Kishk, Mohamed-Slim Alouini
2021 arXiv   pre-print
Benefiting from the usage of the high-frequency band, utilizing part of the large available bandwidth for wireless backhauling is feasible without considerable performance sacrifice.  ...  After that, we survey existing research on IAB networks, the integrations of IAB to cache-enabled network, optical communication transport network, and the non-terrestrial network.  ...  usage of deep reinforcement learning (DRL) to derive the optimal solution in [51] .  ... 
arXiv:2101.01286v1 fatcat:tlclycckznel5huuxuwcnn6ysm

On the implementation, deployment and evaluation of a networking protocol for VANETs: The VARON case

M. Isabel Sanchez, Marco Gramaglia, Carlos J. Bernardos, Antonio de la Oliva, Maria Calderon
2014 Ad hoc networks  
We believe that the experience and lessons learned during this process $ do not only apply to VARON, but also to other multi-hop wireless vehicular communication solutions, and that therefore these lessons  ...  However, simulation tools may not reflect properly the highly dynamic and complex characteristics of the vehicular scenario.  ...  Giust for their help during the testing phase.  ... 
doi:10.1016/j.adhoc.2014.02.001 fatcat:o5lnnpronfbqfflylfj4bzvomm

Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligence [article]

Peng Wei, Kun Guo, Ye Li, Jue Wang, Wei Feng, Shi Jin, Ning Ge, Ying-Chang Liang
2022 arXiv   pre-print
However, its uncertainty, referred to as dynamic and randomness, from the mobile device, wireless channel, and edge network sides, results in high-dimensional, nonconvex, nonlinear, and NP-hard optimization  ...  Thanks to the evolved reinforcement learning (RL), upon iteratively interacting with the dynamic and random environment, its trained agent can intelligently obtain the optimal policy in MEC.  ...  service (QoS) and quality of experience (QoE), are categorized and explained.  ... 
arXiv:2201.11410v4 fatcat:24igkq4kbrb2pjzwf3mf3n7qtq

Dynamic Routings in Satellite Networks: An Overview

Xiaoli Cao, Yitao Li, Xingzhong Xiong, Jun Wang
2022 Sensors  
the study of the routing methods in satellite networks a research hotspot.  ...  Therefore, this paper investigates the latest existing routing works to tackle the dynamic routing problems in satellite networks.  ...  With reinforcement learning techniques, the SDN can fully combine the centralized control capability of the SDN and the dynamic adaptive capability of reinforcement learning.  ... 
doi:10.3390/s22124552 pmid:35746331 pmcid:PMC9231381 fatcat:lmac3ns7dnfgbj3232nlyxik7u

A Review of AI-enabled Routing Protocols for UAV Networks: Trends, Challenges, and Future Outlook [article]

Arnau Rovira-Sugranes, Abolfazl Razi, Fatemeh Afghah, Jacob Chakareski
2021 arXiv   pre-print
highly-dynamic network topology.  ...  This paper reviews AI-enabled routing protocols designed primarily for aerial networks, including topology-predictive and self-adaptive learning-based routing algorithms, with an emphasis on accommodating  ...  Perception and Energy Awareness OLSR (MPEAOLSR) [170] , Dynamic Dual Reinforcement Learning Routing (DDRLR) [171] , Destination Sequenced Distance Vector (DSDV) [172] , BABEL [173] , Cluster head  ... 
arXiv:2104.01283v2 fatcat:p2vgtckponfm5flwz4dscf7dju

Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligence

Peng Wei, Kun Guo, Ye Li, Jue Wang, Wei Feng, Shi Jin, Ning Ge, Ying-Chang Liang
2022 IEEE Access  
INDEX TERMS Mobile edge computing (MEC), network uncertainty, reinforcement learning (RL).  ...  However, its uncertainty, referred to as dynamic and randomness, from the mobile device, wireless channel, and edge network sides, results in high-dimensional, nonconvex, nonlinear, and NP-hard optimization  ...  degrades the QoS or quality of experience (QoE) performance for delay-sensitive and computation-intensive applications.  ... 
doi:10.1109/access.2022.3183647 fatcat:pd5z6q4innd5jl25g4r7b4nq3i

2020 Index IEEE Transactions on Wireless Communications Vol. 19

2020 IEEE Transactions on Wireless Communications  
for MIMO Systems in Dynamic Environments; TWC June 2020 3643-3657 Huang, C., see Yang, M., TWC Sept. 2020 5860-5874 Huang, D., Tao, X., Jiang, C., Cui, S., and Lu, J  ...  ., Joint Access and Backhaul Resource Management in Satellite-Drone Networks: A Competitive Market Approach; TWC June 2020 3908-3923 Hu, Y.H., see Xia, M., TWC June 2020 3769-3781 Hua, C., see Li, M  ...  ., +, TWC Aug. 2020 5132-5147 Multi-Agent Reinforcement Learning for Adaptive User Association in Dynamic mmWave Networks.  ... 
doi:10.1109/twc.2020.3044507 fatcat:ie4rwz4dgvaqbaxf3idysubc54

2021 Index IEEE Transactions on Wireless Communications Vol. 20

2021 IEEE Transactions on Wireless Communications  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination.  ...  Yan, J., +, TWC July 2021 4495-4512 QoS-Aware Secure Routing Design for Wireless Networks With Selfish Jammers.  ... 
doi:10.1109/twc.2021.3135649 fatcat:bgd3vzb7pbee7jp75dnbucihmq

Ant Algorithms for Routing in Wireless Multi-Hop Networks [chapter]

Martina Umlauft, Wilfried Elmenreich
2022 The Application of Ant Colony Optimization  
Due to their distributed nature, routing algorithms for these types of networks have to be self-organized.  ...  This chapter provides an introduction to Wireless Multi-Hop Networks, their specific challenges, and an overview of the ant algorithms available for routing in such networks.  ...  Acknowledgements The publication of this chapter was supported by the University of Klagenfurt.  ... 
doi:10.5772/intechopen.99682 fatcat:gksvgdfvfrf6fnpspqj3srm67a

A Survey on Integrated Access and Backhaul Networks

Yongqiang Zhang, Mustafa A. Kishk, Mohamed-Slim Alouini
2021 Frontiers in Communications and Networks  
Benefiting from the usage of the high-frequency band, utilizing part of the large available bandwidth for wireless backhauling is feasible without considerable performance sacrifice.  ...  networks.  ...  usage of deep reinforcement learning (DRL) to derive the optimal solution in (Lei et al., 2020) .  ... 
doi:10.3389/frcmn.2021.647284 fatcat:fx4on6was5ghlokplkrugowgye

A Dyna-Q-Based Solution for UAV Networks Against Smart Jamming Attacks

Li, Lu, Shi, Wang, Qiao, Liu
2019 Symmetry  
Built on the top of the SDN, the state information of the whole network converges quickly and is fitted to an environment model used to develop an improved Dyna-Q-based reinforcement learning algorithm  ...  and potential attackers of UAV networks.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/sym11050617 fatcat:7arl5urblrhqtoscbmykipqv7i

2019 Index IEEE Transactions on Industrial Informatics Vol. 15

2019 IEEE Transactions on Industrial Informatics  
He, J., +, TII Feb. 2019 1226-1233 Mesh generation A Distributed Position-Based Routing Algorithm in 3-D Wireless Industrial Internet of Things.  ...  Joint Learning of Degradation Assessment and RUL Prediction for Aeroengines via Dual-Task Deep LSTM Networks.  ... 
doi:10.1109/tii.2020.2968165 fatcat:utk3ywxc6zgbdbfsys5f4otv7u
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