無(wú)人機使能的無(wú)線(xiàn)傳感網(wǎng)總能耗優(yōu)化方法
doi: 10.16383/j.aas.c220914 cstr: 32138.14.j.aas.c220914
-
1.
重慶郵電大學(xué)自動(dòng)化學(xué)院工業(yè)物聯(lián)網(wǎng)與網(wǎng)絡(luò )化控制教育部重點(diǎn)實(shí)驗室 重慶 400065
基金項目: 國家自然科學(xué)基金(92267106, 61972061), 重慶英才計劃基礎研究與前沿探索項目(cstc2021ycjh-bgzxm0017)資助
Optimization of Total Energy Consumption for Unmanned Aerial Vehicle-enabled Wireless Sensor Networks
-
1.
Key Laboratory of Industrial Internet of Things & Networked Control, Ministry of Education, College of Automation, Chongqing University of Posts and Telecommunications, Chong-qing 400065
Funds: Supported by National Natural Science Foundation of China (92267106, 61972061) and Fundamental Research and Frontier Exploration Program of Chongqing (cstc2021ycjh-bgzxm0017)
-
摘要: 為降低無(wú)人機(Unmanned aerial vehicle, UAV)使能的無(wú)線(xiàn)傳感網(wǎng)(Wireless sensor networks, WSNs)的能耗, 延長(cháng)網(wǎng)絡(luò )生命周期, 提出一種在地面節點(diǎn)能量預算下系統總能耗優(yōu)化方法. 首先, 提出地面節點(diǎn)聚類(lèi)方法, 利用目標函數確定最優(yōu)簇數, 改進(jìn)模糊C均值(Fuzzy C-mean, FCM)算法構建能量均衡的集群, 采用退避定時(shí)器機制根據隸屬度和能量值選擇各集群的最優(yōu)簇頭, 減少地面節點(diǎn)的能耗; 然后, 根據已選簇頭位置, 利用遺傳算法規劃UAV飛行軌跡, 減少UAV能耗; 最后, 通過(guò)單純形搜索算法和連續凸逼近(Successive convex approximation, SCA)算法聯(lián)合優(yōu)化簇頭發(fā)射功率和UAV懸停位置, 減少數據采集時(shí)系統的總能耗. 仿真結果表明, 該方法優(yōu)于其他方法.
-
關(guān)鍵詞:
- 無(wú)線(xiàn)傳感網(wǎng) /
- 無(wú)人機 /
- 能耗 /
- 聚類(lèi) /
- 軌跡規劃
Abstract: To reduce the total energy consumption for unmanned aerial vehicle (UAV) enabled wireless sensor networks (WSNs) and prolong the network lifetime, this paper proposes a scheme to optimize the total energy consumption of the system within the energy budget of ground nodes. Firstly, a clustering algorithm for ground nodes is proposed, where the optimal number of clusters is determined according to the objective function, then a fuzzy C-mean (FCM) algorithm is improved to form the energy-balanced clusters and a receding timer mechanism is employed to select the optimal cluster heads based on the affiliation and energy values, so as to reduce the energy consumption of ground nodes. Secondly, the flight trajectory of the UAV is planned according to the locations of the selected cluster heads by employing a genetic algorithm, which cuts down the energy consumption of UAV. Finally, the transmit power of the ground nodes and the UAV＇s hovering positions are optimized jointly by a simplex search algorithm and a successive convex approximation (SCA) algorithm to decrease the total energy consumption of the system for data collection. The simulation results verify that the proposal outperforms the compared schemes. -
圖 2 不同簇頭個(gè)數的系統總能耗
Fig. 2 Total energy consumption of the system with different numbers of cluster head
圖 9 不同簇成員個(gè)數時(shí)簇頭發(fā)射功率與系統能耗/懸停位置的關(guān)系
Fig. 9 Relationship between cluster head transmit power and system energy consumption/hovering positions with different numbers of cluster members
圖 10 不同簇頭能量預算對系統能耗的影響
Fig. 10 Impact of different cluster head energy budgets on system energy consumption
表 1 仿真參數
Table 1 Simulation parameter
參數 參數值 參數 參數值 $\alpha$ 0.03 ${{v}_{v}}$ 10 m/s $\beta$ 10 ${{E}_{cap}}$ 50 J $\eta LoS$ 3 dB $l$ 1 Mb $\eta NLoS$ 13 dB ${{\alpha }_{1}}$,${{\alpha }_{2}}$ 0.5 ${rf50c1hsl6_{0}}$ 1 m $\phi $ 1000 ${{\sigma }^{2}}$ ?174 dBm/Hz ${{v}_{u}}$ 15 m/s 
