Routing Protocols for Unmanned Aerial Vehicle Networks: A Survey

Arafat, Muhammad Yeasir; Moh, Sangman

doi:10.1109/access.2019.2930813

Cited by 157 publications

(81 citation statements)

References 127 publications

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“…The Ad-hoc On-Demand Distance Vector (AODV) protocol is also widely used in UAV networks. However, due to the dynamic link connections, it suffers from the network overhead and latency issues [14]. In [15], authors evaluated the performance of the Optimized Link State Routing (OLSR) protocol in UAV network comprising of ground stations and two UAVs, and conclude that the OLSR is unreliable in UAV networks due to the rapid topology changes.…”

Section: Literature Reviewmentioning

confidence: 99%

Mobility and Location-Aware Stable Clustering Scheme for UAV Networks

2020

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In Unmanned Aerial Vehicle (UAV) networks, mobility of the UAV and the corresponding network dynamics cause frequent network adaptation. One key challenge caused by this in Flying Ad-hoc Network (FANET) is how to maintain the link stability such that both the packet loss rate and network latency can be reduced. Clustering of UAVs could effectively improve the performance of large-scale UAV swarm. However, the use of conventional clustering schemes in dynamic and high mobility FANET will lead to more link outages. Besides, frequent updates of cluster structure would cause the instability of network topology and the increase of control overhead and latency. To solve this problem, we propose a locationbased k-means UAV clustering algorithms by incorporating the mobility and relative location of the UAVs to enhance the performance and reliability of the UAV network with limited resource. The objective of the proposed Mobility and Location-aware Stable Clustering (MLSC) mechanism is to enhance the stability and accuracy of the network by reducing unnecessary overheads and network latency through incorporating several design factors with minimum resource constraints. Furthermore, we derive the relationship between the maximum coverage probability of Cluster Head (CH) and cluster size to find the optimal cluster size to minimize the network overhead. Our simulation results show that the proposed MLSC scheme significantly reduces the network overheads, and also improves packet delivery ratio and network latency as compared to the conventional clustering methods. INDEX TERMS Unmanned aerial vehicles (UAVs), coverage probability, stable clustering, k-means clustering.

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Section: Literature Reviewmentioning

confidence: 99%

Mobility and Location-Aware Stable Clustering Scheme for UAV Networks

2020

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“…Additional real world problems for routing can be found in the expanding domain of network routing. Such research areas include Device to Device (D2D) network routing [33], unmanned aerial vehicle networks [2] and disaster relief networking [19]. Each of these research areas have differing constraints, with for example, disaster relief having requirements for redundancy and flexibility that other network optimizations may consider less important.…”

Section: Related Workmentioning

confidence: 99%

A hybrid metaheuristic approach to a real world employee scheduling problem

Reid

Brownlee

et al. 2019

Proceedings of the Genetic and Evolutionary Computation Conference

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Efficient optimization of resources is paramount to success in many problems faced today. In the field of operational research the efficient scheduling of employees; packing of vans; routing of vehicles; logistics of airlines and transport of materials can be the difference between emission reduction or excess, profits or losses and feasibility or unworkable solutions. The video game Factorio, by Wube Software, has a myriad of problems which are analogous to such real-world problems, and is a useful simulator for developing solutions for these problems. In this paper we define the logistic transport belt problem and define mathematical integer programming model of it. We developed an interface to allow optimizers in any programming language to interact with Factorio, and we provide an initial benchmark of logistic transport belt problems. We present results for Simulated Annealing, quick Genetic Programming and Evolutionary Reinforcement Learning, three different meta-heuristic techniques to optimize this novel problem.

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“…We assume that UAVs are evenly distributed in a L max × L max square plane space, and the average coverage area of a UAV is π( D 2 ) 2 , where D is the cover diameter.…”

Section: The Approximate Energy Consumption Model Of Hop-by-hop Romentioning

confidence: 99%

“…Some solutions use store-carryforward (SCF) routing to facilitate end to end data transmission. In SCF routing, the message is stored in a node (or some nodes) and moved from source to the destination one hop at a time [2].…”

Section: Introductionmentioning

confidence: 99%

Is Hop-by-Hop Always Better Than Store-Carry-Forward for UAV Network?

Xiong

Lei

Xie³

et al. 2019

IEEE Access

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As energy supply on Unmanned Aerial Vehicle (UAV) is limited, energy efficient data transmission in UAV networks would be of great concern to the researchers. Nevertheless, UAV networks exhibit strong dynamic nature compared to ordinary Mobile Ad hoc Network (MANET), it is not rare that some nodes will be disjoint from other nodes from time to time. Under such circumstance, ordinary hop-byhop routing schemes can not be used anymore, for there is no route existed from source to destination. Some solutions use store-carry-forward (SCF) routing to facilitate end to end data transmission. However, when should we use SCF routing, when we should use hop-by-hop routing if we taking energy into consideration? Is hop-by-hop routing always better than SCF? If it is good or not when the hop-by-hop routing is combined with the SCF routing? All these questions need to be answered. In this paper, we answered above questions by proposing three types of minimum energy consumption models of hop-by-hop routing, and two SCF routing models. We compared all these models and considered four tactics of combination modes for the transmissions in a rotary-wing UAV network. The researches show that two tactics have better performance than other modes in energy saving capabilities by reducing 70% at most with the hop-by-hop routing, and the transmission time of combination modes are in same scale compared with hop-by-hop routing. The two tactics are appropriate for different UAV networks respectively, in which the source UAV can or cannot get the GPS information of relay UAVs in the route. The research conclusion can also be used for the transmissions in a fixed-wing UAV network by modifying the energy consumption models of fixed wing UAV, which is different from rotary wing UAVs. INDEX TERMS Hop-by-hop routing, store-carry-forward, UAV network, energy consumption.

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Routing Protocols for Unmanned Aerial Vehicle Networks: A Survey

Cited by 157 publications

References 127 publications

Mobility and Location-Aware Stable Clustering Scheme for UAV Networks

Mobility and Location-Aware Stable Clustering Scheme for UAV Networks

A hybrid metaheuristic approach to a real world employee scheduling problem

Is Hop-by-Hop Always Better Than Store-Carry-Forward for UAV Network?

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