Improvement of GPSR Routing Protocol for TDMA-Based UAV Ad-Hoc Networks

Gao, Yanzhong; Fu, Jielin; Lü, Yi

doi:10.1109/wccct52091.2021.00018

Cited by 10 publications

(4 citation statements)

References 9 publications

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“…Further, due to the suitability of the traditional GPSR, the authors proposed TD‐GPSR 35 approach for UAVs network, which considered the time slot distance. The proposed approach selects the next‐hop UAV based on the space–time distance of the neighbor UAV.…”

Section: Related Workmentioning

confidence: 99%

“…Further, the empirical outcomes verified that the PSO-GLFR outperforms the existing approach with minimum latency and energy routing support for FANETs. Further, due to the suitability of the traditional GPSR, the authors proposed TD-GPSR 35 approach for UAVs network, which considered the time slot distance. The proposed approach selects the next-hop UAV based on the space-time distance of the neighbor UAV.…”

Section: Related Workmentioning

confidence: 99%

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UF‐GPSR: Modified geographical routing protocol for flying ad‐hoc networks

Kumar

Raw

Bansal

et al. 2023

Trans Emerging Tel Tech

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Flying Ad‐hoc Networks (FANETs) has recently gained significant exposure for their emerging military, civil, and commercial applications. In FANETs, all Unmanned Aerial Vehicles (UAVs) can communicate within a restricted wireless communication range without fixed infrastructure. However, the highly dynamic nature of UAVs imposes a significant issue in FANET's communications. Therefore, designing an effective routing strategy is always necessary for sustaining appropriate network performance in FANETs. This paper is interested in modifying Greedy Perimeter Stateless Routing (GPSR) protocol named Utility Function based Greedy Perimeter Stateless Routing (UF‐GPSR) protocol for FANETs. The proposed approach optimizes the greedy forwarding strategy by considering multiple vital parameters of UAVs: residual energy ratio, distance degree, movement direction, link risk degree, and speed, respectively. The proposed UF‐GPSR applies the utility function on these parameters to enhance the routing performance by selecting optimal next‐hop within the communication range. The effectiveness of the proposed work is mathematically evaluated and then simulated through the NS‐3.23 simulator. The experimental outcomes verify that the proposed routing approach enhances the packet delivery ratio and throughput while decreasing packet drop ratio and delay compared to the other routing approaches: GPSR and GPSR‐PPU.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

UF‐GPSR: Modified geographical routing protocol for flying ad‐hoc networks

Kumar

Raw

Bansal

et al. 2023

Trans Emerging Tel Tech

View full text Add to dashboard Cite

show abstract

“…In other words, the trajectory of UAVs are often pre-planned in advance through mission planning and trajectory planning. Nevertheless, the vast majority of prior works do not fully utilize trajectory knowledge to assist routing decisions and simply assume that UAV follows the random mobility model [15]- [18], [20], [21]. Therefore, it is not the best strategy to simply use the routing protocol of traditional ad hoc networks in a mission-oriented UAV swarm.…”

Section: Introductionmentioning

confidence: 99%

“…In [19], a stabilitybased adaptive-period DSDV intra-cluster routing protocol was proposed to autonomously adapt the routing update interval based on the stability of links among nodes within the cluster structure. In [20], taking "spatial distance" as well as "time slot distance" into account, Gao et al proposed an improved GPSR routing protocol in TDMA-based UAV ad-hoc networks to improve the latency performance. In [21], Gangopadhyay et al proposed a position-based modified optimized link state routing protocol (OLSR) for FANET.…”

Section: Introductionmentioning

confidence: 99%

Optimized Routing Protocol Through Exploitation of Trajectory Knowledge for UAV Swarms

Zhou,

Tang,

Feng

et al. 2024

IEEE Trans. Veh. Technol.

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Due to its strong flexibility, easy deployment and extensive connectivity, unmanned aerial vehicle (UAV) swarm has been widely used in emergency communication in recent years, especially in the case when terrestrial communication infrastructures are no longer available. Within a UAV swarm, the design of routing protocol is one of the most challenging problems that enables cooperation among multiple UAVs to perform complex tasks in disaster areas. Nevertheless, the routing protocols reported so far have made the simplifying assumption that the UAV nodes move randomly. In the context of mission-oriented scenarios, such as emergency communication in disaster areas, this assumption appears evidently impractical. On the contrary, for many UAV applications, the trajectories of UAVs are pre-planned in advance through mission planning and trajectory planning. Disregarding trajectory information from the application layer may result in routing protocols struggling to adapt to rapid changes in network topology and facing challenges in achieving optimal performance across various communication metrics. To break the bottleneck, in this paper, we propose an efficient trajectory-based optimized routing protocol (TORP) for mission-oriented UAV swarm. Firstly, we model the packet routing problem using the concept of time-dependent graph. The objective is to maximize network performance, considering the power consumption and end-to-end (E2E) delay. The formulated problem is a binary linear programming (BLP) problem which is intractable to solve. Firstly, we propose a modified dynamically weighted Dijkstra's algorithm (MDWD). Based on the MDWD algorithm, we further propose an efficient optimization scheme to solve the constructed optimization problem. The simulation results demonstrate that our algorithm can make effective routing selections in dynamic UAV network, thereby improving the system performance in terms of packet delivery ratio, end-toend delay and power consumption.

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Flying Ad-Hoc Network Routing Protocol Based on Backward Algorithm

Yu,

2024

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Improvement of GPSR Routing Protocol for TDMA-Based UAV Ad-Hoc Networks

Cited by 10 publications

References 9 publications

UF‐GPSR: Modified geographical routing protocol for flying ad‐hoc networks

UF‐GPSR: Modified geographical routing protocol for flying ad‐hoc networks

Optimized Routing Protocol Through Exploitation of Trajectory Knowledge for UAV Swarms

Flying Ad-Hoc Network Routing Protocol Based on Backward Algorithm

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