AoI-prioritized Multi-UAV Deployment and Resource Allocation Method in Scenarios with Differentiated User Requirements

JIN Feihong; ZHANG Jing; XIE Yaqin

doi:10.11999/JEIT251062

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JIN Feihong, ZHANG Jing, XIE Yaqin. AoI-prioritized Multi-UAV Deployment and Resource Allocation Method in Scenarios with Differentiated User Requirements[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251062

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JIN Feihong, ZHANG Jing, XIE Yaqin. AoI-prioritized Multi-UAV Deployment and Resource Allocation Method in Scenarios with Differentiated User Requirements[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251062

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AoI-prioritized Multi-UAV Deployment and Resource Allocation Method in Scenarios with Differentiated User Requirements

doi: 10.11999/JEIT251062 cstr: 32379.14.JEIT251062

School of Electronics and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China

Funds: Major Science and Technology Project of Jiangsu Province(BG2024002), The National Natural Science Foundation of China (62001238)

Received Date: 2025-10-09
Accepted Date: 2025-12-12
Rev Recd Date: 2025-12-09

Available Online: 2025-12-18

Abstract

Abstract

Objective In emergency scenarios such as natural disasters, ground-based fixed base stations are often damaged and may not be restored promptly. Because Unmanned Aerial Vehicles (UAVs) provide flexibility and low cost, UAV-assisted emergency communication has gained growing attention from academia and industry. However, existing studies on bandwidth and power allocation often overlook the heterogeneity of traffic demands among different Ground Users (GUs). They also do not fully address the effect of Age of Information (AoI) on the timeliness of emergency decision-making. Given differentiated traffic requirements and the direct effect of AoI on emergency response, this study proposes an AoI-based joint UAV deployment and resource allocation method for emergency communication. The objectives are: (1) to determine the minimum number of UAVs required while meeting the total GU traffic demand, and (2) to jointly optimize bandwidth, power, and Three-Dimensional (3D) UAV positions to minimize the system’s average AoI. Methods A two-stage approach that combines the Multiple UAV Deployment (MUD) algorithm and the Bandwidth, Power, and 3D Location (BPL) algorithm is proposed. For UAV quantity determination, the Particle Swarm Optimization (PSO) algorithm calculates the traffic density of each uncovered GU. The GU with the highest traffic density is selected as the core, and its adjacent GUs form a cluster. PSO optimizes the cluster position to maximize covered traffic volume while meeting UAV service constraints and determines the minimum number of UAVs required. For joint resource and position optimization, the BPL algorithm allocates bandwidth, power, and 3D locations. Bandwidth allocation uses an improved relaxation adjustment method in which weights are assigned based on GU data transmission time, and subchannels are allocated dynamically to balance transmission time. Power allocation follows the same structure. For 3D position optimization, the Whale Optimization Algorithm (WOA) is applied. After fixing the UAV’s horizontal position, the minimum height needed for coverage is derived using ellipse characteristics to reduce energy consumption. This converts the 3D search into a 2D search for the optimal position. Results and Discussions Simulation results confirm the effectiveness of the method. In a scenario with 100 GUs distributed randomly in a 1 km × 1 km area, 7 UAVs are required to achieve a 90% coverage rate (Fig. 2). The system’s average AoI under this deployment meets basic real-time communication requirements. Compared with benchmark algorithms such as Weighted K-Means (WKM) and Minimum Degree Prior (MDP), the MUD algorithm consistently uses fewer UAVs under different conditions of area size, GU quantity, and UAV service capability (Fig. 3). As the maximum GU traffic demand increases, data transmission time increases, which raises the required UAV count, whereas UAV climbing time decreases because cluster radii are smaller. There fore, the average AoI shows a slight decrease (Fig. 4). The improved allocation method yields better performance than average allocation. It reduces the maximum GU data transmission time by 26.35% (Fig. 5a) and assigns 16.7% more bandwidth and power to high-traffic GUs (Fig. 5b). This leads to more balanced transmission times and higher resource use efficiency. When compared with NBPL (no Bandwidth-Power and Location optimization), OL (Only Location optimization), and OBP (Only Bandwidth-Power optimization), the full BPL (Bandwidth-Power and Location optimization) algorithm achieves the lowest average AoI under different GU quantities. When the GU count is large, the BPL algorithm reduces the average AoI by about 21.1% compared with NBPL (Fig. 6a). The method also reaches the lowest total energy consumption per UAV among all compared schemes (Fig. 6b). Its computational complexity remains suitable for practical emergency deployment. Conclusions This study proposes an AoI-prioritized multi-UAV deployment and resource allocation method for emergency communication scenarios characterized by differentiated user traffic demands. The method integrates a PSO-enhanced MUD algorithm to determine the minimum UAV quantity and a BPL algorithm that jointly optimizes bandwidth, power, and 3D UAV positions using WOA and an improved allocation method. It meets three objectives: reducing UAV use, minimizing average AoI to maintain information freshness, and lowering energy consumption. Simulation results confirm advantages in deployment efficiency, AoI performance, and energy efficiency. Future work includes extending the method to non-LoS channel conditions, designing lower-complexity heuristic methods for larger-scale tasks, developing distributed optimization frameworks, and studying online joint trajectory and resource optimization methods for dynamic environments.
- Multi-Unmanned Aerial Vehicles (UAV),
- Traffic demand heterogeneity,
- Age of Information (AoI),
- Emergency communications,
- Resource allocation

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