下載: 導出CSV
表 2 不同算法的VSC值比較
Table 2 Comparison of VSC values for different algorithms
實(shí)驗次數 OCM-FCM IEECP SHM-FCM 1 428.40 52.85 48.50 2 362.35 49.05 46.70 3 271.15 66.55 57.65 4 254.20 51.75 43.45 5 272.40 58.65 50.50 6 387.50 52.90 31.75 7 329.15 49.35 43.54 8 289.45 58.45 62.55 9 290.25 55.80 55.20 10 319.15 46.75 37.50
下載: 導出CSV
表 3 網(wǎng)絡(luò )穩定性比較
Table 3 Comparison of network stability
算法名稱(chēng) FND HND LND WFND OCM-FCM 1 75 154 0.0065 IEECP 2 104 226 0.0089 SHM-FCM 9 176 416 0.0220
下載: 導出CSV
亚洲第一网址_国产国产人精品视频69_久久久久精品视频_国产精品第九页<button id="qm3rj"><thead id="qm3rj"></thead></button> <samp id="qm3rj"></samp> <source id="qm3rj"><menu id="qm3rj"><pre id="qm3rj"></pre></menu></source> <video id="qm3rj"><code id="qm3rj"></code></video> <tt id="qm3rj"><track id="qm3rj"></track></tt> -
[1] Li J X, Zhao H T, Wang H J, Gu F L, Wei J B, Yin H, et al. Joint optimization on trajectory, altitude, velocity, and link scheduling for minimum mission time in UAV-aided data collection. IEEE Internet of Things Journal, 2019, 7(2): 1464?1475 [2] 王峰, 黃子路, 韓孟臣, 邢立寧, 王凌. 基于KnCMPSO算法的異構無(wú)人機協(xié)同多任務(wù)分配. 自動(dòng)化學(xué)報, 2023, 49(2): 399?414 doi: 10.16383/j.aas.c210696Wang Feng, Huang Zi-Lu, Han Meng-Chen, Xing Li-Ning, Wang Ling. A knee point based coevolution multi-objective particle swarm optimization algorithm for heterogeneous UAV cooperative multi-task allocation. Acta Automatica Sinica, 2023, 49(2): 399?414 doi: 10.16383/j.aas.c210696 [3] Samir M, Sharafeddine S, Assi C M, Nguyen T M, Ghrayeb A. UAV trajectory planning for data collection from time-constr-ained IoT devices. IEEE Transactions on Wireless Communications, 2019, 19(1): 34?46 [4] 劉志新, 趙松晗, 楊毅, 袁亞洲. 智能反射面輔助的無(wú)人機無(wú)線(xiàn)攜能通信網(wǎng)絡(luò )吞吐量最大化算法研究. 電子與信息學(xué)報, 2022, 44(7): 2325?2331 doi: 10.11999/JEIT220195Liu Zhi-Xin, Zhao Song-Han, Yang Yi, Yuan Ya-Zhou. Thro-ugh put maximization algorithm for intelligent reflecting surface-aided unmanned aerial vehicle communication networks with wireless energy transfer. Journal of Electronics & Information Technology, 2022, 44(7): 2325?2331 doi: 10.11999/JEIT220195 [5] Heinzelman W B, Chandrakasan A P, Balakrishnan H. Approximate policy-based accelerated deep reinforcement learning. An Application-specific Protocol Architecture for Wireless Microsensor Networks, 2002, 1(4): 660?670 [6] Behera T M, Mohapatra S K, Samal U C, Khan S M, Daneshmand M, Gandomi A H. Residual energy-based cluster-head selection in WSNs for IoT application. IEEE Internet of Things Journal, 2019, 6(3): 5132?5139 doi: 10.1109/JIOT.2019.2897119 [7] Su S C, Zhao S G. An optimal clustering mechanism based on Fuzzy-C means for wireless sensor networks. Sustainable Computing: Informatics and Systems, 2018, 18: 127?134 doi: 10.1016/j.suscom.2017.08.001 [8] Hassan A A H, Shah W M, Habeb A H H, Othman M F I, Al-Mhiqani M N. An improved energy-efficient clustering protocol to prolong the lifetime of the WSN-based IoT. IEEE Access, 2020, 8: 200500?200517 doi: 10.1109/ACCESS.2020.3035624 [9] Zhan C, Lai H. Energy minimization in internet-of-things system based on rotary-wing UAV. IEEE Wireless Communications Letters, 