Internet of Drones: Improving Multipath TCP over WiFi with Federated Multi-Armed Bandits for Limitless Connectivity

We consider multipath TCP (MPTCP) flows over the data networking dynamics of IEEE 802.11<i>ay</i> for drone surveillance of areas using high-definition video streaming. Mobility-induced handoffs are critical in IEEE 802.11<i>ay</i> (because of the smaller coverage of mmWaves)...

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Main Authors: Shiva Raj Pokhrel, Michel Mandjes
Format: Article
Language:English
Published: MDPI AG 2022-12-01
Series:Drones
Subjects:
Online Access:https://www.mdpi.com/2504-446X/7/1/30
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author Shiva Raj Pokhrel
Michel Mandjes
author_facet Shiva Raj Pokhrel
Michel Mandjes
author_sort Shiva Raj Pokhrel
collection DOAJ
description We consider multipath TCP (MPTCP) flows over the data networking dynamics of IEEE 802.11<i>ay</i> for drone surveillance of areas using high-definition video streaming. Mobility-induced handoffs are critical in IEEE 802.11<i>ay</i> (because of the smaller coverage of mmWaves), which adversely affects the performance of such data streaming flows. As a result of the enhanced 802.11<i>ay</i> network events and features (triggered by beamforming, channel bonding, MIMO, mobility-induced handoffs, channel sharing, retransmissions, etc.), the time taken for packets to travel end-to-end in 802.11<i>ay</i> are inherently time-varying. Several fundamental assumptions inherent in stochastic TCP models, including Poisson arrivals of packets, Gaussian process, and parameter certainty, are challenged by the improved data traffic dynamics over IEEE 802.11<i>ay</i> networks. The MPTCP model’s state estimation differs largely from the actual network values. We develop a new <i>data-driven stochastic framework</i> to address current deficiencies of MPTCP models and design a foundational architecture for intelligent multipath scheduling (at the <i>transport layer</i>) considering lower layer (hybrid) beamforming. At the heart of our cross-layer architecture is an intelligent learning agent for actuating and interfacing, which learns from experience optimal packet cloning, scheduling, aggregation, and beamforming using successful features of multi-armed bandits and federated learning. We demonstrate that the proposed framework can estimate and optimize jointly (<b>explore–exploit</b>) and is more practicable for designing the next generation of low-delay and robust MPTCP models.
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spelling doaj.art-f861887a4b8b4c7c8725c28b5ae743a42023-11-30T21:55:18ZengMDPI AGDrones2504-446X2022-12-01713010.3390/drones7010030Internet of Drones: Improving Multipath TCP over WiFi with Federated Multi-Armed Bandits for Limitless ConnectivityShiva Raj Pokhrel0Michel Mandjes1School of Information Technology, Deakin University, Geelong, VIC 3220, AustraliaFaculty of Science, Mathematics and Informatics Korteweg-de Vries Institute, University of Amsterdam, 1090 GE Amsterdam, The NetherlandsWe consider multipath TCP (MPTCP) flows over the data networking dynamics of IEEE 802.11<i>ay</i> for drone surveillance of areas using high-definition video streaming. Mobility-induced handoffs are critical in IEEE 802.11<i>ay</i> (because of the smaller coverage of mmWaves), which adversely affects the performance of such data streaming flows. As a result of the enhanced 802.11<i>ay</i> network events and features (triggered by beamforming, channel bonding, MIMO, mobility-induced handoffs, channel sharing, retransmissions, etc.), the time taken for packets to travel end-to-end in 802.11<i>ay</i> are inherently time-varying. Several fundamental assumptions inherent in stochastic TCP models, including Poisson arrivals of packets, Gaussian process, and parameter certainty, are challenged by the improved data traffic dynamics over IEEE 802.11<i>ay</i> networks. The MPTCP model’s state estimation differs largely from the actual network values. We develop a new <i>data-driven stochastic framework</i> to address current deficiencies of MPTCP models and design a foundational architecture for intelligent multipath scheduling (at the <i>transport layer</i>) considering lower layer (hybrid) beamforming. At the heart of our cross-layer architecture is an intelligent learning agent for actuating and interfacing, which learns from experience optimal packet cloning, scheduling, aggregation, and beamforming using successful features of multi-armed bandits and federated learning. We demonstrate that the proposed framework can estimate and optimize jointly (<b>explore–exploit</b>) and is more practicable for designing the next generation of low-delay and robust MPTCP models.https://www.mdpi.com/2504-446X/7/1/30internet of drones (IoD)multi-armed bandits (MAB)multipath TCP (MPTCP)schedulingbeamformingcross-layer approach
spellingShingle Shiva Raj Pokhrel
Michel Mandjes
Internet of Drones: Improving Multipath TCP over WiFi with Federated Multi-Armed Bandits for Limitless Connectivity
Drones
internet of drones (IoD)
multi-armed bandits (MAB)
multipath TCP (MPTCP)
scheduling
beamforming
cross-layer approach
title Internet of Drones: Improving Multipath TCP over WiFi with Federated Multi-Armed Bandits for Limitless Connectivity
title_full Internet of Drones: Improving Multipath TCP over WiFi with Federated Multi-Armed Bandits for Limitless Connectivity
title_fullStr Internet of Drones: Improving Multipath TCP over WiFi with Federated Multi-Armed Bandits for Limitless Connectivity
title_full_unstemmed Internet of Drones: Improving Multipath TCP over WiFi with Federated Multi-Armed Bandits for Limitless Connectivity
title_short Internet of Drones: Improving Multipath TCP over WiFi with Federated Multi-Armed Bandits for Limitless Connectivity
title_sort internet of drones improving multipath tcp over wifi with federated multi armed bandits for limitless connectivity
topic internet of drones (IoD)
multi-armed bandits (MAB)
multipath TCP (MPTCP)
scheduling
beamforming
cross-layer approach
url https://www.mdpi.com/2504-446X/7/1/30
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