Trust based energy efficient data collection with unmanned aerial vehicle in edge network

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.

Automated summary

In this article, a trust-based energy efficient data collection scheme using unmanned aerial vehicles is proposed, which optimizes trajectory and identifies trusted data to prolong network lifetime and improve data collection quality.