Maximizing Throughput of UAV-Relaying Networks with the Load-Carry-and-Deliver Paradigm

Cheng, Chen-Mou; Hsiao, Pai-Hsiang; Kung, H. T.; Vlah, Dario

doi:10.1109/wcnc.2007.805

Cited by 128 publications

(87 citation statements)

References 9 publications

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“…• One such protocol is Load Carry and Deliver Routing (LCAD) [34] in which the ground node passes the data to a UAV, which carries it to the destination. It aims to maximize the throughput while increasing security.…”

Section: ) Static Routing Protocolsmentioning

confidence: 99%

“…Because of this constraint, these systems are not fault tolerant or suitable for dynamically changing environment. Their applicability to UAV networks is limited and is only presented here for academic interest.• One such protocol is Load Carry and Deliver Routing (LCAD) [34] in which the ground node passes the data to a UAV, which carries it to the destination. It aims to maximize the throughput while increasing security.…”

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Survey of Important Issues in UAV Communication Networks

Gupta

Jain

Vaszkun

2016

IEEE Commun. Surv. Tutorials

1,860

859

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Abstract-Unmanned Aerial Vehicles (UAVs) have enormous potential in the public and civil domains. These are particularly useful in applications where human lives would otherwise be endangered. Multi-UAV systems can collaboratively complete missions more efficiently and economically as compared to single UAV systems. However, there are many issues to be resolved before effective use of UAVs can be made to provide stable and reliable context-specific networks. Much of the work carried out in the areas of Mobile Ad Hoc Networks (MANETs), and Vehicular Ad Hoc Networks (VANETs) does not address the unique characteristics of the UAV networks. UAV networks may vary from slow dynamic to dynamic; have intermittent links and fluid topology. While it is believed that ad hoc mesh network would be most suitable for UAV networks yet the architecture of multi-UAV networks has been an understudied area. Software Defined Networking (SDN) could facilitate flexible deployment and management of new services and help reduce cost, increase security and availability in networks. Routing demands of UAV networks go beyond the needs of MANETS and VANETS. Protocols are required that would adapt to high mobility, dynamic topology, intermittent links, power constraints and changing link quality. UAVs may fail and the network may get partitioned making delay and disruption tolerance an important design consideration. Limited life of the node and dynamicity of the network leads to the requirement of seamless handovers where researchers are looking at the work done in the areas of MANETs and VANETs, but the jury is still out. As energy supply on UAVs is limited, protocols in various layers should contribute towards greening of the network. This article surveys the work done towards all of these outstanding issues, relating to this new class of networks, so as to spur further research in these areas.Index Terms-Unmanned Aerial Vehicle, UAV, Multi-UAV Networks, ad hoc networks, communication networks, wireless mesh networks, software defined network, routing, seamless handover, energy efficiency I. INTRODUCTION A. The growing importance of UAV networksUnmanned Aerial Vehicles (UAVs) are an emerging The latter ones are proving to be quite useful in civilian applications. As described by Daniel and Wietfeld in [1] they are likely to become invaluable inclusions in the operations of police departments, fire brigades and other homeland security organizations in the near future. Besides, advances in electronics and sensor technology have widened the scope of UAV network applications [2] to include applications as diverse as traffic monitoring, wind estimation and remote sensing [3].In this context it would be relevant to mention that the current FAA guidelines allow a government public safety agency to operate unmanned aircraft weighing 4.4 pounds or less, within the line of sight of the operator; less than 400 feet above the ground; during daylight conditions; within Class G airspace; and outside of 5 statute miles from any airport, heliport, seapl...

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Section: ) Static Routing Protocolsmentioning

confidence: 99%

mentioning

confidence: 99%

Survey of Important Issues in UAV Communication Networks

Gupta

Jain

Vaszkun

2016

IEEE Commun. Surv. Tutorials

1,860

859

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“…Due to the channel's high loss rate, we boost the default retransmissions for all packets to 12 retries, over the Linux default 8. Figure 6 plots throughput for one lap of the UAV, comparing our adaptive link layer to a raw TCP connection that relies solely on retransmissions, and to two delay-tolerant protocols, DTN-1 and DTN-10 [25]. The DTN protocols queue packets when the UAV is out of range, and rely on retransmissions when the UAV is in range of the ground receiver.…”

Section: B Emulated Tcp Resultsmentioning

confidence: 99%

Hierarchical Sparse Coding for Wireless Link Prediction in an Airborne Scenario

Tarsa

Kung

2013

MILCOM 2013 - 2013 IEEE Military Communications Conference

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Abstract-We build a data-driven hierarchical inference model to predict wireless link quality between a mobile unmanned aerial vehicle (UAV) and ground nodes. Clustering, sparse feature extraction, and non-linear pooling are combined to improve Support Vector Machine (SVM) classification when a limited training set does not comprehensively characterize data variations. Our approach first learns two layers of dictionaries by clustering packet reception data. These dictionaries are used to perform sparse feature extraction, which expresses link state vectors first in terms of a few prominent local patterns, or features, and then in terms of co-occurring features along the flight path. In order to tolerate artifacts like small positional shifts in field-collected data, we pool large magnitude features among overlapping shifted patches within windows. Together, these techniques transform raw link measurements into stable feature vectors that capture environmental effects driven by radio range limitations, antenna pattern variations, line-of-sight occlusions, etc. Link outage prediction is implemented by an SVM that assigns a common label to feature vectors immediately preceding gaps of successive packet losses; predictions are then fed to an adaptive link layer protocol that adjusts forward error correction rates, or queues packets during outages to prevent TCP timeout. In our harsh target environment, links are unstable and temporary outages common, so baseline TCP connections achieve only minimal throughput. However, connections under our predictive protocol temporarily hold packets that would otherwise be lost on unavailable links, and react quickly when the UAV link is restored, increasing overall channel utilization.

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“…Building off previous work on UAV based communication networks [18], we plan to examine system issues that arise in networks comprised of both airborne and ground-based clusters. Through practical field experiments, we hope to highlight and address system implementation issues that must be considered if network coding is to be deployed widely in real-world applications.…”

Section: Resultsmentioning

confidence: 99%