Topology-Aware Resilient Routing Protocol for FANETs: An Adaptive <i>Q</i>-Learning Approach

Cui, Yanpeng; Zhang, Qixun; Feng, Zhiyong; Wei, Zhiqing; Shi, Ce; Yang, Heng

doi:10.1109/jiot.2022.3162849

Cited by 34 publications

(10 citation statements)

References 44 publications

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“…The regular beacon transmission increases control overhead, bandwidth consumption, and energy expenditure. 24,39,40 Thus, beaconless geographic routing schemes have been proposed for UAV networks. 32,41 In beaconless routing, a current forwarding node or a custodian broadcasts a packet, and neighboring nodes present in the forwarding area, termed tentative custodians, are eligible to forward the packet based on delay timers.…”

Section: Beaconless Geographic Routingmentioning

confidence: 99%

“…In geographic routing, nodes broadcast beacons or hello messages periodically to exchange position information with neighboring nodes. The regular beacon transmission increases control overhead, bandwidth consumption, and energy expenditure 24,39,40 . Thus, beaconless geographic routing schemes have been proposed for UAV networks 32,41 .…”

Section: Related Workmentioning

confidence: 99%

“…As a result, it prefers neighboring nodes at the border of the communication range of the current forwarding node, where the probability of link failure is higher due to interference and higher mobility of UAVs 21,22 . Additionally, the limited communication range of UAVs and rapid change in network topology results in the frequent occurrence of routing holes or local minima in FANETs 23,24 . A routing hole occurs when a forwarding node has no neighboring node closer to the destination than itself.…”

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: Beaconless Geographic Routingmentioning

confidence: 99%

Section: Related Workmentioning

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 authors then proposed a routing mechanism by leveraging the link duration. In [18], the authors proposed a routing strategy based on adaptive Q-learning. Adaptive Q-learning allows UAVs to take distributed, autonomous and adaptive routing decisions.…”

Section: Related Workmentioning

confidence: 99%

“…In [16], Kong et al revealed that the traditional queue-length based MaxWeight task scheduling has failed to deliver optimal throughput. Subsequently, many novel scheduling techniques have been designed recently in the context of routing protocols by leveraging link duration [17], adaptive Q-Learning [18] or independent of routing by leveraging ant colony optimization [19], meta learning [20]. However, none of these works have considered the dynamic traffic flows while designing their scheduling techniques.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Super Round-Based Distributed Task Scheduling for UAV Networks

Halder

Ghosal

Conti

2023

IEEE Trans. Wireless Commun.

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Networks of Unmanned Aerial Vehicles (UAVs) are emerging in many application domains, e.g., military surveillance. To perform collaborative tasks, the involved UAVs exchange several types of information, e.g., sensor data and commands. The major question here is how to schedule the tasks under dynamic traffic flows to provide network services. Existing solutions use the Round-Robin Strategy (RRS), where the tasks are scheduled statistically by dividing the time into fixed-length rounds. However, the RRS wastes significant network and device resources due to task scheduling in each round.This paper proposes DROVE -a novel clustering approach that allows the UAVs for dynamic task scheduling. However, determining the task scheduling is crucial, as it significantly affects several network parameters, e.g., throughput. Therefore, we devise the problem of distributed task scheduling under dynamic traffic flow scenarios to optimize the throughput. We propose a clustering task scheduling algorithm to serve dynamic traffic flows. Particularly, we integrate the dynamic traffic flows into the Lyapunov drift analysis framework, and determine the throughput optimality of our proposed scheduling algorithm. We perform extensive simulations to validate the effectiveness of DROVE. The results show that DROVE outperforms the stateof-the-art solutions in terms of energy consumption, clustering overhead, throughput, end-to-end delay, flow success rate and packet drop rate.

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Challenges, Communications, Routing Protocols and Applications of FANETs-A Systematic Review

Sharma,

Dubey,

Batham

et al. 2024

Lecture Notes in Networks and Systems

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Topology-Aware Resilient Routing Protocol for FANETs: An Adaptive Q-Learning Approach

Cited by 34 publications

References 44 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

Dynamic Super Round-Based Distributed Task Scheduling for UAV Networks

Challenges, Communications, Routing Protocols and Applications of FANETs-A Systematic Review

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