Interface protocol design: a communication guide for indoor FANET

Sadiq, Bashir Olaniyi

doi:10.12928/telkomnika.v17i6.13296

Cited by 2 publications

(2 citation statements)

References 18 publications

(27 reference statements)

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“…Owning that the future routing protocols depend on the nature of the communication link, the works in [26,27] present the design of an interface protocol for an indoor Flying Ad-hoc Network-specific routing protocol, using light fidelity as a communication link. The focus was to achieve high throughput.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The routing protocol used to share the captured information from the UAVs in the grazing field monitoring was a Specific Routing Protocol for Flying Adhoc Network, which was coined from the standard Ad Hoc On-Demand Distance Vector (AODV) as detailed in [26,27]. The standard AODV is a loop-free routing system designed to self-start in a network of nodes and withstand a variety of network behaviors such as node connection failures, mobility, and packet losses.…”

Section: Algorithm 1 Grazing Field Algorithmmentioning

confidence: 99%

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Livestock Management on Grazing Field: A FANET Based Approach

et al. 2022

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In recent times, designated grazing areas/fields or routes for livestock grazing are usually defined. Hence, their herding activities’ success relies on data extracted from aerial photographs. As such, a direct and cost-effective way of monitoring livestock for perimeter coverage and in other natural situations is required. This paper presents a coverage solution involving multiple unmanned aerial vehicles interacting. The presented approach is built on a graph, with geographic coordinates set such that several Unmanned Aerial Vehicles (UAVs) can successfully cover the area. The maximum flying time determines the number of UAVs employed for coverage. The proposed solution is thought to solve some practical problems encountered during the execution of the task with actual UAVs. It is suitable for long-term monitoring of animal behavior under various weather conditions and observing the relationship between livestock distribution and available resources on a grazing field. The simulation was carried out using MATLAB and aerial images from Google Earth.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Algorithm 1 Grazing Field Algorithmmentioning

confidence: 99%