A Multi-Objective Optimization for enhancing the efficiency of Service in Flying Ad-Hoc Network Environment
Küçük Resim Yok
Tarih
2023
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Inst Computer Sciences, Social Informatics & Telecommunications Eng-Icst
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Flying Ad-hoc Network (FANET) is one among the emerging technology and it is used in the huge application of the intelligent communication system. FANETs are combined with multiple Unmanned Aerial Vehicles (UAVs) to control the complex environment. Due to high mobility in FANETs the computation overhead and computation delay of the network is greatly increased that reflects in the reduction of the performance of FANETs. So it becomes very essential to provide effective routing and optimization in FANETs to maintain the stable communication. For that purpose, in this paper MultiObjective Hybrid Optimization for Quality of Service (QoS) Assisted Flying Ad-Hoc Network (MOHOQFANET) approach is proposed with the combination of Ant colony optimization (ACO) and particle swarm optimization (PSO). To achieve effective routing in FANETs, reliability of ad-hoc that depend on demand vector routing (RAODV). In order to perform initial shortest path selection in FANETs, ACO algorithm is utilized. The PSO optimization is applied in FANETs to achieve the best optimal solution between the flying nodes during the time of communication between them. The MOHOQ-FANET technique is implemented using NS2 as the platform. As well as being compared to earlier studies like CSPO-FANET and OSNP-FANET, the performance of the FANETs is assessed using metrics like ratio of packet delivery, host-to-host delay, routing overhead, and network throughput. The outcomes have illustrated, as compared to earlier systems, the proposed MOHOQ-FANET approach delivers high packet delivery ratio and throughput as well as reduced host-to-host delay and routing overhead.
Açıklama
Anahtar Kelimeler
Flying Ad-hoc Network, Quality of Service, Ant colony optimization, particle swarm optimization, Adhoc On demand vector
Kaynak
Eai Endorsed Transactions On Scalable Information Systems
WoS Q Değeri
Q4
Scopus Q Değeri
Q3
Cilt
10
Sayı
5
