ABNT: Adaptive beaconing and neighbor timeout for geographical routing in UAV networks

Singh, Vedant; Sharma, Krishna Pal; Verma, Harsh Kumar

doi:10.1007/s12083-022-01341-4

Cited by 6 publications

(9 citation statements)

References 49 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%

“…compute the link lifetime (LLT) between them. 40,51 Assume two UAVs U i and U j currently at position (x i , y i , z i ) and (x j , y j , z j ), flying with velocity v i and v j while making angle 𝜃 i , 𝜙 i and 𝜃 j , 𝜙 j , with respect to z-axis and y-axis, respectively. The LLT between U i and U j is computed:…”

Section: Neighborhood Discoverymentioning

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%

“…Thus, to maintain accurate neighborhood information in the FT, the link duration between neighboring nodes is estimated and employed as TOT. The relative velocity and Euclidean distance between adjacent nodes are used to compute the link lifetime (LLT) between them 40,51 . Assume two UAVs

{U}_i

and

{U}_j

currently at position

\left({x}_i,{y}_i,{z}_i\right)

and

\left({x}_j,{y}_j,{z}_j\right)

, flying with velocity

{v}_i

and

{v}_j

while making angle

{\theta}_i

{\phi}_i

and

{\theta}_j

{\phi}_j

, with respect to z‐axis and y‐axis, respectively.…”

Section: Proposed Schemementioning

confidence: 99%

See 3 more Smart Citations

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: Neighborhood Discoverymentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

{U}_i

and

{U}_j

currently at position

\left({x}_i,{y}_i,{z}_i\right)

and

\left({x}_j,{y}_j,{z}_j\right)

, flying with velocity

{v}_i

and

{v}_j

while making angle

{\theta}_i

{\phi}_i

and

{\theta}_j

{\phi}_j

, with respect to z‐axis and y‐axis, respectively.…”

Section: Proposed Schemementioning

confidence: 99%

See 2 more Smart Citations

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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show abstract

“…These challenges attract the attention of many researchers. In the recent years, researchers have studied and investigated the existing routing protocols in MANETs and VANETs [10][11][12]. These protocols cannot make effective communications in FANETs and must be improved to obtain good performance in these networks because they ignore the specific features of FANET such as the high dynamic topology, highly mobile nodes, and limited energy resources.…”

Section: Introductionmentioning

confidence: 99%

A Novel Optimized Link-State Routing Scheme with Greedy and Perimeter Forwarding Capability in Flying Ad Hoc Networks

Alsalami,

Yousefpoor,

Hosseinzadeh

et al. 2024

Mathematics

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A flying ad hoc network (FANET) is formed from a swarm of drones also known as unmanned aerial vehicles (UAVs) and is currently a popular research subject because of its ability to carry out complicated missions. However, the specific features of UAVs such as mobility, restricted energy, and dynamic topology have led to vital challenges for making reliable communications between drones, especially when designing routing methods. In this paper, a novel optimized link-state routing scheme with a greedy and perimeter forwarding capability called OLSR+GPSR is proposed in flying ad hoc networks. In OLSR+GPSR, optimized link-state routing (OLSR) and greedy perimeter stateless routing (GPSR) are merged together. The proposed method employs a fuzzy system to regulate the broadcast period of hello messages based on two inputs, namely the velocity of UAVs and position prediction error so that high-speed UAVs have a shorter hello broadcast period than low-speed UAVs. In OLSR+GPSR, unlike OLSR, MPR nodes are determined based on several metrics, especially neighbor degree, node stability (based on velocity, direction, and distance), the occupied buffer capacity, and residual energy. In the last step, the proposed method deletes two phases in OLSR, i.e., the TC message dissemination and the calculation of all routing paths to reduce routing overhead. Finally, OLSR+GPSR is run on an NS3 simulator, and its performance is evaluated in terms of delay, packet delivery ratio, throughput, and overhead in comparison with Gangopadhyay et al., P-OLSR, and OLSR-ETX. This evaluation shows the superiority of OLSR+GPSR.

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Adaptive Multi-UAV Control Algorithm by Using Deep Learning

Garg

2023

2023 International Conference on Data Science and Network Security (ICDSNS)

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ABNT: Adaptive beaconing and neighbor timeout for geographical routing in UAV networks

Cited by 6 publications

References 49 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

A Novel Optimized Link-State Routing Scheme with Greedy and Perimeter Forwarding Capability in Flying Ad Hoc Networks

Adaptive Multi-UAV Control Algorithm by Using Deep Learning

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