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An Effective Routing Algorithm to Minimize the UAV Routing Time and Extend the Network Lifetime in Clustered IoT Network

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Názov: An Effective Routing Algorithm to Minimize the UAV Routing Time and Extend the Network Lifetime in Clustered IoT Network
Autori: Zafor, H., Sheikh, T. A., Mazumdar, N., Nag, A.
Zdroj: Radioengineering, Vol 34, Iss 2, Pp 342-352 (2025)
Informácie o vydavateľovi: Brno University of Technology, 2025.
Rok vydania: 2025
Predmety: Genetic Algorithm (GA), data collection (dc), ant colony optimization (aco), local search (ls), Internet of Things (IoT), unmanned aerial vehicles (uavs), particle-swarm optimization (pso), TK1-9971, Local Search (LS), internet of things (iot), genetic algorithm (ga), Ant Colony Optimization (ACO), Electrical engineering. Electronics. Nuclear engineering, Particle-Swarm Optimization (PSO), Unmanned Aerial Vehicles (UAVs), Data Collection (DC)
Popis: Recently, unmanned aerial vehicles (UAVs) have become more popular due to their ease of adaptability and capability to carry out a variety of activities, including the delivery of services, monitoring and surveillance in military and civilian contexts. One of the most significant challenges in UAV operation is ensuring maximum network lifetime and management of their limited battery life. To solve these problems, we have proposed an effective routing algorithm that finds the best route to minimize UAV routing time and extend network lifetime. This is performed using the Ant Colony Optimization with Local Search (ACO-LS) algorithm for data collection from the clustered IoT network by UAV to ensure maximum network lifetime. It solved the routing problem in the minimum time in the presence of multiple charging stations and optimized the routing path. The simulation was carried out using various performance metrics: network lifetime (NT), energy consumption (EC), number of alive nodes (NAN), and packet delivery percentage (PDP). These parameters were compared with some existing algorithms such as Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) and found that our proposed algorithm performs better in terms of higher NT, less EC, more NAN, and higher PDP than the existing algorithms ACO, PSO, and GA.
Druh dokumentu: Article
Popis súboru: text; application/pdf
Jazyk: English
ISSN: 1210-2512
DOI: 10.13164/re.2025.0342
Prístupová URL adresa: https://doaj.org/article/b7c05cfcf2ec446fb3f8800abe81f5b5
https://hdl.handle.net/11012/250927
Prístupové číslo: edsair.doi.dedup.....a973683e2c91bf24d8f0ece7e557903e
Databáza: OpenAIRE
Popis
Abstrakt:Recently, unmanned aerial vehicles (UAVs) have become more popular due to their ease of adaptability and capability to carry out a variety of activities, including the delivery of services, monitoring and surveillance in military and civilian contexts. One of the most significant challenges in UAV operation is ensuring maximum network lifetime and management of their limited battery life. To solve these problems, we have proposed an effective routing algorithm that finds the best route to minimize UAV routing time and extend network lifetime. This is performed using the Ant Colony Optimization with Local Search (ACO-LS) algorithm for data collection from the clustered IoT network by UAV to ensure maximum network lifetime. It solved the routing problem in the minimum time in the presence of multiple charging stations and optimized the routing path. The simulation was carried out using various performance metrics: network lifetime (NT), energy consumption (EC), number of alive nodes (NAN), and packet delivery percentage (PDP). These parameters were compared with some existing algorithms such as Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) and found that our proposed algorithm performs better in terms of higher NT, less EC, more NAN, and higher PDP than the existing algorithms ACO, PSO, and GA.
ISSN:12102512
DOI:10.13164/re.2025.0342