
@article{ahmedFairnessAwareGroup2011,
  title = {Fairness {{Aware Group Proportional Frequency Domain Resource Allocation}} in {{L}}-{{SC}}-{{FDMA Based Uplink}}},
  author = {Ahmed, Irfan and Mohamed, Amr},
  date = {2011},
  journaltitle = {IJCNS},
  volume = {04},
  number = {08},
  pages = {487--494},
  issn = {1913-3715, 1913-3723},
  doi = {10.4236/ijcns.2011.48060},
  url = {http://www.scirp.org/journal/doi.aspx?DOI=10.4236/ijcns.2011.48060},
  urldate = {2021-05-20},
  file = {/home/leopold/snap/zotero-snap/common/Zotero/storage/H8M4DAE6/Ahmed et Mohamed - 2011 - Fairness Aware Group Proportional Frequency Domain.pdf}
}

@incollection{ancillottiRTTBasedCongestionControl2018,
  title = {{{RTT}}-{{Based Congestion Control}} for the {{Internet}} of {{Things}}},
  booktitle = {Wired/{{Wireless Internet Communications}}},
  author = {Ancillotti, Emilio and Bolettieri, Simone and Bruno, Raffaele},
  editor = {Chowdhury, Kaushik Roy and Di Felice, Marco and Matta, Ibrahim and Sheng, Bo},
  date = {2018},
  series = {Lecture {{Notes}} in {{Computer Science}}},
  volume = {10866},
  pages = {3--15},
  publisher = {{Springer International Publishing}},
  location = {{Cham}},
  doi = {10.1007/978-3-030-02931-9_1},
  url = {http://link.springer.com/10.1007/978-3-030-02931-9_1},
  urldate = {2021-05-20},
  abstract = {The design of scalable and reliable transport protocols for IoT environments is still an unsolved issue. A simple stop-and-wait congestion control method and a lightweight reliability mechanism are only implemented in CoAP, an application protocol that provides standardised RESTful services for IoT devices. Inspired by delay-based congestion control algorithms that have been proposed for the TCP, in this work we propose a rate control technique that leverages measurements of roundtrip times (RTTs) to infer network state and to determine the flow rate that would prevent network congestion. Our key idea is that the growth of RTT variance, coupled with thresholds on CoAP message losses, is an effective way to detect the onset of network congestion. To validate our approach, we conduct a comparative performance analysis with the two loss-based congestion control methods of standard CoAP under different application scenarios. Results show that our solution outperforms the alternative methods, with a significant improvement of fairness and robustness against unacknowledged traffic.},
  isbn = {978-3-030-02930-2 978-3-030-02931-9},
  langid = {english},
  file = {/home/leopold/snap/zotero-snap/common/Zotero/storage/V8MFYI2R/Ancillotti et al. - 2018 - RTT-Based Congestion Control for the Internet of T.pdf}
}

@inproceedings{ancillottiRTTBasedCongestionControl2018a,
  title = {{{RTT}}-{{Based Congestion Control}} for the {{Internet}} of {{Things}}},
  author = {Ancillotti, Emilio and Bolettieri, Simone and Bruno, Raffaele},
  date = {2018-06-18},
  volume = {LNCS-10866},
  pages = {3},
  publisher = {{Springer International Publishing}},
  doi = {10.1007/978-3-030-02931-9_1},
  url = {https://hal.inria.fr/hal-02269740},
  urldate = {2021-05-26},
  abstract = {The design of scalable and reliable transport protocols for IoT environments is still an unsolved issue. A simple stop-and-wait congestion control method and a lightweight reliability mechanism are only implemented in CoAP, an application protocol that provides standardised RESTful services for IoT devices. Inspired by delay-based congestion control algorithms that have been proposed for the TCP, in this work we propose a rate control technique that leverages measurements of round-trip times (RTTs) to infer network state and to determine the flow rate that would prevent network congestion. Our key idea is that the growth of RTT variance, coupled with thresholds on CoAP message losses, is an effective way to detect the onset of network congestion. To validate our approach, we conduct a comparative performance analysis with the two loss-based congestion control methods of standard CoAP under different application scenarios. Results show that our solution outperforms the alternative methods, with a significant improvement of fairness and robustness against unacknowledged traffic.},
  eventtitle = {International {{Conference}} on {{Wired}}/{{Wireless Internet Communication}} ({{WWIC}})},
  langid = {english},
  file = {/home/leopold/snap/zotero-snap/common/Zotero/storage/PEFCKHKA/Ancillotti et al. - 2018 - RTT-Based Congestion Control for the Internet of T.pdf;/home/leopold/snap/zotero-snap/common/Zotero/storage/FHBCKQBZ/hal-02269740.html}
}

