Evidence map›Paper›PMID 41471517›Full record

ArticleSensors (Basel, Switzerland)2025

SHARP-AODV: An Intelligent Adaptive Routing Protocol for Highly Mobile Autonomous Aerial Vehicle (AAV) Networks.

Nguyen Duc Tu, Ammar Muthanna, Abdukodir Khakimov, Irina Kochetkova, Konstantin Samouylov, Abdelhamied A Ateya, Andrey Koucheryavy

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Nguyen Duc TuDepartment of Telecommunication Networks and Data Transmission, The Bonch-Bruevich Saint-Petersburg State University of Telecommunications, Saint Petersburg 193232, Russia.ORCID 0009-0002-0055-1526
Ammar MuthannaInstitute of Computer Science and Telecommunications, Peoples' Friendship University of Russia (RUDN University), Moscow 117198, Russia.ORCID 0000-0003-0213-8145
Abdukodir KhakimovInstitute of Computer Science and Telecommunications, Peoples' Friendship University of Russia (RUDN University), Moscow 117198, Russia.ORCID 0000-0003-2362-3270
Irina KochetkovaInstitute of Computer Science and Telecommunications, Peoples' Friendship University of Russia (RUDN University), Moscow 117198, Russia.ORCID 0000-0002-1594-427X
Konstantin SamouylovInstitute of Computer Science and Telecommunications, Peoples' Friendship University of Russia (RUDN University), Moscow 117198, Russia.
Abdelhamied A AteyaEIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia.ORCID 0000-0002-1610-9612
Andrey KoucheryavyDepartment of Telecommunication Networks and Data Transmission, The Bonch-Bruevich Saint-Petersburg State University of Telecommunications, Saint Petersburg 193232, Russia.

Funding

RUDN University project No. 025322-2-000
6 · The paper itself

Abstract

In ad hoc networks employing Autonomous Aerial Vehicles (AAVs), the importance of real-time applications and edge computing is steadily increasing. However, existing routing protocols still fail to meet the strict performance requirements under the unique conditions of AAV networks, where the network topology changes continuously, and nodes move at high speed. This paper presents SHARP-AODV (Stability Heuristic Adaptive Routing Protocol-AODV), an enhanced routing protocol specifically developed for AAV networks. SHARP-AODV introduces two key innovations: (1) an intelligent RREQ (Route Request) dissemination mechanism that combines neighbor density control with a multi-parameter probabilistic model, and (2) a multi-criteria path selection mechanism that jointly considers hop count, link quality, and resource state. Simulation results in NS-3 across four distinct mobility models and various numbers of AAV nodes show that SHARP-AODV significantly outperforms standard AODV, improving packet delivery ratio (PDR) by up to 23.9%, increasing throughput by up to 61%, while reducing end-to-end delay by up to 87.8% and jitter by up to 90.6%. The proposed protocol is especially suitable for AAV-enabled applications in Edge Computing and Metaverse ecosystems that require low-latency, highly reliable connectivity with adaptation to dynamic network conditions. Furthermore, SHARP-AODV satisfies 6G network requirements for connection reliability, ultra-low latency, and high device density, unlocking new opportunities for employing AAVs in smart cities, environmental monitoring, and distributed VR/AR systems.

Indexed as

6Gadaptive routingad hoc networksAutonomous Aerial Vehicles (AAVs)edge computinglow latencyMetaverseQuality of Service (QoS)SHARP-AODV

Identifiers

PMID41471517
PMCPMC12737316

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.