On A Novel Adaptive UAV-Mounted Cloudlet-Aided Recommendation System for LBSNs

Location Based Social Networks (LBSNs) have recently emerged as a hot research area. However, the high mobility of LBSN users and the need to quickly provide access points in their interest zones present a unique research challenge. In order to address this challenge, in this paper, we consider the Unmanned Aerial Vehicles (UAVs) to be a viable candidate to promptly form a wireless, meshed offloading backbone to support the LBSN data sensing and relevant data computations in the LBSN cloud. In the considered network, UAV-mounted cloudlets are assumed to carry out adaptive recommendation in a distributed manner so as to reduce computing and traffic load. Furthermore, the computational complexity and communication overhead of our proposed adaptive recommendation are analyzed. The effectiveness of the proposed recommendation system in the considered LBSN is evaluated through computer-based simulations. Simulation results demonstrate that our proposal achieves much improved performance compared to conventional methods in terms of accuracy, throughput, and delay.