考虑无人机飞行时间的能耗与数据量联合优化

doi:10.3778/j.issn.1002-8331.2207-0348

摘要/Abstract

摘要： 在无线传感网络中运用无人机（unmanned aerial vehicle，UAV）通信进行数据采集是一项有价值的技术。针对UAV在有限时间内的数据采集任务，提出了一种考虑系统数据量、节点传输能耗和UAV飞行能耗的联合优化方案。该方案的决策空间包括UAV轨迹与传输调度，复杂度较高。由于联合优化是NP难问题，基于决策空间降维，将优化过程分为初始轨迹优化和二次轨迹优化两步。针对初始轨迹优化，提出基于贪心算法和禁忌搜索算法的优化方案，实现节点选择并得到UAV初始轨迹；针对二次轨迹优化，运用离散化方法转换原问题，采用逐次凸逼近算法进行优化，得到其有效次优解。仿真结果表明，所提优化方案能够在满足时间约束的前提下，提高UAV采集的数据量，并降低UAV和节点的能耗。

关键词: 无人机通信, 旋翼无人机, 轨迹优化

Abstract: The use of UAV communication for data acquisition in wireless sensor networks is a valuable technology. For UAV data collection tasks in limited time, a joint optimization scheme considering system data volume, node transmission energy consumption and UAV flight energy consumption is presented. The decision space of this scheme includes UAV trajectory and transmission scheduling with high complexity. Since the joint optimization is an NP-hard problem, based on the dimensionality reduction of the decision space, the optimization process is divided into two steps：initial trajectory optimization and secondary trajectory optimization. For initial trajectory optimization, an optimization scheme based on greedy algorithm and tabu search algorithm is proposed to achieve node selection and obtain the initial trajectory of the UAV. For quadratic trajectory optimization, the discretization method is used to convert the original problem, and the successive convex approximation algorithm is used to optimize it, and an effective suboptimal solution is obtained. The simulation results show that the optimized scheme can increase the amount of data collected by the UAV and reduce the energy consumption of the UAV and the nodes while meeting the time constraints.

Key words: unmanned aerial vehicle (UAV) communication, rotary-wing unmanned?aerial vehicle, trajectory optimization

朱江, 肖津. 考虑无人机飞行时间的能耗与数据量联合优化[J]. 计算机工程与应用, 2024, 60(1): 271-280.

ZHU Jiang, XIAO Jin. Joint Optimization of Energy Consumption and Data Volume Based on UAV Flight Time[J]. Computer Engineering and Applications, 2024, 60(1): 271-280.

参考文献

[1] 张平, 陶运铮, 张治. 5G若干关键技术评述[J]. 通信学报, 2016, 37(7): 15-29.
ZHANG P, TAO Y Z, ZHANG Z. Survey of several key technologies for 5G[J]. Journal on Communications, 2016, 37(7): 15-29.
[2] ANDEWS J G, BUZZI S, CHOI W, et al. What will 5G be?[J]. IEEE Journal on Selected Areas in Communications, 2014, 32(6): 1065-1082.
[3] 陈新颖, 盛敏, 李博, 等. 面向6G的无人机通信综述[J]. 电子与信息学报, 2022, 44(3): 781-789.
CHEN X Y, SHENG M, LI B, et al. Survey on unmanned aerial vehicle communications for 6G[J]. Journal of Electronics & Information Technology, 2022, 44(3): 781-789.
[4] QIAN Y W, WANG F F, LI J, et al. User association and path planning for UAV-aided mobile edge computing with energy restriction[J]. IEEE Wireless Communications Letters, 2019, 8(5): 1312-1315.
[5] YOU C S, PENG X M, ZHANG R. 3D trajectory design for UAV-enabled data harvesting in probabilistic LoS channel[C]//Proceedings of 2019 IEEE Global Communications Conference (GLOBECOM), 2019: 1-6.
[6] CHEN W C, ZHAO S J, ZHANG R Q, et al. UAV-assisted data collection with non-orthogonal multiple access[C]//Proceedings of the 2020 IEEE Wireless Communications and Networking Conference (WCNC), 2020: 1-6.
[7] CHENG Z, ZENG Y, ZHANG R. Energy-efficient data collection in UAV enabled wireless sensor network[J]. IEEE Wireless Communications Letters, 2018, 7(3): 328-331.
[8] CHENG Z, HONG L. Energy minimization in internet-of-things system based on rotary-wing UAV[J]. IEEE Wireless Communications Letters, 2019, 8(5): 1341-1344.
[9] XU X L, ZENG Y, GUANG Y L, et al. Overcoming endurance issue: UAV-enabled communications with proactive caching[J]. IEEE Journal on Selected Areas in Communications, 2018, 36(6): 1231-1244.
[10] WANG Y L, CHEN M, PAN C H, et al. Joint optimization of UAV trajectory and sensor uploading powers for UAV-assisted data collection in wireless sensor networks[J]. IEEE Internet of Things Journal, 2021: 11214-11226.
[11] ZHU B T, BEDEER E, NGUYEN H H, et al. UAV trajectory planning in wireless sensor networks for energy consumption minimization by deep reinforcement learning[J]. IEEE Transactions on Vehicular Technology, 2021, 70(9): 9540-9554.
[12] CHENG Z, ZENG Y. Aerial-ground cost tradeoff for multi-UAV-enabled data collection in wireless sensor networks[J]. IEEE Transactions on Communications, 2020, 68(3): 1937-1950.
[13] ZENG Y, XU J, ZHANG R. Energy minimization for wireless communication with rotary-wing UAV[J]. IEEE Transactions on Wireless Communications, 2019, 18(4): 2329-2345.
[14] LAPORTE G. The traveling salesman problem: an overview of exact and approximate algorithms[J]. European Journal of Operational Research, 1992, 59(2): 231-247.
[15] KARAMCHETI V, MALEK M. A TSP engine for performing tabu search[C]//Proceedings of the International Conference on Application Specific Array Processors, 1991: 309-321.
[16] 牟乃夏, 徐玉静, 李洁, 等. 遗传禁忌搜索算法收敛性和时间复杂度分析[J]. 河南理工大学学报 (自然科学版), 2018, 37(4): 118-122.
MOU N X, XU Y J, LI J, et al. Analyses of convergence and time complexity of genetic tabu search algorithm[J]. Journal of Henan Polytechnic University (Natural Science), 2018, 37(4): 118-122.