Advanced Search
Volume 45 Issue 6
Jun.  2023
Turn off MathJax
Article Contents
HUANG Xiaoge, HE Yong, CHEN Qianbin, ZHANG Jie. Optimization Method for Energy Consumption in Data Acquisition Assisted by UAV Swarms[J]. Journal of Electronics & Information Technology, 2023, 45(6): 2054-2062. doi: 10.11999/JEIT220554
Citation: HUANG Xiaoge, HE Yong, CHEN Qianbin, ZHANG Jie. Optimization Method for Energy Consumption in Data Acquisition Assisted by UAV Swarms[J]. Journal of Electronics & Information Technology, 2023, 45(6): 2054-2062. doi: 10.11999/JEIT220554

Optimization Method for Energy Consumption in Data Acquisition Assisted by UAV Swarms

doi: 10.11999/JEIT220554
Funds:  The National Natural Science Foundation of China (61831002), The Innovation Project of the Common Key Technology of Chongqing Science and Technology Industry (cstc2018jcyjAx0383)
  • Received Date: 2022-05-07
  • Rev Recd Date: 2022-08-29
  • Available Online: 2022-09-05
  • Publish Date: 2023-06-10
  • To ensure security and reduce energy consumption for data acquisition in Wireless Sensor Network (WSN), Unmanned Aerial Vehicle (UAV) swarms-aided energy consumption optimization for data acquisition algorithm is proposed. The total energy consumption of the system is reduced by optimizing the number of UAVs, the height and the number of data transmissions in the WSN according to this algorithm. Firstly, for data acquisition in WSN, a Reputation baseD Data Dual Compression (RDDC) algorithm is proposed, which divides sensors into clusters according to geographic locations. In a cluster, there are one cluster head which is responsible for model selection, aggregation, and reputation update, and several cluster members which are responsible for training the prediction model and send it to the cluster head. Secondly, a UAV deployment optimization algorithm is proposed to minimize energy consumption of UAV swarms, which is transformed into a circular packing problem and solved by dynamically adjusting the number of UAVs. Moreover, a private blockchain is enabled in the UAV swarm to improve the security of the data acquisition process. Finally, the proposed method is verified by Berkeley Research Laboratory dataset and simulation results show that this method could optimize the deployment of UAVs, achieve small error, low energy consumption and high security.
  • loading
  • [1]
    FEKIH M A, BECHKIT W, RIVANO H, et al. Participatory air quality and urban heat Islands monitoring system[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1–14. doi: 10.1109/TIM.2020.3034987
    [2]
    PACHARANEY U S and GUPTA R K. Clustering and compressive data gathering in wireless sensor network[J]. Wireless Personal Communications, 2019, 109(2): 1311–1331. doi: 10.1007/s11277-019-06614-5
    [3]
    DAS S N, MISRA S, WOLFINGER B E, et al. Temporal-correlation-aware dynamic self-management of wireless sensor networks[J]. IEEE Transactions on Industrial Informatics, 2016, 12(6): 2127–2138. doi: 10.1109/TII.2016.2594758
    [4]
    TOLANI M, SUNNY, and SINGH R K. Lifetime improvement of wireless sensor network by information sensitive aggregation method for railway condition monitoring[J]. Ad Hoc Networks, 2019, 87: 128–145. doi: 10.1016/j.adhoc.2018.11.009
    [5]
    BAEK J, HAN S I, and HAN Y. Energy-efficient UAV routing for wireless sensor networks[J]. IEEE Transactions on Vehicular Technology, 2020, 69(2): 1741–1750. doi: 10.1109/TVT.2019.2959808
    [6]
    POPESCU D, STOICAN F, STAMATESCU G, et al. Advanced UAV–WSN system for intelligent monitoring in precision agriculture[J]. Sensors, 2020, 20(3): 817. doi: 10.3390/s20030817
    [7]
    XU Xiaobin, ZHAO Hui, YAO Haipeng, et al. A Blockchain-enabled energy-efficient data collection system for UAV-assisted IoT[J]. IEEE Internet of Things Journal, 2021, 8(4): 2431–2443. doi: 10.1109/JIOT.2020.3030080
    [8]
    LI Gang, HE Bin, WANG Zhipeng, et al. Blockchain-enhanced spatiotemporal data aggregation for UAV-assisted wireless sensor networks[J]. IEEE Transactions on Industrial Informatics, 2022, 18(7): 4520–4530. doi: 10.1109/TII.2021.3120973
    [9]
    YOUSSEF S B H, REKHIS S, and BOUDRIGA N. A blockchain based secure IoT solution for the dam surveillance[C]. IEEE Wireless Communications and Networking Conference (WCNC), Marrakesh, Morocco, 2019: 1–6.
    [10]
    AL-HOURANI A, KANDEEPAN S, and JAMALIPOUR A. Modeling air-to-ground path loss for low altitude platforms in urban environments[C]. IEEE Global Communications Conference, Austin, USA, 2014: 2898–2904.
    [11]
    GÁSPÁR Z and TARNAI T. Upper bound of density for packing of equal circles in special domains in the plane[J]. Periodica Polytechnica Civil Engineering, 2000, 44(1): 13–32.
    [12]
    GRANDI F. On the analysis of bloom filters[J]. Information Processing Letters, 2018, 129: 35–39. doi: 10.1016/j.ipl.2017.09.004
    [13]
    POLASTRE J, SZEWCZYK R, and CULLER D. Telos: Enabling ultra-low power wireless research[C]. IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005, Boise, USA, 2005: 364–369.
    [14]
    RAZA U, CAMERRA A, MURPHY A L, et al. Practical data prediction for real-world wireless sensor networks[J]. IEEE Transactions on Knowledge and Data Engineering, 2015, 27(8): 2231–2244. doi: 10.1109/TKDE.2015.2411594
    [15]
    王雷春, 马传香. 传感器网络中一种基于一元线性回归模型的空时数据压缩算法[J]. 电子与信息学报, 2010, 32(3): 755–758. doi: 10.3724/SP.J.1146.2009.00704

    WANG Leichun and MA Chuanxiang. A one-dimensional linear regression model based spatial and temporal data compression algorithm for wireless sensor networks[J]. Journal of Electronics &Information Technology, 2010, 32(3): 755–758. doi: 10.3724/SP.J.1146.2009.00704
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(12)  / Tables(3)

    Article Metrics

    Article views (952) PDF downloads(169) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return