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Path Planning Algorithm for UAV Sensing Data Collection Based on the Efficacy Function

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IoT as a Service (IoTaaS 2018)

Abstract

Data collection is one of the most fundamental tasks of wireless sensor networks. At present, the information collection methods of the sensor network mainly include static information collection methods and information collection methods based on mobile sink nodes. Static information collection methods have “energy void problem”. However, in another method, the movement of sink nodes will be limited by the environmental terrain. Therefore, these two methods are difficult to effectively collect information in many application scenarios for a long time. In order to solve the above problems, we use the UAV to collect sensing data from the sensor network. It can also choose the order of collecting information based on the importance and the demand of information. In order to solve the problem of unmanned aerial vehicle’s energy limitation and time delay of data collection in the real environment, an efficiency function is constructed which considered data value, energy consumption, time and risk. An improved A* path planning algorithm based on efficiency function is proposed for planning the flying path between SDG nodes. We also propose a bee colony path planning algorithm for solving the problem of SDG node allocation and access order.

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Correspondence to Siqi Tao .

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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Tao, S., He, J., Zhang, Y., Ji, W., Chen, L. (2019). Path Planning Algorithm for UAV Sensing Data Collection Based on the Efficacy Function. In: Li, B., Yang, M., Yuan, H., Yan, Z. (eds) IoT as a Service. IoTaaS 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 271. Springer, Cham. https://doi.org/10.1007/978-3-030-14657-3_17

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  • DOI: https://doi.org/10.1007/978-3-030-14657-3_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-14656-6

  • Online ISBN: 978-3-030-14657-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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