@article{bormannBlockWiseTransfersConstrained2016,
  title = {Block-{{Wise Transfers}} in the {{Constrained Application Protocol}} ({{CoAP}})},
  author = {Bormann, C. and Z. Shelby, Ed},
  date = {2016},
  number = {RFC 7959},
  issn = {2070-1721},
  url = {https://www.rfc-editor.org/info/rfc7959},
  urldate = {2021-06-06},
  file = {/home/leopold/snap/zotero-snap/common/Zotero/storage/HPKFQFKU/rfc7959.html}
}

@report{bormannTerminologyConstrainedNodeNetworks2014,
  title = {Terminology for {{Constrained}}-{{Node Networks}}},
  author = {Bormann, C. and Ersue, M. and Keranen, A.},
  date = {2014-05},
  number = {RFC7228},
  pages = {RFC7228},
  institution = {{RFC Editor}},
  doi = {10.17487/rfc7228},
  url = {https://www.rfc-editor.org/info/rfc7228},
  urldate = {2021-06-06},
  langid = {english},
  file = {/home/leopold/snap/zotero-snap/common/Zotero/storage/KXAF4RDI/Bormann et al. - 2014 - Terminology for Constrained-Node Networks.pdf}
}

@report{bradnerKeyWordsUse1997,
  title = {Key Words for Use in {{RFCs}} to {{Indicate Requirement Levels}}},
  author = {Bradner, S.},
  date = {1997-03},
  number = {RFC2119},
  pages = {RFC2119},
  institution = {{RFC Editor}},
  doi = {10.17487/rfc2119},
  url = {https://www.rfc-editor.org/info/rfc2119},
  urldate = {2021-06-06},
  langid = {english},
  file = {/home/leopold/snap/zotero-snap/common/Zotero/storage/CSVTPM96/Bradner - 1997 - Key words for use in RFCs to Indicate Requirement .pdf}
}

@online{brockmanOpenAIGym2016,
  title = {{{OpenAI Gym}}},
  author = {Brockman, Greg and Cheung, Vicki and Pettersson, Ludwig and Schneider, Jonas and Schulman, John and Tang, Jie and Zaremba, Wojciech},
  date = {2016-06-05},
  eprint = {1606.01540},
  eprinttype = {arxiv},
  primaryclass = {cs},
  url = {http://arxiv.org/abs/1606.01540},
  urldate = {2021-08-19},
  abstract = {OpenAI Gym1 is a toolkit for reinforcement learning research. It includes a growing collection of benchmark problems that expose a common interface, and a website where people can share their results and compare the performance of algorithms. This whitepaper discusses the components of OpenAI Gym and the design decisions that went into the software.},
  archiveprefix = {arXiv},
  langid = {english},
  keywords = {Computer Science - Artificial Intelligence,Computer Science - Machine Learning},
  file = {/home/leopold/snap/zotero-snap/common/Zotero/storage/L6IXPMUV/Brockman et al. - 2016 - OpenAI Gym.pdf}
}

@inreference{FairnessMeasure2020,
  title = {Fairness Measure},
  booktitle = {Wikipedia},
  date = {2020-12-11T15:23:39Z},
  url = {https://en.wikipedia.org/w/index.php?title=Fairness_measure&oldid=993616223},
  urldate = {2021-05-20},
  abstract = {Fairness measures or metrics are used in network engineering to determine whether users or applications are receiving a fair share of system resources. There are several mathematical and conceptual definitions of fairness.},
  langid = {english},
  annotation = {Page Version ID: 993616223},
  file = {/home/leopold/snap/zotero-snap/common/Zotero/storage/P7E9KWUD/index.html}
}

