An Effective Routing Algorithm to Minimize the UAV Routing Time and Extend the Network Lifetime in Clustered IoT Network
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 o...
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| Format: | Article |
| Language: | English |
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Spolecnost pro radioelektronicke inzenyrstvi
2025-06-01
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| Series: | Radioengineering |
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| Online Access: | https://www.radioeng.cz/fulltexts/2025/25_02_0342_0352.pdf |
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| author | H. Zafor T. A. Sheikh N. Mazumdar A. Nag |
| author_facet | H. Zafor T. A. Sheikh N. Mazumdar A. Nag |
| author_sort | H. Zafor |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b7c05cfcf2ec446fb3f8800abe81f5b5 |
| institution | Kabale University |
| issn | 1210-2512 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Spolecnost pro radioelektronicke inzenyrstvi |
| record_format | Article |
| series | Radioengineering |
| spelling | doaj-art-b7c05cfcf2ec446fb3f8800abe81f5b52025-08-20T03:31:26ZengSpolecnost pro radioelektronicke inzenyrstviRadioengineering1210-25122025-06-01342342352An Effective Routing Algorithm to Minimize the UAV Routing Time and Extend the Network Lifetime in Clustered IoT NetworkH. ZaforT. A. SheikhN. MazumdarA. NagRecently, 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.https://www.radioeng.cz/fulltexts/2025/25_02_0342_0352.pdfinternet of things (iot)data collection (dc)unmanned aerial vehicles (uavs)ant colony optimization (aco)local search (ls)particle-swarm optimization (pso)genetic algorithm (ga) |
| spellingShingle | H. Zafor T. A. Sheikh N. Mazumdar A. Nag An Effective Routing Algorithm to Minimize the UAV Routing Time and Extend the Network Lifetime in Clustered IoT Network Radioengineering internet of things (iot) data collection (dc) unmanned aerial vehicles (uavs) ant colony optimization (aco) local search (ls) particle-swarm optimization (pso) genetic algorithm (ga) |
| title | An Effective Routing Algorithm to Minimize the UAV Routing Time and Extend the Network Lifetime in Clustered IoT Network |
| title_full | An Effective Routing Algorithm to Minimize the UAV Routing Time and Extend the Network Lifetime in Clustered IoT Network |
| title_fullStr | An Effective Routing Algorithm to Minimize the UAV Routing Time and Extend the Network Lifetime in Clustered IoT Network |
| title_full_unstemmed | An Effective Routing Algorithm to Minimize the UAV Routing Time and Extend the Network Lifetime in Clustered IoT Network |
| title_short | An Effective Routing Algorithm to Minimize the UAV Routing Time and Extend the Network Lifetime in Clustered IoT Network |
| title_sort | effective routing algorithm to minimize the uav routing time and extend the network lifetime in clustered iot network |
| topic | internet of things (iot) data collection (dc) unmanned aerial vehicles (uavs) ant colony optimization (aco) local search (ls) particle-swarm optimization (pso) genetic algorithm (ga) |
| url | https://www.radioeng.cz/fulltexts/2025/25_02_0342_0352.pdf |
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