Online Computation Offloading and Trajectory Scheduling for UAV-Enabled Wireless Powered Mobile Edge Computing

The unmanned aerial vehicle(UAV)-enabled mobile edge computing(MEC) architecture is expected to be a powerful technique to facilitate 5 G and beyond ubiquitous wireless connectivity and diverse vertical applications and services, anytime and anywhere. Wireless power transfer(WPT) is another promising technology to prolong the operation time of low-power wireless devices in the era of Internet of Things(IoT). However, the integration of WPT and UAV-enabled MEC systems is far from being well studied, especially in dynamic environments. In order to tackle this issue, this paper aims to investigate the stochastic computation offloading and trajectory scheduling for the UAV-enabled wireless powered MEC system. A UAV offers both RF wireless power transmission and computation services for IoT devices. Considering the stochastic task arrivals and random channel conditions, a long-term average energyefficiency(EE) minimization problem is formulated.Due to non-convexity and the time domain coupling of the variables in the formulated problem, a lowcomplexity online computation offloading and trajectory scheduling algorithm(OCOTSA) is proposed by exploiting Lyapunov optimization. Simulation results verify that there exists a balance between EE and the service delay, and demonstrate that the system EE performance obtained by the proposed scheme outperforms other benchmark schemes.

A Privacy-Preserving Scheme for Location-Based Services in the Internet of Vehicles

Ubiquitous information exchange is achieved among connected vehicles through the increasingly smart environment.The concept of conventional vehicular ad hoc network is gradually transformed into the Internet of vehicles(IoV).Meanwhile,more and more locationbased services(LBSs)are created to provide convenience for drivers.However,the frequently updated location information sent to the LBS server also puts user location privacy at risk.Thus,preserve user location privacy while allowing vehicles to have high-quality LBSs is a critical issue.Many solutions have been proposed in the literature to preserve location privacy.However,most of them cannot provide real-time LBS with accurate location updates.In this paper,we propose a novel location privacy-preserving scheme,which allows vehicles to send accurate real-time location information to the LBS server while preventing being tracked by attackers.In the proposed scheme,a vehicle utilizes the location information of selected shadow vehicles,whose route diverge from the requester,to generate multiple virtual trajectories to the LBS server so as to mislead attackers.Simulation results show that our proposed scheme achieves a high privacy-preserving level and outperforms other state-of-the-art schemes in terms of location entropy and tracking success ratio.