@article{haileEndtoendCongestionControl2021,
  title = {End-to-End Congestion Control Approaches for High Throughput and Low Delay in {{4G}}/{{5G}} Cellular Networks},
  author = {Haile, Habtegebreil and Grinnemo, Karl-Johan and Ferlin, Simone and Hurtig, Per and Brunstrom, Anna},
  date = {2021-02-26},
  journaltitle = {Computer Networks},
  volume = {186},
  pages = {107692},
  issn = {1389-1286},
  doi = {10.1016/j.comnet.2020.107692},
  url = {https://www.sciencedirect.com/science/article/pii/S1389128620312974},
  urldate = {2021-05-17},
  abstract = {Cellular networks have evolved to support high peak bitrates with low loss rates as observed by the higher layers. However, applications and services running over cellular networks are now facing other difficult congestion-related challenges, most notably a highly variable link capacity and bufferbloat. To overcome these issues and improve performance of network traffic in 4G/5G cellular networks, a number of in-network and end-to-end solutions have been proposed. Fairness between interacting congestion control algorithms (CCAs) has played an important role in the type of CCAs considered for research and deployment. The placement of content closer to the user and the allocation of per-user queues in cellular networks has increased the likelihood of a cellular access bottleneck and reduced the extent of flow interaction between multiple users. This has resulted in renewed interest in end-to-end CCAs for cellular networks by opening up room for research and exploration. In this work, we present end-to-end CCAs that target a high throughput and a low latency over highly variable network links, and classify them according to the way they address the congestion control. The work also discusses the deployability of the algorithms. In addition, we provide insights into possible future research directions, such as coping with a higher degree of variability, interaction of CCAs in a shared bottleneck, and avenues for synergized research, such as CCAs assisted by software defined networking and network function virtualization. We hope that this work will serve as a starting point for systematically navigating through the expanding number of cellular CCAs.},
  langid = {english},
  keywords = {4G,5G,Congestion control,Mobile,QUIC,Survey,TCP,Wireless},
  file = {/home/leopold/snap/zotero-snap/common/Zotero/storage/ZYVN97E6/Haile et al. - 2021 - End-to-end congestion control approaches for high .pdf;/home/leopold/snap/zotero-snap/common/Zotero/storage/ZBPCYB5Y/S1389128620312974.html}
}

@report{hartkeObservingResourcesConstrained2015,
  title = {Observing {{Resources}} in the {{Constrained Application Protocol}} ({{CoAP}})},
  author = {Hartke, K.},
  date = {2015-09},
  number = {RFC7641},
  pages = {RFC7641},
  institution = {{RFC Editor}},
  doi = {10.17487/RFC7641},
  url = {https://www.rfc-editor.org/info/rfc7641},
  urldate = {2021-06-06},
  langid = {english},
  file = {/home/leopold/snap/zotero-snap/common/Zotero/storage/8VN6JIFQ/Hartke - 2015 - Observing Resources in the Constrained Application.pdf}
}

@article{hochreiterLongShortTermMemory1997,
  title = {Long {{Short}}-{{Term Memory}}},
  author = {Hochreiter, Sepp and Schmidhuber, Jürgen},
  date = {1997-11-15},
  journaltitle = {Neural Computation},
  volume = {9},
  number = {8},
  pages = {1735--1780},
  issn = {0899-7667},
  doi = {10.1162/neco.1997.9.8.1735},
  url = {https://doi.org/10.1162/neco.1997.9.8.1735},
  urldate = {2021-05-28},
  abstract = {Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O. 1. Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.},
  file = {/home/leopold/snap/zotero-snap/common/Zotero/storage/E29B3BIB/Hochreiter et Schmidhuber - 1997 - Long Short-Term Memory.pdf;/home/leopold/snap/zotero-snap/common/Zotero/storage/CZ2ERT63/Long-Short-Term-Memory.html}
}

@online{jainQuantitativeMeasureFairness1998,
  title = {A {{Quantitative Measure Of Fairness And Discrimination For Resource Allocation In Shared Computer Systems}}},
  author = {Jain, R. and Chiu, D. and Hawe, W.},
  date = {1998-09-24},
  eprint = {cs/9809099},
  eprinttype = {arxiv},
  url = {http://arxiv.org/abs/cs/9809099},
  urldate = {2021-05-20},
  abstract = {Fairness is an important performance criterion in all resource allocation schemes, including those in distributed computer systems. However, it is often specified only qualitatively. The quantitative measures proposed in the literature are either too specific to a particular application, or suffer from some undesirable characteristics. In this paper, we have introduced a quantitative measure called Indiex of FRairness. The index is applicable to any resource sharing or allocation problem. It is independent of the amount of the resource. The fairness index always lies between 0 and 1. This boundedness aids intuitive understanding of the fairness index. For example, a distribution algorithm with a fairness of 0.10 means that it is unfair to 90\% of the users. Also, the discrimination index can be defined as 1 - fairness index.},
  archiveprefix = {arXiv},
  keywords = {C.2.1,Computer Science - Networking and Internet Architecture},
  file = {/home/leopold/snap/zotero-snap/common/Zotero/storage/NZS4CZVM/Jain et al. - 1998 - A Quantitative Measure Of Fairness And Discriminat.pdf;/home/leopold/snap/zotero-snap/common/Zotero/storage/PP7ZG8W3/9809099.html}
}

