A novel airborne greedy geographic routing protocol for flying Ad hoc networks

Zheng, Bo; Zhuo, Kun; Zhang, Hengyang; Wu, Huaxin

doi:10.1007/s11276-022-03030-9

Cited by 14 publications

(13 citation statements)

References 21 publications

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“…• Airborne greedy geographic routing (AGGR) 29 The following subsections detail the simulation environment, followed by the performance metrics, and finally, the analysis of simulation results.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…The neighboring node expected to reach the communication range of the destination in the least time is selected as the next hop. The works presented in Wang et al 28 and Zheng et al 29 exploit the relative motion between aircraft to choose the stable next hop. Moreover, these works have used automatic dependent surveillance‐broadcast and adaptive beaconing for neighborhood discovery instead of the traditional beaconing approach.…”

Section: Related Workmentioning

confidence: 99%

“…The proposed routing protocol MF‐DLB is compared with the following existing geographic routing schemes. Greedy perimeter stateless routing (GPSR) 35 : It is a popular and simplest geographic routing scheme that employs greedy forwarding to route packets from source to destination. Geographic position mobility oriented routing (GPMOR) 26 : This routing scheme considers the mobility features of neighboring UAVs in addition to their geographic position to reduce the impact of intermittent connectivity. Airborne greedy geographic routing (AGGR) 29 : It uses an adaptive hello mechanism and greedy forwarding based on the relative motion of neighboring UAVs. …”

Section: Performance Evaluationmentioning

confidence: 99%

“…So far, various enhancements have been introduced in greedy forwarding by incorporating additional metrics. The mobility characteristics of UAVs are exploited in addition to geographic location to reduce the impact of frequent link failure due to dynamic topology 26–31 . Also, for dealing with heavy loads on the shortest routing path and minimizing buffer overflows, the forwarding UAV considers the queue status of neighboring UAVs while selecting the next hop 23,32–34 .…”

Section: Introductionmentioning

confidence: 99%

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MF‐DLB: Multimetric forwarding and directed acyclic graph‐based load balancing for geographic routing in FANETs

Singh,

Sharma,

Verma

2024

Concurrency and Computation

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SummaryFlying ad hoc network (FANET) comprising unmanned aerial vehicles (UAVs) emerges as a promising solution for numerous military and civil applications. Transferring data collected from the environment to the ground station (GS) is a primary concern for meeting the communication demands of most of these applications. However, the highly mobile UAVs with limited communication range, resulting in frequent topology change and intermittent connectivity, make data routing challenging. In such scenarios, geographic routing is a viable solution due to its scalability and robustness. However, the basic forwarding mechanism of geographic routing favors the neighboring UAV nearest to the destination, impacted substantially by link failures and routing holes in a dynamic environment. Additionally, routing decisions ignoring the current load over UAVs contribute to performance degradation due to the high concentration of data traffic near the GS. Thus, to address these issues, a geographic routing protocol named MF‐DLB comprising multimetric forwarding (MF) and a directed acyclic graph‐based load balancing (DLB) scheme is proposed to enhance packet forwarding in FANETs. MF takes account of multiple metrics related to connectivity, geographic progress, link lifetime, and residual energy to select the next hop with a stable communication link while effectively bypassing the routing holes. The second scheme, DLB, focuses on proactively maintaining routing paths near GS for load distribution among underutilized nodes to address the congestion problem. Simulations performed in network simulator ns‐3 confirm the outperformance of MF‐DLB over other related routing schemes in terms of different performance metrics.

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Section: Performance Evaluationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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MF‐DLB: Multimetric forwarding and directed acyclic graph‐based load balancing for geographic routing in FANETs

Singh,

Sharma,

Verma

2024

Concurrency and Computation

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“…The novel routing protocol in [ 32 ] used the location estimation of the destination to deliver packets in an appropriate direction while considering congestion information to forward a packet for delay-tolerant and energy-limited UAV networks. The authors of [ 33 ] presented an adaptive “hello” mechanism and a greedy forwarding mechanism according to the relative motion of the nodes. Overall, the conventional routing protocols have been relatively simple and easy to implement, but they lack the ability to self-adapt or to employ intelligent and autonomous decision-making.…”

Section: Related Workmentioning

confidence: 99%

Delay-Aware and Link-Quality-Aware Geographical Routing Protocol for UANET via Dueling Deep Q-Network

Zhang

Qiu

2023

Sensors

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In an unmanned aerial vehicles ad hoc network (UANET), UAVs communicate with each other to accomplish intricate tasks collaboratively and cooperatively. However, the high mobility of UAVs, the variable link quality, and heavy traffic loads can lead to difficulties in finding an optimal communication path. We proposed a delay-aware and link-quality-aware geographical routing protocol for a UANET via the dueling deep Q-network (DLGR-2DQ) to address these problems. Firstly, the link quality was not only related to the physical layer metric, the signal-to-noise ratio, which was influenced by path loss and Doppler shifts, but also the expected transmission count of the data link layer. In addition, we also considered the total waiting time of packets in the candidate forwarding node in order to decrease the end-to-end delay. Then, we modeled the packet-forwarding process as a Markov decision process. We crafted an appropriate reward function that utilized the penalty value for each additional hop, total waiting time, and link quality to accelerate the learning of the dueling DQN algorithm. Finally, the simulation results illustrated that our proposed routing protocol outperformed others in terms of the packet delivery ratio and the average end-to-end delay.

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PCSSHO: An implementation of Percentage of Circle Search and Spotted Hyena Optimizer for multi‐constraint load balancing and routing framework in FANET

Ilango,

Sivarajan,

RaviChandran

et al. 2024

Int J Communication

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SummaryFlying ad hoc network (FANET) is a technology that has seen tremendous growth in the past years due to its application in various military and civil developments. Conventional topology‐aided routing schemes are not suitable for large‐scale FANETs. This is because of the higher mobility rate in UAVs with the architecture differences. Hence, several path entries in routing become invalid and the neighboring nodes can be occupied before the interruption. Hence, it is very important to solve the afore‐explained issues in FANET. Initially, the task is assigned to the UAVs. These tasks are performed by satisfying the demands such as timing, congestion, and energy to attain the effective load‐balancing performance. Then, the optimal clustering and cluster head (CH) selection are performed using the developed Percentage of Circle Search and Spotted Hyena Optimizer (PCSSHO) in the communication path. Here, the feature selection is done based on constraints like position, speed, moving direction, height variation, link quality, and inter‐ and intra‐cluster distance. Then, the developed PCSSHO model performs routing by considering different constraints such as “end‐to‐end delay, delivery ratio, power consumption, and link quality.” Thus, the recommenced load balancing model in FANET secures an enhanced performance rate than the conventional load balancing frameworks.

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A novel airborne greedy geographic routing protocol for flying Ad hoc networks

Cited by 14 publications

References 21 publications

MF‐DLB: Multimetric forwarding and directed acyclic graph‐based load balancing for geographic routing in FANETs

MF‐DLB: Multimetric forwarding and directed acyclic graph‐based load balancing for geographic routing in FANETs

Delay-Aware and Link-Quality-Aware Geographical Routing Protocol for UANET via Dueling Deep Q-Network

PCSSHO: An implementation of Percentage of Circle Search and Spotted Hyena Optimizer for multi‐constraint load balancing and routing framework in FANET

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