機會無人機輔助數(shù)據(jù)收集的組網(wǎng)和資源分配方法

孫偉皓; 王海; 秦蓁; 屈毓錛

doi:10.11999/JEIT241053

機會無人機輔助數(shù)據(jù)收集的組網(wǎng)和資源分配方法

doi: 10.11999/JEIT241053

孫偉皓¹,
王海^1, ,,
秦蓁²,
屈毓錛³

1.
陸軍工程大學通信工程學院南京 210007
2.
武警士官學校杭州 311499
3.
南京航空航天大學電磁頻譜空間認知動態(tài)系統(tǒng)工信部重點實驗室南京 211106

基金項目: 國家自然科學基金(62171465)

詳細信息

作者簡介:
孫偉皓：男，博士生，研究方向為網(wǎng)絡(luò)規(guī)劃

王海：男，教授，研究方向為無線自組網(wǎng)和軟件定義網(wǎng)絡(luò)

秦蓁：女，講師，研究方向為邊緣計算和組合優(yōu)化

屈毓錛：男，副研究員，研究方向為邊緣計算和聯(lián)邦學習

通訊作者:
王?！?a href="mailto:hai_wang@aeu.edu.cn">hai_wang@aeu.edu.cn

中圖分類號: TN915.03
計量
- 文章訪問數(shù): 103
- HTML全文瀏覽量: 30
- PDF下載量: 11
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2024-11-28
- 修回日期: 2025-02-12
- 網(wǎng)絡(luò)出版日期: 2025-02-21

Networking and Resource Allocation Methods for Opportunistic UAV-assisted Data Collection

SUN Weihao¹,
WANG Hai^{1
, ,},
QIN Zhen²,
QU Yuben³

1.
College of Communication Engineering, Army Engineering University of PLA, Nanjing 210007, China
2.
Noncommissioned Officer Academy of PAP, Hangzhou 310000, China
3.
The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space, Ministry of Industry and Information Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China

Funds: The National Natural Science Foundation of China (62171465)

摘要

摘要: 配備存儲部件的機會無人機打開了數(shù)據(jù)傳輸?shù)臋C會時間窗口，在低空數(shù)據(jù)收集系統(tǒng)中呈現(xiàn)巨大的潛力。為了提高數(shù)據(jù)收集效率，移動用戶可以主動組網(wǎng)，將數(shù)據(jù)預(yù)先集聚到具備位置優(yōu)勢的簇頭節(jié)點，由簇頭節(jié)點負責上傳，實現(xiàn)時空維度的流量塑形。該文研究了機會無人機輔助數(shù)據(jù)收集的組網(wǎng)和資源分配方法。具體而言，如何根據(jù)機會無人機的既定航跡，通過聯(lián)合優(yōu)化用戶的子網(wǎng)數(shù)據(jù)傳輸策略、子網(wǎng)資源分配策略和子網(wǎng)形成策略，最大化全網(wǎng)數(shù)據(jù)上傳總量。上述問題高度耦合且具有海量的狀態(tài)空間，較難求解。該文通過推導(dǎo)閉式表達式求解子網(wǎng)數(shù)據(jù)傳輸和資源分配子問題，通過聯(lián)盟博弈求解子網(wǎng)形成子問題。最終提出了一種迭代優(yōu)化算法來獲得具有高效、可靠、自組織和低復(fù)雜度的解決方案。仿真結(jié)果表明所提方法能夠有效提升數(shù)據(jù)收集效率。同獨立上傳策略以及基于距離聚類和傳統(tǒng)聯(lián)盟博弈組網(wǎng)策略相比，所提方案的數(shù)據(jù)上傳總量分別提升了56.3%，51.6%和17.8%。
- 無人機輔助通信 /
- 機會傳輸 /
- 資源分配 /
- 聯(lián)盟博弈
Abstract: Objective Unmanned Aerial Vehicles (UAVs) tasked with customized operations, such as environmental monitoring and intelligent logistics, are referred to as opportunistic UAVs. These UAVs, while traversing the task area, can be leveraged by ground nodes in regions that are either uncovered or heavily loaded, enabling them to function as data storage. This reduces the operational costs associated with deploying dedicated UAVs for data collection. In practice, however, the flight paths of opportunistic UAVs are uncontrolled, and the data-uploading capabilities of ground nodes in various regions vary. To enhance efficiency, ground nodes can actively form a network, pre-aggregate data, and allocate resources to cluster head nodes located advantageously for data transmission. Despite extensive research into networking technologies, two key challenges remain. First, existing studies predominantly focus on static networking strategies, overlooking the reliability of data aggregation in mobile scenarios. Ground nodes involved in tasks such as emergency response, disaster relief, or military reconnaissance may exhibit mobility. The dynamic topology of these mobile nodes, coupled with non-line-of-sight transmission path loss and severe signal fading, creates substantial challenges for reliable transmission, leading to bit errors, packet losses, and retransmissions. Therefore, mobile ground nodes must dynamically adjust their subnet data transmission strategies based on the time-varying relative distances between cluster members and heads.Second, most studies focus on data aggregation capacity within subnets but fail to consider the uploading capabilities of cluster heads. In opportunistic communication scenarios, where UAV flight paths are uncontrolled, the data-uploading capacity of each subnet is constrained by the minimum of the data collected, aggregation capacity, and uploading capability. Therefore, effective networking strategies for opportunistic UAV-assisted data collection must account for the relationships between cluster members, cluster heads, and UAVs. Coordinated resource allocation and subnet formation strategies are essential to improving system performance. In summary, exploring networking and resource allocation methods for opportunistic UAV-assisted data collection is of significant practical importance. Methods Due to the interdependent nature of the subnet data transmission, resource allocation, and formation strategies, the problem presents a large state space that is difficult to solve directly. To address this, a decomposition approach is applied. First, given the subnet formation strategy, the paper sequentially derives the closed-form solutions for the subnet data transmission and resource allocation strategies, significantly simplifying the original problem. Next, the subnet formation subproblem is modeled as a formation game. An altruistic networking criterion is proposed, and using potential game theory, it is proven that the formulated game has at least one pure strategy Nash equilibrium. A subnet formation strategy based on the best response method is proposed. Finally, the convergence and complexity of the proposed algorithm are analyzed. Results and Discussions Simulation results confirm the effectiveness of the proposed algorithm. As shown in the networking diagram, the algorithm predominantly selects nodes near the flight path as cluster heads due to their superior data uploading capabilities (Fig. 2, Fig. 3(a)). The data uploaded is constrained by the minimum values of the data collected, data aggregation capacity, and data uploading capacity, creating a bottleneck. In this context, the algorithm balances subnet data aggregation and uploading capacities, ultimately improving transmission efficiency (Fig. 3(b)). Additionally, the relationship between distance and subnet data transmission strategy is evaluated. Specifically, the proposed transmission strategy reduces the amount of data aggregated for reliability as the distance increases, while increasing data aggregation for efficiency when the distance decreases (Fig. 4). This dynamic transmission approach enhances reliability as the amount of aggregated data fluctuates (Fig. 5(a)). Furthermore, the proposed algorithm outperforms benchmark networking schemes with increasing iteration numbers, demonstrating up to a 56.3% improvement (Fig. 5(b)). Finally, regardless of variations in flight speed, the proposed algorithm consistently shows superior transmission efficiency (Fig. 5(c)). Conclusions This paper explores terrestrial networking and resource allocation methods to enhance the transmission efficiency of opportunistic UAV-assisted data collection. The strategies for subnet data transmission, resource allocation, and formation are jointly addressed. The paper derives closed-form solutions for the subnet data transmission and resource allocation strategies sequentially, followed by the formulation of the subnet formation strategy as a formation game, which is solved using the best response method. Extensive simulation results validate the performance improvements. However, this study considers only scenarios with a single opportunistic UAV. In practical applications, multiple UAVs may coexist, requiring further analysis of the time-varying relationships between cluster heads and UAVs in future work.
- UAV-assisted communication /
- Opportunistic transmission /
- Resource allocation /
- Coalition game

