In Internet of vehicles, vehicular edge computing (VEC) as a new paradigm can effectively accomplish various tasks. Due to limited computing resources of the roadside units (RSUs), computing ability of vehicles can be a powerful supplement to computing resources. Then the task to be processed in data center can be offloaded to the vehicles by the RSUs. Due to mobility of the vehicles, the tasks will be migrated among the RSUs. How to effectively offload multiple tasks to the vehicles for processing is a challenging problem. A mobility aware multi-task migration and offloading scheme for Internet of vehicles is presented and analyzed. Considering the coupling between migration and offloading, the joint migration and offloading optimization problem is formulated. The problem is a NP-hard problem and it is very hard to be solved by the conventional methods. To tackle the difficult problem, the idea of alternating optimization and divide and conquer is introduced. The problem can be decoupled into two sub-problems: computing resource allocation problem and vehicle node selection problem. If the vehicle node selection is given, the problem can be solved based on Lagrange function. And if the allocation of computing resource is given, the problem turns into a 0–1 integer programming problem, and the linear relaxation of branch bound algorithm is introduced to solve it. Then the optimization value is obtained through continuous iteration. Simulation results show that the proposed algorithm can effectively improve system performance.

In unmanned aerial vehicle (UAV)-enabled fog computing networks, how to efficiently offload multiple tasks to the computing nodes is a challenging combinatorial optimization problem. In this paper, in order to optimize the total delay for the UAV-enabled fog computing networks, a simple scheduling algorithm and a multi-task offloading scheme based on fireworks algorithm (FWA) are proposed. First, the system model of multiple tasks offloading in UAV-enabled fog computing networks is described in detail. Then, a simple scheduling algorithm is proposed to optimize the delay of the tasks allocated to a single node. Based on the scheduling algorithm, a multi-task offloading scheme for all tasks and all computing nodes is presented. Finally, simulation results show that the performance of a proposed scheduling algorithm and offloading strategy outperforms than that of a genetic algorithm and a random algorithm. This result can provide an effective optimization for multi-task offloading in UAV-enabled fog computing networks.

Considering that the future cellular networks are more content-centric and content sharing among user equipments is more universal, device-to-device (D2D) technique attracts attention on supplying a feasible way for content delivery among proximal users without deploying additional infrastructure. Whereas some problems are still not clear such as the limitation on D2D communication for content delivery and which transmission mode (unicast or multicast) is more efficient. In this paper, the performance of content delivery is analyzed for D2D unicast and multicast, respectively. Several metrics such as the number of caching users, the number of request users, and the serving time of a content request are quantified with the consideration of user deployment and interference environment. These analyses provide an insight into D2D content delivery and deep understanding of D2D unicast and multicast on content delivery. To further distinguish appropriate conditions of employing unicast and multicast, energy efficiency is also investigated for two transmission modes. Extensive evaluations show that a better transmission mode can be selected based on the work presented in this paper. It is also verified that the efficiency of content delivery in cellular networks can be improved by this mode selection.

Vehicle-to-everything (V2X) communications in future 6G intelligent transportation systems are expected to enable various convenience applications which consume amount of computation and storage resources in vehicular networks to deliver high-quality, low-latency immersive experiences via vehicular edge computing (VEC). However, as the number of intensive tasks increases, the trade-off problem between task latency requirements and energy consumption becomes more prominent. In this paper, we study the problem of system-wide energy efficient computation offloading in speed-adjustable vehicular edge computing. We firstly consider a novel task offloading environment that considers vehicle speed adjustment to provide latency-constrained computation services for resource-limited vehicles, which fully stimulates the collaborative ability of the transportation system. We formulate the problem as a mixed-integer nonlinear programming problem to minimize the weighted energy consumption of multiple tasks. To solve this problem, we decouple it into two sub-problems, namely the task offloading decision and resource allocation problem, and the vehicle speed adjustment problem. We propose a low-complexity algorithm based on dynamic programming and a speed adjustment algorithm using a direction operator. Simulation results demonstrate the effectiveness of the proposed algorithms in optimizing the weighted energy consumption of the whole system.