@inproceedings{jayDeepReinforcementLearning2019,
  title = {A {{Deep Reinforcement Learning Perspective}} on {{Internet Congestion Control}}},
  booktitle = {International {{Conference}} on {{Machine Learning}}},
  author = {Jay, Nathan and Rotman, Noga and Godfrey, Brighten and Schapira, Michael and Tamar, Aviv},
  date = {2019-05-24},
  pages = {3050--3059},
  publisher = {{PMLR}},
  issn = {2640-3498},
  url = {http://proceedings.mlr.press/v97/jay19a.html},
  urldate = {2021-05-17},
  abstract = {We present and investigate a novel and timely application domain for deep reinforcement learning (RL): Internet congestion control. Congestion control is the core networking task of modulating traf...},
  eventtitle = {International {{Conference}} on {{Machine Learning}}},
  langid = {english},
  file = {/home/leopold/snap/zotero-snap/common/Zotero/storage/J3EGXP4X/Jay et al. - 2019 - A Deep Reinforcement Learning Perspective on Inter.pdf;/home/leopold/snap/zotero-snap/common/Zotero/storage/4LBJU9M4/jay19a.html}
}

@report{kushalnagarIPv6LowPowerWireless2007,
  title = {{{IPv6}} over {{Low}}-{{Power Wireless Personal Area Networks}} ({{6LoWPANs}}): {{Overview}}, {{Assumptions}}, {{Problem Statement}}, and {{Goals}}},
  shorttitle = {{{IPv6}} over {{Low}}-{{Power Wireless Personal Area Networks}} ({{6LoWPANs}})},
  author = {Kushalnagar, N. and Montenegro, G. and Schumacher, C.},
  date = {2007-08},
  number = {RFC4919},
  pages = {RFC4919},
  institution = {{RFC Editor}},
  doi = {10.17487/rfc4919},
  url = {https://www.rfc-editor.org/info/rfc4919},
  urldate = {2021-06-06},
  langid = {english},
  file = {/home/leopold/snap/zotero-snap/common/Zotero/storage/EE2RHNFM/Kushalnagar et al. - 2007 - IPv6 over Low-Power Wireless Personal Area Network.pdf}
}

@inproceedings{lanDeepReinforcementLearning2019,
  title = {A {{Deep Reinforcement Learning Based Congestion Control Mechanism}} for {{NDN}}},
  booktitle = {{{ICC}} 2019 - 2019 {{IEEE International Conference}} on {{Communications}} ({{ICC}})},
  author = {Lan, Dehao and Tan, Xiaobin and Lv, Jinyang and Jin, Yang and Yang, Jian},
  date = {2019-05},
  pages = {1--7},
  issn = {1938-1883},
  doi = {10.1109/ICC.2019.8761737},
  abstract = {Named Data Networking (NDN) is an emerging future network architecture that changes the network communication model from push mode to pull mode, which leads to the requirement of a new mechanism of congestion control. To fully exploit the capability of NDN, a suitable congestion control scheme must consider the characteristics of NDN, such as connectionless, in-network caching, content perceptibility, etc. In this paper, firstly, we redefine the congestion control objective for NDN, which considers requirements diversities for different contents. Then we design and develop an efficient congestion control mechanism based on deep reinforcement learning (DRL), namely DRL-based Congestion Control Protocol (DRL-CCP). DRL-CCP enables consumers to automatically learn the optimal congestion control policy from historical congestion control experience. Finally, a real-world test platform with some typical congestion control algorithms for NDN is implemented, and a series of comparative experiments are performed on this platform to verify the performance of DRL-CCP.},
  eventtitle = {{{ICC}} 2019 - 2019 {{IEEE International Conference}} on {{Communications}} ({{ICC}})},
  keywords = {Data models,Deep learning,Neural networks,Protocols,Reinforcement learning,TCPIP,Training},
  file = {/home/leopold/snap/zotero-snap/common/Zotero/storage/JD3BUTCA/8761737.html}
}

