重庆邮电大学通信与信息工程学院,重庆 400065
王茜竹(1975年生),女;研究方向:移动通信基带算法、多址接入算法;E-mail:wangqz@cqupt.edu.cn
吴广富(1980年生),男;研究方向:移动通信基带算法、多址接入算法; E-mail:wugf@cqupt.edu.cn
纸质出版日期:2024-09-25,
网络出版日期:2024-07-22,
收稿日期:2024-05-09,
录用日期:2024-06-11
移动端阅览
王茜竹,卢诗萱,吴广富.面向UAV辅助的WSN信息年龄优化算法[J].中山大学学报(自然科学版)(中英文),2024,63(05):148-155.
WANG Qianzhu,LU Shixuan,WU Guangfu.Age of information optimization algorithm for UAV-assisted WSNs[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2024,63(05):148-155.
王茜竹,卢诗萱,吴广富.面向UAV辅助的WSN信息年龄优化算法[J].中山大学学报(自然科学版)(中英文),2024,63(05):148-155. DOI: 10.13471/j.cnki.acta.snus.ZR20240151.
WANG Qianzhu,LU Shixuan,WU Guangfu.Age of information optimization algorithm for UAV-assisted WSNs[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2024,63(05):148-155. DOI: 10.13471/j.cnki.acta.snus.ZR20240151.
提出了一种综合传感器能源供给、数据传输时效性和移动用户需求的系统平均信息年龄(AoI)优化算法。首先,采用无人机(UAV)辅助WSN来保障传感器的能量收集和数据传输。其次,引入AoI作为衡量指标,联合优化多设备调度、发射功率和UAV轨迹,建立了以最小化传感器的平均AoI为目标的非凸优化问题。然后,通过约束松弛、变量替换和连续凸逼近等方法,将非凸问题转化为凸问题,并设计了一种迭代式的平均AoI最小化算法。仿真结果表明:该算法在满足移动用户体验的同时有效提升了传感器数据新鲜度。
It's proposed that a systematic average age of information optimization algorithm that integrates sensor energy supply, data transmission timeliness, and mobile user requirements. Firstly, an unmanned aerial vehicle assisted WSN is used to secure the energy collection and data transmission from the sensors. Secondly, AoI is introduced as a measure to jointly optimize multi-device scheduling, transmit power and UAV trajectories to establish a non-convex optimization problem with the objective of minimizing the average AoI of the sensors. Then, the non-convex problem is transformed into a convex one by means of constraint relaxation, variable substitution and successive convex approximation, and an iterative average AoI minimization algorithm is designed. Finally, the algorithm is verified through simulation, and the results show that the algorithm effectively improves the freshness of the sensor data while satisfying the mobile user experience.
无线传感器网络无人机信息年龄能量收集
wireless sensor networksunmanned aerial vehicleage of informationenergy harvesting
李新民, 尹宝林, 魏李莉,等, 2022. 强化学习无人机通信系统中的信息年龄优化[J]. 电子科技大学学报, 51(2): 213-218.
王茜竹, 胡洪瑞, 徐勇军,等, 2022. 基于能量收集的UAV-D2D网络资源分配算法[J]. 电子与信息学报, 44(3): 976-986.
王新雨, 汪驰升, 2019. 基于深度学习的密集人群安全监测系统[J]. 物联网技术, 9(11): 8-12+17.
ABD-ELMAGID M A, DHILLON H S, 2019. Average peak age-of-information minimization in UAV-assisted IoT networks[J]. IEEE Trans Veh Technol, 68(2): 2003-2008.
ALHAIDARI F, BALHARITH T, AL-YAHYAN E, 2019. Comparative analysis for task scheduling algorithms on cloud computing[C]//International Conference on Computer and Information Sciences.
AZARHAVA H, MUSEVI J, 2020. Energy efficient resource allocation in wireless energy harvesting sensor networks[J]. IEEE Wirel Commun Lett, 9(7): 1000-1003.
CHEN G, WU Q, CHEN W, et al, 2023. IRS-aided wireless powered MEC systems: TDMA or NOMA for computation offloading?[J]. IEEE Trans Wirel Commun, 22(2): 1201-1218.
KAUL S, YATES R, GRUTESER M, 2012. Real-time status: How often should one update?[C]// IEEE Infocom. Orlando, FL, USA:IEEE: 2731-2735.
LIU J, TONG P, WANG X, et al, 2021. UAV-aided data collection for information freshness in wireless sensor networks[J]. IEEE Trans Wirel Commun, 20(4): 2368-2382.
LI K, TANG X, VEERAVALLI B, et al, 2015. Scheduling precedence constrained stochastic tasks on heterogeneous cluster systems[J] IEEE Trans Comput,64(1): 191-204.
PANDEY G K, GURJAR D S, YADAV S, et al, 2023. UAV-empowered IoT network with hardware impairments and shadowing[J]. IEEE Sens Lett, 7(7): 1-4.
SAKSHI, CHETAN S, SHAMNEESH S, et al, 2022. A new median-average round Robin scheduling algorithm: An optimal approach for reducing turnaround and waiting time[J]. Alex Eng J, 61(12): 10527-10538.
WANG B J, ZHANG R Q, CHEN C, et al, 2020. Graph-based file dispatching protocol with D2D-enhanced UAV-NOMA communications in large-scale networks[J]. IEEE Internet Things J, 7(9): 8615-8630.
WANG J, GE Y, 2022. A radio frequency energy harvesting-based multihop clustering routing protocol for cognitive radio sensor networks[J]. IEEE Sens J, 22(7): 7142-7156.
WU F, YANG S, ZHENG Z, et al, 2021. Fine-grained user profiling for personalized task matching in mobile crowdsensing[J]. IEEE Trans Mobile Comput, 20(10): 2961-2976.
WU T H, LIU J F, LIU J, et al, 2022. A novel AI-based framework for AoI-optimal trajectory planning in UAV-assisted wireless sensor networks[J]. IEEE Trans Wirel Commun, 21(4): 2462-2475.
XIE L, XU J, ZHANG R, 2019. Throughput maximization for UAV-enabled wireless powered communication networks[J]. IEEE Internet Things J, 6(2): 1690-1703.
ZHANG G, SHEN C, SHI Q, et al, 2023. AoI minimization for WSN data collection with periodic updating scheme[J]. IEEE Trans Wirel Commun, 22(1): 32-46.
ZHANG J, ZHANG Y, XIANG L, et al, 2021. Robust energy-efficient transmission for wireless-powered D2D communication networks[J]. IEEE Trans Veh Tech, 70(8): 7951-7965.
ZHANG J, KANG K, YANG M, et al, 2022. AoI-minimization in UAV-assisted IoT network with massive devices[C]//IEEE Wireless Communications and Networking Conference (WCNC). Austin, TX, USA:IEEE: 1290-1295.
ZHANG S, ZHANG H, HAN Z, et al, 2020. Age of information in a cellular internet of UAVs: Sensing and communication trade-off design[J]. IEEE Trans Wirel Commun, 19(10): 6578-6592.
ZHOU M, CHEN H, SHU L, et al, 2022. UAV-assisted sleep scheduling algorithm for energy-efficient data collection in agricultural internet of things[J]. IEEE Internet Things J, 9(13): 11043-11056.
ZHU B, BEDEER E, NGUYEN H H, et al, 2023. UAV trajectory planning for AoI-minimal data collection in UAV-aided IoT networks by transformer[J]. IEEE Trans Wirel Commun, 22(2): 1343-1358.
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