Vehicular fog computing (VFC) is envisioned as a promising solution to process the explosive tasks in autonomous vehicular networks. In the VFC system, task offloading is the key technique to process the computation-intensive tasks efficiently. In the task offloading, the task is transmitted to the VFC system according to the 802.11p standard and processed by the computation resources in the VFC system. The delay of task offloading, consisting of the transmission delay and computing delay, is extremely critical especially for some delay sensitive applications. Furthermore, the long-term reward of the system (i.e., jointly considers the transmission delay, computing delay, available resources and diversity of vehicles and tasks) becomes a significantly important issue for providers. Thus, in this paper, we propose an optimal task offloading scheme to maximize the long-term reward of the system where 802.11p is employed as the transmission protocol for the communications between vehicles. Specifically, a task offloading problem based on a semi Markov decision process (SMDP) is formulated. To solve this problem, we utilize an iterative algorithm based on Bellman equation to approach the desired solution. The performance of the proposed scheme has been demonstrated by extensive numerical results.

Vehicular fog and cloud computing (VFCC) system, which provides huge computing power for processing numerous computation-intensive and delay sensitive tasks, is envisioned as an enabler for intelligent connected vehicles (ICVs). Although previous works have studied the optimal offloading scheme in the VFCC system, no existing work has considered the departure of vehicles that are processing tasks, i.e., the occupied vehicles. However, vehicles leaving the system with uncompleted tasks will affect the overall performance of the system. To solve the problem, in this paper, we study the optimal offloading scheme that considers the departure of occupied vehicles. We first formulate the task offloading problem as an semi-Markov decision process (SMDP). Then we design the value iteration algorithm for the SMDP to maximize the total long-term reward of the VFCC system. Finally, the numerical results demenstrate that the proposed offloading scheme can achieve higher system reward than the greedy scheme. INDEX TERMS Vehicular fog computing, cloud computing, task offloading, semi-Markov decision process.

Multi-platooning is an important management strategy for autonomous driving technology. The backbone vehicles in a multi-platoon adopt the IEEE 802.11 distributed coordination function (DCF) mechanism to transmit vehicles’ kinematics information through inter-platoon communications, and then forward the information to the member vehicles through intra-platoon communications. In this case, each vehicle in a multi-platoon can acquire the kinematics information of other vehicles. The parameters of DCF, the hidden terminal problem and the number of neighbors may incur a long and unbalanced one-hop delay of inter-platoon communications, which would further prolong end-to-end delay of inter-platoon communications. In this case, some vehicles within a multi-platoon cannot acquire the emergency changes of other vehicles’ kinematics within a limited time duration and take prompt action accordingly to keep a multi-platoon formation. Unlike other related works, this paper proposes a swarming approach to optimize the one-hop delay of inter-platoon communications in a multi-platoon scenario. Specifically, the minimum contention window size of each backbone vehicle is adjusted to enable the one-hop delay of each backbone vehicle to get close to the minimum average one-hop delay. The simulation results indicate that, the one-hop delay of the proposed approach is reduced by 12% as compared to the DCF mechanism with the IEEE standard contention window size. Moreover, the end-to-end delay, one-hop throughput, end-to-end throughput and transmission probability have been significantly improved.

With the proliferation of the Internet-of-Things (IoT), the users’ trajectory data containing privacy information in the IoT systems are easily exposed to the adversaries in continuous location-based services (LBSs) and trajectory publication. Existing trajectory protection schemes generate dummy trajectories without considering the user mobility pattern accurately. This would cause that the adversaries can easily exclude the dummy trajectories according to the obtained geographic feature information. In this paper, the continuous location entropy and the trajectory entropy are defined based on the gravity mobility model to measure the level of trajectory protection. Then, two trajectory protection schemes are proposed based on the defined entropy metrics to protect the trajectory data in continuous LBSs and trajectory publication, respectively. Experimental results demonstrate that the proposed schemes have a higher level than the enhanced dummy-location selection (enhance-DLS) scheme and the random scheme.

Two-dimensional transition metal carbides and nitrides (MXenes) are widely applied in the fields of electrochemistry, energy storage, electromagnetism, etc., due to their extremely excellent properties, including mechanical performance, thermal stability, photothermal conversion and abundant surface properties. Usually, the surfaces of the MXenes are terminated by –OH, –F, –O or other functional groups and these functional groups of MXenes are related surface properties and reported to affect the mechanical properties of MXenes. Thus, understanding the effects of surface terminal groups on the properties of MXenes is crucial for device fabrication as well as composite synthesis using MXenes. In this paper, using molecular dynamics (MD) simulation, we study the adhesion and friction properties of Ti2C and Ti2CO2, including the indentation strength, adhesion energy and dynamics of friction. Our indentation fracture simulation reveals that there are many unbroken bonds and large residual stresses due to the oxidation of oxygen atoms on the surface of Ti2CO2. By contrast, the cracks of Ti2C keep clean at all temperatures. In addition, we calculate the elastic constants of Ti2C and Ti2CO2 by the fitting force–displacement curves with elastic plate theory and demonstrate that the elastic module of Ti2CO2 is higher. Although the temperature had a significant effect on the indentation fracture process, it hardly influences maximum adhesion. The adhesion energies of Ti2C and Ti2CO2 were calculated to be 0.3 J/m2 and 0.5 J/m2 according to Maugis–Dugdale theory. In the friction simulation, the stick-slip atomic scale phenomenon is clearly observed. The friction force and roughness (Ra) of Ti2C and Ti2CO2 at different temperatures are analyzed. Our study provides a comprehensive insight into the mechanical behavior of nanoindentation and the surface properties of oxygen functionalized MXenes, and the results are beneficial for the further design of nanodevices and composites.