2019, 8(5): 1341?1344 doi: 10.1109/LWC.2019.2916549 [10] Zhan C, Zeng Y, Zhang R. Energy-efficient data collection in UAV enabled wireless sensor network. IEEE Wireless Communications Letters, 2017, 7(3): 328?331 [11] Ebrahimi D, Sharafeddine S, Ho P H, Assi C. UAV-aided projection-based compressive data gathering in wireless sensor networks. IEEE Internet of Things Journal, 2018, 6(2): 1893?1905 [12] Chen J M, Li S Y, Chen S, He S B, Shi Z G. Q-charge: A quadcopter-based wireless charging platform for large-scale sensing applications. IEEE Network, 2017, 31(6): 56?61 doi: 10.1109/MNET.2017.1700071 [13] Zeng Y, Xu J, Zhang R. Energy minimization for wireless communication with rotary-wing UAV. IEEE Transactions on Wireless Communications, 2019, 18(4): 2329?2345 doi: 10.1109/TWC.2019.2902559 [14] Zeng Y, Zhang R. Energy-efficient UAV communication with trajectory optimization. IEEE Transactions on Wireless Communications, 2017, 16(6): 3747?3760 doi: 10.1109/TWC.2017.2688328 [15] Ghdiri O, Jaafar W, Alfattani S, Abderrazak J B, Yanikomeroglu H. Offline and online UAV-enabled data collection in time-constrained IoT networks. IEEE Transactions on Green Communications and Networking, 2021, 5(4): 1918?1933 doi: 10.1109/TGCN.2021.3104801 [16] Yang D C, Wu Q Q, Zeng Y, Zhang R. Energy tradeoff in ground-to-UAV communication via trajectory design. IEEE Transactions on Vehicular Technology, 2018, 67(7): 6721?6726 doi: 10.1109/TVT.2018.2816244 [17] Zhan C, Huang R J. Energy minimization for data collection in wireless sensor networks with UAV. In: Proceedings of the IEEE Global Communications Conference. Waikoloa, USA: IEEE, 2019. 1?6 [18] 王巍, 彭力, 趙繼軍, 朱天宇, 崔益豪, 田立勤. 基于旋翼無(wú)人機近地面空間應急物聯(lián)網(wǎng)節點(diǎn)動(dòng)態(tài)協(xié)同部署. 自動(dòng)化學(xué)報, 2021, 47(8): 2002?2015 doi: 10.16383/j.aas.c180146Wang Wei, Peng Li, Zhao Ji-Jun, Zhu Tian-Yu, Cui Yi-Hao, Tian Li-Qin. Dynamic cooperative deployment of emergency internet of things near ground space based on drone. Acta Automatica Sinica, 2021, 47(8): 2002?2015 doi: 10.16383/j.aas.c180146 [19] 李安, 戴龍斌, 余禮蘇, 王振. 加權能耗最小化的無(wú)人機輔助移動(dòng)邊緣計算資源分配策略. 電子與信息學(xué)報, 2022, 44(11): 3858?3865 doi: 10.11999/JEIT210832Li An, Dai Long-Bin, Yu Li-Su, Wang Zhen. Resource allocation for unmanned aerial vehicle-assisted mobile edge computing to minimize weighted energy consumption. Journal of Electronics & Information Technology, 2022, 44(11): 3858?3865 doi: 10.11999/JEIT210832 [20] Zhu B T, Bedeer E, Nguyen H H, Barton R, Henry J. UAV trajectory planning in wireless sensor networks for energy consumption minimization by deep reinforcement learning. IEEE Transactions on Vehicular Technology, 2021, 70(5): 9540?9554 [21] Zhu B T, Bedeer E, Nguyen H H, Barton R, Henry J. Joint cluster head selection and trajectory planning in UAV-aided IoT networks by reinforcement learning with sequential model. IEEE Internet of Things Journal, 2021, 9(14): 12071?12084 [22] Mozaffari M, Saad W, Bennis M, Debbah M. Mobile unmanned aerial vehicles (UAVs) for energy-efficient Internet of Things communications. IEEE Internet of Things Journal, 2017, 16(11): 7574?7589 -
圖(10) / 表(3)
計量
- 文章訪(fǎng)問(wèn)數: 571
- HTML全文瀏覽量: 177
- PDF下載量: 42
- 被引次數: 0
