Mobile Crowd Sensing Based Content Sharing And Energy Efficiency Optimization

Due to the proliferation of smart devices,the large quantity of data,and the trend of high energy consumption,the explosively growing demand for efficient content sharing and secure data collection in wireless communications has soared at a tremendous pace over recent years.In order to solve the problem of large-scale efficient content sharing in wireless communication systems,applications and different scenarios,Mobile Crowd Sensing(MCS)is used to promote information sharing and gathering in this paper.A novel local content sharing framework for multi-user with different social relationships is designed.Besides,this paper also studies the application of UAV-assisted data sensing with limited-lifespan data packets,as well as the UAV path planning for diminishing data losing.Firstly,as for content sharing,this paper proposes a novel many-to-many local data sharing framework while allowing users to exchange real-time information as well as searching for messages,by designing an innovative hashtag.Under the statistical channel statement information condition,a Stable Multiple Activity matching theory is applied as a distributive solution to work excellently with the users' flexible relations.Further,under the constraint of total energy consumption within the whole system,this work provides a heuristic algorithm to jointly optimize the power allocation scheme and the system utility,while perfecting the system performance significantly.Simulation results show the effectiveness of the proposed SMA method in this paper with lower complexity.In addition,as for data sensing,this work designs the UAV trajectory for collecting the data used to reliable data analysis.In consideration of the deadline-sensitive data and the limited storage of sensors,this paper proposes a UAV-path planning model in an effort to minimize the number of expired or overflow data packets within the whole sensor system.Due to complex constraints of multi-dimension discrete variables,we relax the obscure original problem into a Min-Max-AoI-optimal path scheme.The reinforcement learning strategy is utilized to design the optimal flight path.With comparison of other two benchmarks,simulation results validate the effectiveness of the proposed method.