Delay-Informed Intelligent Formation Control for UAV-Assisted IoT Application.

Multiple unmanned aerial vehicles (UAVs) have a greater potential to be widely used in UAV-assisted IoT applications. UAV formation, as an effective way to improve surveillance and security, has been extensively of concern. The leader-follower approach is efficient for UAV formation, as the whole formation system needs to find only the leader's trajectory. This paper studies the leader-follower surveillance system. Owing to different scenarios and assignments, the leading velocity is dynamic. The inevitable communication time delays resulting from information sending, communicating and receiving process bring challenges in the design of real-time UAV formation control. In this paper, the design of UAV formation tracking based on deep reinforcement learning (DRL) is investigated for high mobility scenarios in the presence of communication delay. To be more specific, the optimization UAV formation problem is firstly formulated to be a state error minimization problem by using the quadratic cost function when the communication delay is considered. Then, the delay-informed Markov decision process (DIMDP) is developed by including the previous actions in order to compensate the performance degradation induced by the time delay. Subsequently, an extended-delay informed deep deterministic policy gradient (DIDDPG) algorithm is proposed. Finally, some issues, such as computational complexity analysis and the effect of the time delay are discussed, and then the proposed intelligent algorithm is further extended to the arbitrary communication delay case. Numerical experiments demonstrate that the proposed DIDDPG algorithm can significantly alleviate the performance degradation caused by time delays.

Do economic policy uncertainty and financial development influence the renewable energy consumption levels in China?

Last few decades, several economic uncertainty challenges have emerged in the energy market. This study newly contributes to existing research by inspecting the asymmetric effect of economic policy uncertainty and financial development on renewable energy consumption in China. We employ a nonlinear ARDL approach by using a time-series dataset spanning from 1990 to 2019. Our symmetric model shows that economic policy uncertainty matters in the short run, as it increases renewable energy consumption while exhibiting a negative impact on renewable energy in long run in China. Our asymmetric results in the short and long run have deviated from the symmetric results. Our asymmetric results of the short and long run are similar in direction but different in magnitude. The results show that positive change in economic policy uncertainty has increased 3.216% and negative change in economic policy uncertainty has decreased 1.461% in renewable energy consumption in long run in China. Financial development does not matter in renewable energy consumption in China. Based on these outcomes, we can draw some robust economic policies in China as well as for other pollutant economies. Policymakers should be made economic policies more predictable in the modern era.