GeNp-ODHR: Green Energic and Smart Network Performance Estimation-Based Optimized Deep Hello Routing for Flying Ad Hoc Network

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Authors Abstract
Content
The emergence of the flying ad hoc network (FANET) has gained popularity after COVID-19 pandemic. Due to disruptions in ground-based monitoring, aerial monitoring has become the preferred approach. Aerial communication has become essential, with multiple aerial vehicles equipped with sensors forming a FANET in a specific geographical area. These vehicles communicate autonomously in an ad-hoc fashion using hello packets, but the periodic transmission of these packets consumes a significant amount of energy. This type of aerial communication is particularly useful in infrastructure-fewer conditions, and the transition from 4G to 5G infrastructure has further facilitated aerial communication. To address limited flight periodicity of aerial vehicles due to onboard battery constraints, a new deep hello routing, GeNp-ODHR has been proposed to optimize the battery consumption and performance, which indirectly extended the flight time by saving the energy. Through simulation-based testing using Network Simulator version 3.0, GeNp-ODHR has been shown to achieve better performance in terms throughput, packet delivery ratio, end-to-end delay, and energy savings of approximately 4%–30%, indirectly extending the flight time. This investigation has also explored the potetial of unmanned aerial vehicles in the context of financial restrictions, technological infrastructure, and public acceptance. Additionally, it has highlighted the implications in terms of energy efficacy, scalability, and the recommendation for the next generation in addressing social inequality and environmental sustainability.
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DOI
https://doi.org/10.4271/12-08-03-0022
Pages
17
Citation
Saini, H., "GeNp-ODHR: Green Energic and Smart Network Performance Estimation-Based Optimized Deep Hello Routing for Flying Ad Hoc Network," SAE Int. J. CAV 8(3), 2025, https://doi.org/10.4271/12-08-03-0022.
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Publisher
Published
Sep 24
Product Code
12-08-03-0022
Content Type
Journal Article
Language
English