Collaborative Vehicular Edge Computing (CVEC) employs an edge server on the roadside unit and volunteer vehicles as processors to provide vehicle-to-infrastructure (V2I) offloading and vehicle-to-vehicle (V2V) offloading for requester vehicles in computation offloading. Since the processors have heterogeneous computing capabilities, we study a hybrid task scheduling problem to minimize the total service cost of all requester vehicles subject to feasible constraints. More specifically, the service cost of a requester vehicle is formulated as the product of the priority value and weighted sum of the delay and energy consumption of processing the task. We derive delay constraints of the V2V and V2I offloading according to the mobility of the vehicles. Furthermore, we present a deep reinforcement learning approach to solve the above problem in the dynamic vehicular environment. Particularly, we adopt the state-of-the-art Rainbow algorithm to accelerate the convergence and achieve better performance. Finally, we provide numerical results to demonstrate that our approach outperforms the baseline approaches in achieving the faster and more accurate learning.

Wireless body area network (WBAN) suffers secure challenges, especially the eavesdropping attack, due to constraint resources. In this article, deep reinforcement learning (DRL) and mobile edge computing (MEC) technology are adopted to formulate a DRL-MEC-based jamming-aided anti-eavesdropping (DMEC-JAE) scheme to resist the eavesdropping attack without considering the channel state information. In this scheme, a MEC sensor is chosen to send artificial jamming signals to improve the secrecy rate of the system. Power control technique is utilized to optimize the transmission power of both the source sensor and the MEC sensor to save energy. The remaining energy of the MEC sensor is concerned to ensure routine data transmission and jamming signal transmission. Additionally, the DMEC-JAE scheme integrates with transfer learning for a higher learning rate. The performance bounds of the scheme concerning the secrecy rate, energy consumption, and the utility are evaluated. Simulation results show that the DMEC-JAE scheme can approach the performance bounds with high learning speed, which outperforms the benchmark schemes.

Abstract Due to a large amount of time‐series data (e.g. precipitation, temperature, humidity) in the air, reliable and fast data collection is a challenging issue. Using an unmanned aerial vehicle (UAV) as a data collector to collect time‐series data is an effective method. However, due to the limited storage capacity of the UAV, the UAV must access the sensor multiple times to collect time‐series data from the deployed sensors and dump the data to the data centre, which leads to significantly increased time and cost for data collection mission. To address this challenge, an efficient time‐series data collection framework is proposed based on the Internet of UAVs. In this system, a min‐maximum data processing strategy is adopted based on data value to store the collected time‐series data. Specifically, efficient data compression storage is achieved with minimal loss of precision by extracting the dominant dataset with maximum value. Furthermore, an efficient affine transformation method is proposed to improve the efficiency of the system. Extensive case studies on some real‐world datasets demonstrate that the proposed framework can achieve efficient data management and compressed storage.