Optimization of Total Energy Consumption for Unmanned Aerial Vehicle-enabled Wireless Sensor Networks
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摘要: 为降低无人机(Unmanned aerial vehicle, UAV)使能的无线传感网(Wireless sensor networks, WSNs)的能耗, 延长网络生命周期, 提出一种在地面节点能量预算下系统总能耗优化方法. 首先, 提出地面节点聚类方法, 利用目标函数确定最优簇数, 改进模糊C均值(Fuzzy C-mean, FCM)算法构建能量均衡的集群, 采用退避定时器机制根据隶属度和能量值选择各集群的最优簇头, 减少地面节点的能耗; 然后, 根据已选簇头位置, 利用遗传算法规划UAV飞行轨迹, 减小UAV能耗; 最后, 通过单纯形搜索算法和连续凸逼近(Successive convex approximation, SCA)算法联合优化簇头发射功率和UAV悬停位置, 减小数据采集时系统的总能耗. 仿真结果表明, 该方法优于其他方法.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 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 (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 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 (SCA) algorithm. The simulation results verify that the proposal outperforms the compared schemes.
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图 2 不同簇头个数的系统总能耗
Fig. 2 Total energy consumption of the system with different numbers of cluster head
图 9 不同簇成员个数对系统能耗的影响
Fig. 9 Effect of different number of cluster members on system energy consumption
图 10 不同簇头能量预算对系统能耗的影响
Fig. 10 Impact of different cluster head energy budgets on system energy consumption
表 1 仿真参数
Table 1 Simulation parameter
参数 参数值 参数 参数值 $\alpha$ 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 $ 1000 ${{\sigma }^{2}}$ −174 dBm/Hz ${{v}_{u}}$ 15 m/s 
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表 2 不同算法的VSC值比较
Table 2 Comparison of VSC values for different algorithms
实验次数 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
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表 3 网络稳定性比较
Table 3 Comparison of network stability
算法名称 FND HND LND WFND OCM-FCM 1 75 154 0.0065 IEECP 2 104 226 0.0089 SHM-FCM 9 176 416 0.0220
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