@article{leeEnhancementCongestionControl2016,
  title = {Enhancement of Congestion Control of {{Constrained Application Protocol}}/{{Congestion Control}}/{{Advanced}} for {{Internet}} of {{Things}} Environment},
  author = {Lee, Jung and Kim, Kyung and Youn, Hee},
  date = {2016-11-07},
  journaltitle = {International Journal of Distributed Sensor Networks},
  volume = {12},
  doi = {10.1177/1550147716676274},
  abstract = {With the wide spread of Internet of Things, efficient communication between the nodes is getting more important. Constrained Application Protocol was developed to accommodate the resource-constrained nodes and low-power communication links. Being an Internet protocol, Constrained Application Protocol must adhere to congestion control, primarily to keep the backbone network stable. An advanced congestion control mechanism for Constrained Application Protocol, called Congestion Control/Advanced, has also been recently developed. In this article, we propose a new round trip time–based adaptive congestion control scheme, which further improves Congestion Control/Advanced by utilizing the retransmission count in estimating the retransmission timeout value and the lower bound in round trip time variation. An experiment is conducted based on Californium Constrained Application Protocol framework and real devices, and the performance is compared with Constrained Application Protocol, Congestion Control/Advanced, and an existing scheme. It reveals that the proposed scheme significantly increases the throughput and rate of successful transactions in comparison with the other schemes. The approach of utilizing the option field of Constrained Application Protocol enables the proposed scheme to be implemented without any conflict with the existing protocol and extra overhead.},
  file = {/home/leopold/snap/zotero-snap/common/Zotero/storage/ESXYH32D/Lee et al. - 2016 - Enhancement of congestion control of Constrained A.pdf}
}

@article{liQTCPAdaptiveCongestion2019,
  title = {{{QTCP}}: {{Adaptive Congestion Control}} with {{Reinforcement Learning}}},
  shorttitle = {{{QTCP}}},
  author = {Li, Wei and Zhou, Fan and Chowdhury, Kaushik Roy and Meleis, Waleed},
  date = {2019-07-01},
  journaltitle = {IEEE Trans. Netw. Sci. Eng.},
  volume = {6},
  number = {3},
  pages = {445--458},
  issn = {2327-4697, 2334-329X},
  doi = {10.1109/TNSE.2018.2835758},
  url = {https://ieeexplore.ieee.org/document/8357943/},
  urldate = {2021-05-20},
  abstract = {Next generation network access technologies and Internet applications have increased the challenge of providing satisfactory quality of experience for users with traditional congestion control protocols. Efforts on optimizing the performance of TCP by modifying the core congestion control method depending on specific network architectures or apps do not generalize well under a wide range of network scenarios. This limitation arises from the rule-based design principle, where the performance is linked to a pre-decided mapping between the observed state of the network to the corresponding actions. Therefore, these protocols are unable to adapt their behavior in new environments or learn from experience for better performance. We address this problem by integrating a reinforcement-based Q-learning framework with TCP design in our approach called QTCP. QTCP enables senders to gradually learn the optimal congestion control policy in an on-line manner. QTCP does not need hard-coded rules, and can therefore generalize to a variety of different networking scenarios. Moreover, we develop a generalized Kanerva coding function approximation algorithm, which reduces the computation complexity of value functions and the searchable size of the state space. We show that QTCP outperforms the traditional rule-based TCP by providing 59.5\% higher throughput while maintaining low transmission latency.},
  langid = {english},
  file = {/home/leopold/snap/zotero-snap/common/Zotero/storage/DBTMF6SI/Li et al. - 2019 - QTCP Adaptive Congestion Control with Reinforceme.pdf}
}

@article{mccallGeneticAlgorithmsModelling2005,
  title = {Genetic Algorithms for Modelling and Optimisation},
  author = {McCall, John},
  date = {2005-12-01},
  journaltitle = {Journal of Computational and Applied Mathematics},
  series = {Special {{Issue}} on {{Mathematics Applied}} to {{Immunology}}},
  volume = {184},
  number = {1},
  pages = {205--222},
  issn = {0377-0427},
  doi = {10.1016/j.cam.2004.07.034},
  url = {https://www.sciencedirect.com/science/article/pii/S0377042705000774},
  urldate = {2021-05-30},
  abstract = {Genetic algorithms (GAs) are a heuristic search and optimisation technique inspired by natural evolution. They have been successfully applied to a wide range of real-world problems of significant complexity. This paper is intended as an introduction to GAs aimed at immunologists and mathematicians interested in immunology. We describe how to construct a GA and the main strands of GA theory before speculatively identifying possible applications of GAs to the study of immunology. An illustrative example of using a GA for a medical optimal control problem is provided. The paper also includes a brief account of the related area of artificial immune systems.},
  langid = {english},
  keywords = {Evolution,Genetic algorithms,Immunology,Optimisation},
  file = {/home/leopold/snap/zotero-snap/common/Zotero/storage/ZWGD35YA/McCall - 2005 - Genetic algorithms for modelling and optimisation.pdf;/home/leopold/snap/zotero-snap/common/Zotero/storage/VAQQGXRW/S0377042705000774.html}
}

