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Distributed Beamforming with Unmanned Vehicles and Edge Network Resource Orchestration for Wireless IoT : A Systems Perspective.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Distributed Beamforming with Unmanned Vehicles and Edge Network Resource Orchestration for Wireless IoT : A Systems Perspective./
作者:	Mohanti, Subhramoy.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
面頁冊數:	133 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-12, Section: B.
Contained By:	Dissertations Abstracts International83-12B.
標題:	Computer engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29068491
ISBN:	9798438759812

Distributed Beamforming with Unmanned Vehicles and Edge Network Resource Orchestration for Wireless IoT : A Systems Perspective.
Mohanti, Subhramoy.

Distributed Beamforming with Unmanned Vehicles and Edge Network Resource Orchestration for Wireless IoT : A Systems Perspective. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 133 p.

Source: Dissertations Abstracts International, Volume: 83-12, Section: B.

Thesis (Ph.D.)--Northeastern University, 2022.

This item must not be sold to any third party vendors.

The pervasive deployment of the wireless Internet of Things (IoT) has given rise to heterogeneous sensors, small-form-factor computing devices, and robots in homes, offices, public spaces, and manufacturing floors, among others. Such a substantial number of connected devices require (i) simple ways of charging so that they remain operationally available, and (ii) effective ways of sharing wireless spectrum so that they continue to transmit and receive data amidst competing and interfering signals. This thesis focuses on the link and physical layer of the protocol stack for distributed beamforming and dynamic network resource orchestration to act as key enablers for these two objectives. Specifically, we experimentally demonstrate how beamforming capability can address both wireless power transfer (WPT) needs and resilient communication in interference-challenged environments and how dynamic resource allocation algorithms coupled with reinforcement learning can enable efficient network resource utilization in high user density scenarios.This thesis proposes a method for accessing and sharing the wireless channel for both regular data communication and WPT. This is the first work that accomplishes these dissimilar tasks within the constraints of the standard-compliant IEEE 802.11 protocol, resulting in a practical and so-called 'Wi-Fi-friendly Energy Delivery' (WiFED). First, WiFED exploits the IEEE 802.11 supported protocol features to request energy and for energy transmitters to participate in energy transfer via beamforming. Second, it devises a controller-driven bipartite matching algorithm, assigning an appropriate number of energy transmitters to sensors for efficient energy delivery. Thirdly, it detects outlier sensors, which have limited power reception from static energy transmitters, and utilizes mobile energy transmitters to satisfy their charging cycles.From a communication-only perspective that relies on distributed beamforming, this thesis presents SABRE, a software-based approach that runs on Unmanned Aerial Vehicles (UAVs) or Mobile Autonomous Systems (MASs) to deliver on-demand data to sensors deployed in infrastructure constrained environments. We first show why this problem is difficult given the continuous hovering-related channel fluctuations, synchronizing the distributed transmit streams without a wired clock reference, the need to ensure timely feedback from the ground receiver due to the channel coherence time, and the size, weight, power, and cost (SWaP-C) constraints for UAVs. This work is extended further to consider realistic traffic patterns and packet arrival thresholds, involving dynamic grouping of transmitters to beamform towards target receivers at any given time. Again, we evaluate outcomes both experimentally and in a virtual environment in Colosseum, the world's largest RF emulator.When considering smart factory floors these MASs will revolutionize the future of manufacturing and the service industry by automating tasks. However, to fully supplement human effort, the MASs will need low-latency, reliable connectivity throughout the work zone through edge-controlled wireless links. For this, we present L-NORM, which will allow the MASs to assuredly respond to programming directives as well as self-learning capability that involves real-time relaying of locally generated sensor data to the Mobile Edge Computing (MEC) server. In this case, we first demonstrate a holistic system for multi-MAS motion through reinforcement learning on the edge, with multi-modal MAS sensor data. Next, we discuss a novel edge network resource orchestration method to enable the multi-MAS coordination from the edge, through a combination of heuristic methods and reinforcement learning. Through extensive simulation studies, we highlight the advantages of edge-controlled MAS coordination and network orchestration for seamless coexistence of MAS and legacy devices in typical resource-constrained environments supporting heterogeneous applications.