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圖 1 系統(tǒng)模型

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圖 2 組網(wǎng)策略示意圖

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圖 3 數(shù)據(jù)采集和上傳性能對比

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圖 4 可靠傳輸策略和傳輸距離的關(guān)系

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圖 5 不同算法的性能對比

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1 機會無人機輔助的組網(wǎng)和資源分配算法

輸入：用戶的數(shù)據(jù)量 ${\varGamma _i}$ ，用戶移動軌跡 $l_i^t$ ，無人機航跡 $l_m^t$ ，基　本參數(shù) ${\mathcal{T}^{\rm g}},{\mathcal{T}^{\mathrm{u}}},{d_{{\mathrm{th}}}},{B_0}$
輸出：子網(wǎng)數(shù)據(jù)傳輸策略 ${\boldsymbol{Q}}$ ，子網(wǎng)資源分配策略 ${\boldsymbol{B}}$ 和子網(wǎng)形成　策略 ${\bf{Co}}$
(1) 初始化組網(wǎng)分組，每個節(jié)點自成一個聯(lián)盟
(2) FOR $t = 1:{T_{{\mathrm{iter}}}}$
(3) 　 $i = \text{mod} (t,U) + 1$
(4) 　用戶 ${u_i}$ 離開當前聯(lián)盟 ${\text{C}}{{\text{o}}_n}$ 探索加入聯(lián)盟 ${{\mathrm{Co}}_{n'}}$
(5) 　簇頭 $u_0^n$ 和 $u_0^{n'}$ 根據(jù) $\bar Q_{i,n}^({\Pr ^{{\text{req}}}})$ 、 $\bar Q_{i,n'}^({\Pr ^{{\text{req}}}})$ 和式(26)更　　　新子網(wǎng)資源分配策略
(6) 　用戶 ${u_i}$ 根據(jù)式(20)更新數(shù)據(jù)傳輸策略
(7) 　If 聯(lián)盟切換滿足互利準則(33)do
(8) 　　聯(lián)盟結(jié)構(gòu)變更， ${{\mathrm{Co}}_n} = {{\mathrm{Co}}_n}\backslash \{ {u_i}\} ,{{\mathrm{Co}}_{n'}} = {{\mathrm{Co}}_{n'}} \cup \{ {u_i}\}$
(9) 　End If
(10) End For

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