@report{montenegroTransmissionIPv6Packets2007,
  title = {Transmission of {{IPv6 Packets}} over {{IEEE}} 802.15.4 {{Networks}}},
  author = {Montenegro, G. and Kushalnagar, N. and Hui, J. and Culler, D.},
  date = {2007-09},
  number = {RFC4944},
  pages = {RFC4944},
  institution = {{RFC Editor}},
  doi = {10.17487/rfc4944},
  url = {https://www.rfc-editor.org/info/rfc4944},
  urldate = {2021-06-06},
  langid = {english},
  file = {/home/leopold/snap/zotero-snap/common/Zotero/storage/8IBKXRLW/Montenegro et al. - 2007 - Transmission of IPv6 Packets over IEEE 802.15.4 Ne.pdf}
}

@article{naDLTCPDeepLearningBased2019,
  title = {{{DL}}-{{TCP}}: {{Deep Learning}}-{{Based Transmission Control Protocol}} for {{Disaster 5G mmWave Networks}}},
  shorttitle = {{{DL}}-{{TCP}}},
  author = {Na, Woongsoo and Bae, Byungjun and Cho, Sukhee and Kim, Nayeon},
  date = {2019},
  journaltitle = {IEEE Access},
  volume = {7},
  pages = {145134--145144},
  issn = {2169-3536},
  doi = {10.1109/ACCESS.2019.2945582},
  abstract = {The 5G mobile communication system is attracting attention as one of the most suitable communication models for broadcasting and managing disaster situations, owing to its large capacity and low latency. High-quality videos taken by a drone, which is an embedded IoT device for shooting in a disaster environment, play an important role in managing the disaster. However, the 5G mmWave frequency band is susceptible to obstacles and has beam misalignment problems, severing the connection and greatly affecting the degradation of TCP performance. This problem becomes even more serious in high-mobility drones and disaster sites with many obstacles. To solve this problem, we propose a deep-learning-based TCP (DL-TCP) for a disaster 5G mmWave network. DL-TCP learns the node's mobility information and signal strength, and adjusts the TCP congestion window by predicting when the network is disconnected and reconnected. As a result of the experiment, DL-TCP provides better network stability and higher network throughput than the existing TCP NewReno, TCP Cubic, and TCP BBR.},
  eventtitle = {{{IEEE Access}}},
  keywords = {5G,5G mobile communication,Bandwidth,Deep-learning,Machine learning,mmWave,Protocols,Signal to noise ratio,supervised-learning,TCP,Videos,Wireless communication},
  file = {/home/leopold/snap/zotero-snap/common/Zotero/storage/GCGKXV6U/Na et al. - 2019 - DL-TCP Deep Learning-Based Transmission Control P.pdf;/home/leopold/snap/zotero-snap/common/Zotero/storage/PV8BQ6LQ/8859212.html}
}

@article{oliveiraComputerNetworkTraffic2016,
  title = {Computer Network Traffic Prediction: {{A}} Comparison between Traditional and Deep Learning Neural Networks},
  shorttitle = {Computer Network Traffic Prediction},
  author = {Oliveira, Tiago and Barbar, Jamil and Soares, Alexsandro},
  date = {2016-01-01},
  journaltitle = {International Journal of Big Data Intelligence},
  volume = {3},
  pages = {28},
  doi = {10.1504/IJBDI.2016.073903},
  abstract = {This paper compares four different artificial neural network approaches for computer network traffic forecast, such as: 1) multilayer perceptron (MLP) using the backpropagation as training algorithm; 2) MLP with resilient backpropagation (Rprop); (3) recurrent neural network (RNN); 4) deep learning stacked autoencoder (SAE). The computer network traffic is sampled from the traffic of the network devices that are connected to the internet. It is shown herein how a simpler neural network model, such as the RNN and MLP, can work even better than a more complex model, such as the SAE. Internet traffic prediction is an important task for many applications, such as adaptive applications, congestion control, admission control, anomaly detection and bandwidth allocation. In addition, efficient methods of resource management, such as the bandwidth, can be used to gain performance and reduce costs, improving the quality of service (QoS). The popularity of the newest deep learning methods have been increasing in several areas, but there is a lack of studies concerning time series prediction, such as internet traffic.},
  file = {/home/leopold/snap/zotero-snap/common/Zotero/storage/PP28QWSB/Oliveira et al. - 2016 - Computer network traffic prediction A comparison .pdf}
}