ISBN: 9798438759812Subjects--Topical Terms:

621879
Computer engineering.
Subjects--Index Terms:

Beamforming

Distributed Beamforming with Unmanned Vehicles and Edge Network Resource Orchestration for Wireless IoT : A Systems Perspective.
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245 1 0 $a Distributed Beamforming with Unmanned Vehicles and Edge Network Resource Orchestration for Wireless IoT : A Systems Perspective.
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300 $a 133 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-12, Section: B.
500 $a Advisor: Chowdhury, Kaushik.
502 $a Thesis (Ph.D.)--Northeastern University, 2022.
506 $a This item must not be sold to any third party vendors.
520 $a The pervasive deployment of the wireless Internet of Things (IoT) has given rise to heterogeneous sensors, small-form-factor computing devices, and robots in homes, offices, public spaces, and manufacturing floors, among others. Such a substantial number of connected devices require (i) simple ways of charging so that they remain operationally available, and (ii) effective ways of sharing wireless spectrum so that they continue to transmit and receive data amidst competing and interfering signals. This thesis focuses on the link and physical layer of the protocol stack for distributed beamforming and dynamic network resource orchestration to act as key enablers for these two objectives. Specifically, we experimentally demonstrate how beamforming capability can address both wireless power transfer (WPT) needs and resilient communication in interference-challenged environments and how dynamic resource allocation algorithms coupled with reinforcement learning can enable efficient network resource utilization in high user density scenarios.This thesis proposes a method for accessing and sharing the wireless channel for both regular data communication and WPT. This is the first work that accomplishes these dissimilar tasks within the constraints of the standard-compliant IEEE 802.11 protocol, resulting in a practical and so-called 'Wi-Fi-friendly Energy Delivery' (WiFED). First, WiFED exploits the IEEE 802.11 supported protocol features to request energy and for energy transmitters to participate in energy transfer via beamforming. Second, it devises a controller-driven bipartite matching algorithm, assigning an appropriate number of energy transmitters to sensors for efficient energy delivery. Thirdly, it detects outlier sensors, which have limited power reception from static energy transmitters, and utilizes mobile energy transmitters to satisfy their charging cycles.From a communication-only perspective that relies on distributed beamforming, this thesis presents SABRE, a software-based approach that runs on Unmanned Aerial Vehicles (UAVs) or Mobile Autonomous Systems (MASs) to deliver on-demand data to sensors deployed in infrastructure constrained environments. We first show why this problem is difficult given the continuous hovering-related channel fluctuations, synchronizing the distributed transmit streams without a wired clock reference, the need to ensure timely feedback from the ground receiver due to the channel coherence time, and the size, weight, power, and cost (SWaP-C) constraints for UAVs. This work is extended further to consider realistic traffic patterns and packet arrival thresholds, involving dynamic grouping of transmitters to beamform towards target receivers at any given time. Again, we evaluate outcomes both experimentally and in a virtual environment in Colosseum, the world's largest RF emulator.When considering smart factory floors these MASs will revolutionize the future of manufacturing and the service industry by automating tasks. However, to fully supplement human effort, the MASs will need low-latency, reliable connectivity throughout the work zone through edge-controlled wireless links. For this, we present L-NORM, which will allow the MASs to assuredly respond to programming directives as well as self-learning capability that involves real-time relaying of locally generated sensor data to the Mobile Edge Computing (MEC) server. In this case, we first demonstrate a holistic system for multi-MAS motion through reinforcement learning on the edge, with multi-modal MAS sensor data. Next, we discuss a novel edge network resource orchestration method to enable the multi-MAS coordination from the edge, through a combination of heuristic methods and reinforcement learning. Through extensive simulation studies, we highlight the advantages of edge-controlled MAS coordination and network orchestration for seamless coexistence of MAS and legacy devices in typical resource-constrained environments supporting heterogeneous applications.
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650 4 $a Computer science. $3 523869
650 4 $a Automotive engineering. $3 2181195
650 4 $a Electrical engineering. $3 649834
653 $a Beamforming
653 $a Unmanned vehicles
653 $a Edge network
653 $a Resource orchestration
653 $a Internet of things
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