Optimization of Total Energy Consumption for Unmanned Aerial Vehicle-enabled Wireless Sensor Networks
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摘要: 為降低無人機(Unmanned aerial vehicle, UAV)使能的無線傳感網的能量消耗, 延長網絡生命周期, 該文提出一種在地面節點能量預算下系統總能耗優化方法. 首先, 提出地面節點聚類方法, 利用目標函數確定最優簇數, 改進模糊C均值算法構建能量均衡的集群, 采用退避定時器機制根據隸屬度和能量值選擇各集群的最優簇頭, 減少地面節點的能耗. 其次, 根據已選簇頭位置, 利用遺傳算法規劃UAV的飛行軌跡, 減小UAV能耗. 最后, 通過單純形搜索算法和連續凸逼近算法聯合優化簇頭發射功率和UAV懸停位置, 減小數據采集時系統的總能耗. 仿真結果表明, 所提方法優于所比較的方案.Abstract: To reduce the total energy consumption for unmanned aerial vehicle (UAV) enabled wireless sensor networks and prolong the network lifetime, this paper proposes a scheme to optimize the total energy consumption of the system within the energy budget of sensor nodes. Firstly, a clustering algorithm for sensor nodes on the ground is proposed, where the optimal number of clusters is determined according to the objective function, then a fuzzy C-mean 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 sensor 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 sensor nodes and the UAV's hovering positions are optimized jointly by a simplex search algorithm and a successive convex approximation algorithm. The simulation results verify that the proposal outperforms the compared schemes.
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表 1 仿真參數
Table 1 Simulation parameter
參數符號 參數值 參數符號 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 ${{d}_{0}}$ 1 m $\phi $ 1 000 ${{\sigma }^{2}}$ ?174 dBm/Hz ${{v}_{u}}$ 15 m/s 表 2 不同算法的VSC比較
Table 2 VSC comparison of different algorithms
實驗次數 OCM-FCM算法 IEECP算法 SHM-FCM算法 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 表 3 網絡穩定性比較
Table 3 Comparison of network stability
聚類算法 FND HND LND WFND OCM-FCM 1 75 154 0.0065 IEECP 2 104 226 0.0089 SHM-FCM 9 176 416 0.0220 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 -
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