Trust based energy efficient data collection with unmanned aerial vehicle in edge network - Jiang, Bo; Huang, Guosheng; Wang, Tian; Gui, Jinsong; Zhu, Xiaoyu (Transactions on emerging telecommunications technologies, 2022)
Large‐scale sensing devices spread over a wide area and compose the supervisory control and data acquisition (SCADA) system to remotely control and monitor a specific process through collecting the sensing data from the working field. However, the trustworthy and energy efficient data collection is still a challenging issue for large‐scale Internet of thing systems. In this article, a trust based energy efficient data collection with unmanned aerial vehicle (TEEDC‐UAV) scheme is proposed to prolong lifetime with trustworthy style. First, in TEEDC‐UAV scheme, an ant colony based unmanned aerial vehicle (UAV) trajectory optimization algorithm is proposed in which form the most data anchors in the working field with the trajectory as short as possible. Thus, the sensor nodes in SCADA system can be responsible for the least amount of data and greatly extend network life. Second, a trust reasoning and evolution mechanism is proposed to identify the trust degree of sensor nodes, and only trusted data will be collected so that the quality of data collection can be proved. In our proposed trust mechanism, the UAV can sense and collect data itself, so that data can be used as the baseline to identify the trust degree of sensor nodes. Finally, proved by sufficient experiment results, our proposed TEEDC‐UAV scheme can find an optimized data collection trajectory efficiently, which helps the energy consumption of the network become much more balanced. Compared with previous strategies, the network life is greatly improved by 48.9%. Meanwhile, the trust mechanism proposed in this article can also greatly improve the identification accuracy of node trust degree, which reached 91% when consuming only 8% network life. TEEDC‐UAV scheme is proposed to find a trust based and optimized UAV data collection trajectory in edge network efficiently. First TEEDC‐UAV get an optimized initial path, then add other sensing nodes passing through the UAV flight path as DAs (Data Anchors) to the flight path, and dynamically adjust the DAs and the UAV flight trajectory in multiple rounds of data collection to balance the energy consumption of nodes, which helps the energy consumption of network become much more balanced, and thus improves the network life. The TEEDC‐UAV scheme has good performance on the UAV path selection, network trust identification and energy consumption, as well as network life.