@report{rheeCUBICFastLongDistance2018,
  title = {{{CUBIC}} for {{Fast Long}}-{{Distance Networks}}},
  author = {Rhee, I. and Xu, L. and Ha, S. and Zimmermann, A. and Eggert, L. and Scheffenegger, R.},
  date = {2018-02},
  number = {RFC8312},
  pages = {RFC8312},
  institution = {{RFC Editor}},
  doi = {10.17487/RFC8312},
  url = {https://www.rfc-editor.org/info/rfc8312},
  urldate = {2021-08-16},
  langid = {english}
}

@report{shelbyConstrainedApplicationProtocol2014,
  title = {The {{Constrained Application Protocol}} ({{CoAP}})},
  author = {Shelby, Z. and Hartke, K. and Bormann, C.},
  date = {2014-06},
  number = {RFC7252},
  pages = {RFC7252},
  institution = {{RFC Editor}},
  doi = {10.17487/rfc7252},
  url = {https://www.rfc-editor.org/info/rfc7252},
  urldate = {2021-06-06},
  langid = {english}
}

@report{shelbyConstrainedRESTfulEnvironments2012,
  title = {Constrained {{RESTful Environments}} ({{CoRE}}) {{Link Format}}},
  author = {Shelby, Z.},
  date = {2012-08},
  number = {RFC6690},
  pages = {RFC6690},
  institution = {{RFC Editor}},
  doi = {10.17487/rfc6690},
  url = {https://www.rfc-editor.org/info/rfc6690},
  urldate = {2021-06-06},
  langid = {english},
  file = {/home/leopold/snap/zotero-snap/common/Zotero/storage/TJZ9ZDWD/Shelby - 2012 - Constrained RESTful Environments (CoRE) Link Forma.pdf}
}

@article{spivakComparisonDigitalMaps2014,
  title = {Comparison of {{Digital Maps}}: {{Recognition}} and {{Quantitative Measure}} of {{Changes}}},
  shorttitle = {Comparison of {{Digital Maps}}},
  author = {Spivak, Lev and Spivak, Ivan and Sokolov, Alexey and Voinov, Sergey},
  date = {2014},
  journaltitle = {JGIS},
  volume = {06},
  number = {05},
  pages = {415--422},
  issn = {2151-1950, 2151-1969},
  doi = {10.4236/jgis.2014.65036},
  url = {http://www.scirp.org/journal/doi.aspx?DOI=10.4236/jgis.2014.65036},
  urldate = {2021-05-20},
  file = {/home/leopold/snap/zotero-snap/common/Zotero/storage/39HVB5X5/Spivak et al. - 2014 - Comparison of Digital Maps Recognition and Quanti.pdf}
}

@book{tesslerReinforcementLearningDatacenter2021,
  title = {Reinforcement {{Learning}} for {{Datacenter Congestion Control}}},
  author = {Tessler, Chen and Shpigelman, Yuval and Dalal, Gal and Mandelbaum, Amit and Kazakov, Doron and Fuhrer, Benjamin and Chechik, Gal and Mannor, Shie},
  date = {2021-02-18},
  abstract = {We approach the task of network congestion control in datacenters using Reinforcement Learning (RL). Successful congestion control algorithms can dramatically improve latency and overall network throughput. Until today, no such learning-based algorithms have shown practical potential in this domain. Evidently, the most popular recent deployments rely on rule-based heuristics that are tested on a predetermined set of benchmarks. Consequently, these heuristics do not generalize well to newly-seen scenarios. Contrarily, we devise an RL-based algorithm with the aim of generalizing to different configurations of real-world datacenter networks. We overcome challenges such as partial-observability, non-stationarity, and multi-objectiveness. We further propose a policy gradient algorithm that leverages the analytical structure of the reward function to approximate its derivative and improve stability. We show that this scheme outperforms alternative popular RL approaches, and generalizes to scenarios that were not seen during training. Our experiments, conducted on a realistic simulator that emulates communication networks' behavior, exhibit improved performance concurrently on the multiple considered metrics compared to the popular algorithms deployed today in real datacenters. Our algorithm is being productized to replace heuristics in some of the largest datacenters in the world.}
}

