AoI‐Optimized UAV‐Assisted IoT Data Collection Using Hybrid Swarm Optimization and Collaborative Relay
ABSTRACT In urban environments, IoT devices often face limited direct connectivity to base stations (BS) because of physical obstructions, leading to high latency in data transmission, especially with sparse deployments. UAV‐assisted IoT systems present a solution by acting as relays to overcome the...
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| Published in: | International journal of communication systems Vol. 38; no. 10 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Chichester
Wiley Subscription Services, Inc
10.07.2025
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| Subjects: | |
| ISSN: | 1074-5351, 1099-1131 |
| Online Access: | Get full text |
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| Summary: | ABSTRACT
In urban environments, IoT devices often face limited direct connectivity to base stations (BS) because of physical obstructions, leading to high latency in data transmission, especially with sparse deployments. UAV‐assisted IoT systems present a solution by acting as relays to overcome these limitations. However, current UAV‐assisted IoT systems lack data relay among UAVs and require extensive travel distances, negatively impacting the Age of Information (AoI) performance. This work proposes an AoI‐sensitive data collection (ADC) method, which incorporates aerial collaborative relays to optimize UAV deployment and routing. To achieve this, we introduce the Osprey combined Tuna swarm optimization (OCTSO) algorithm, which considers time, distance, and energy constraints to extend the network lifetime. Additionally, a generalized AoI expectation (GAE) function is used to iteratively optimize UAV flight paths, ensuring minimal AoI. The OCTSO method achieved the minimum time of 1.32 × 10−7, outperforming all other methods, including I‐GWO (1.46 × 10−7), HPSO (1.60 × 10−7), WOA (1.64 × 10−7), DMOA (1.63 × 10−7), SHO (1.50 × 10−7), OOA (1.70 × 10−7), and TSO (1.70 × 10−7), respectively.
The architecture of a UAV, commonly known as a drone, encompasses various components that work together to enable its flight, navigation, and mission capabilities. By using the Base Station to facilitate communication between ground sensor nodes, the UAV serves as a Cluster Head. Therefore, the AoI is considered a crucial statistic for evaluating the effectiveness of data gathering in UAV‐assisted IoT since it provides a reliable indicator of the freshness of IoT data. The fact that the UAV, being responsible for the edge task area, has to go a long way to transmit data to the base station is a major problem with this technique. For the optimal UAV deployment and optimal routing of UAV, a new optimization algorithm, OCTSO algorithm is proposed in this work that combines TSO and OO algorithms. The given optimization problem is solved under the consideration of time, distance, and energy efficiency constraint that effectively prolongs the network's lifespan. The optimization problem defines the fitness of the minimization of AoI. |
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| Bibliography: | The authors received no specific funding for this work. Funding ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 1074-5351 1099-1131 |
| DOI: | 10.1002/dac.70134 |