@article{xiaoTCPDrincSmartCongestion2019,
  title = {{{TCP}}-{{Drinc}}: {{Smart Congestion Control Based}} on {{Deep Reinforcement Learning}}},
  shorttitle = {{{TCP}}-{{Drinc}}},
  author = {Xiao, Kefan and Mao, Shiwen and Tugnait, Jitendra K.},
  date = {2019},
  journaltitle = {IEEE Access},
  volume = {7},
  pages = {11892--11904},
  issn = {2169-3536},
  doi = {10.1109/ACCESS.2019.2892046},
  abstract = {As wired/wireless networks become more and more complex, the fundamental assumptions made by many existing TCP variants may not hold true anymore. In this paper, we develop a model-free, smart congestion control algorithm based on deep reinforcement learning, which has a high potential in dealing with the complex and dynamic network environment. We present TCP-Deep ReInforcement learNing-based Congestion control (Drinc) which learns from past experience in the form of a set of measured features to decide how to adjust the congestion window size. We present the TCP-Drinc design and validate its performance with extensive ns-3 simulations and comparison with five benchmark schemes.},
  eventtitle = {{{IEEE Access}}},
  keywords = {Congestion control,deep convolutional neural network (DCNN),deep reinforcement learning (DRL),Delays,long short term memory (LSTM),machine learning,Microsoft Windows,Protocols,Reinforcement learning,Wireless communication,Wireless sensor networks},
  file = {/home/leopold/snap/zotero-snap/common/Zotero/storage/FTP7JBFP/Xiao et al. - 2019 - TCP-Drinc Smart Congestion Control Based on Deep .pdf;/home/leopold/snap/zotero-snap/common/Zotero/storage/SECVK22A/8610116.html}
}

@article{yangResearchLaboratoryManagement2021,
  title = {Research on the {{Laboratory Management Mode Based}} on the {{Optimal Allocation}} of {{Resources}}},
  author = {Yang, Bo},
  date = {2021},
  journaltitle = {OALib},
  volume = {08},
  number = {01},
  pages = {1--8},
  issn = {2333-9721, 2333-9705},
  doi = {10.4236/oalib.1107119},
  url = {http://www.oalib.com/paper/pdf/6325696},
  urldate = {2021-05-20}
}

@article{zhaoResourceAllocationOFDMAMIMO2013,
  title = {Resource {{Allocation}} for {{OFDMA}}-{{MIMO Relay Systems}} with {{Proportional Fairness Constraints}}},
  author = {Zhao, Cuiru and Li, Youming and Chen, Bin and Wang, Zhao and Wang, Jiongtao},
  date = {2013},
  journaltitle = {CN},
  volume = {05},
  number = {03},
  pages = {303--307},
  issn = {1949-2421, 1947-3826},
  doi = {10.4236/cn.2013.53B2056},
  url = {http://www.scirp.org/journal/doi.aspx?DOI=10.4236/cn.2013.53B2056},
  urldate = {2021-05-20},
  file = {/home/leopold/snap/zotero-snap/common/Zotero/storage/SHNR6SNE/Zhao et al. - 2013 - Resource Allocation for OFDMA-MIMO Relay Systems w.pdf}
}

@article{zhengResearchAirportTaxi2020,
  title = {Research on {{Airport Taxi Resource Allocation Based}} on {{Information Asymmetry}}},
  author = {Zheng, Yansong},
  date = {2020},
  journaltitle = {OJBM},
  volume = {08},
  number = {02},
  pages = {763--769},
  issn = {2329-3284, 2329-3292},
  doi = {10.4236/ojbm.2020.82046},
  url = {https://www.scirp.org/journal/doi.aspx?doi=10.4236/ojbm.2020.82046},
  urldate = {2021-05-20},
  file = {/home/leopold/snap/zotero-snap/common/Zotero/storage/KHZSTVS8/Zheng - 2020 - Research on Airport Taxi Resource Allocation Based.pdf}
}


