MPTO-MT: A multi-period vehicular task offloading method in 5G HetNets

With the continuous development of intelligent transportation system technology, vehicle users have higher and higher requirements for low latency and high service quality of task computing. The computing offloading technology of mobile edge computing (MEC) has received extensive attention in the Internet of Vehicles (IoV) architecture. However, due to the limited resources of the MEC server, it cannot meet the task requests from multiple vehicle users simultaneously. For this reason, making correct and fast offloading decisions to provide users with a service with low latency, low energy consumption, and low cost is still a considerable challenge. Regarding the issue above, in the IoV environment where vehicle users race, this paper designs a three-layer system task offloading overhead model based on the Edge-Cloud collaboration of multiple vehicle users and multiple MEC servers. To solve the problem of minimizing the total cost of the system performing tasks, an Edge-Cloud collaborative, dynamic computation offloading method (ECDDPG) based on a deep deterministic policy gradient is designed. This method is deployed at the edge service layer to make fast offloading decisions for tasks generated by vehicle users. The simulation results show that the performance is better than the Deep Q-network (DQN) method and the Actor-Critic method regarding reward value and convergence. In the face of the change in wireless channel bandwidth and the number of vehicle users, compared with the basic method strategy, the proposed method has better performance in reducing the total computational cost, computing delay, and energy consumption. At the same time, the computational complexity of the system execution tasks is significantly reduced.

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Dynamic Offloading Method for Mobile Edge Computing of Internet of Vehicles Based on Multi-Vehicle Users and Multi-MEC Servers

With the improvements in the intelligent level of connected vehicles (CVs), travelers can enjoy services such as self-driving, self-parking and audiovisual entertainment inside the vehicle, which place extremely high demands on the computing power of onboard systems (OBSs). However, the arithmetic power of a single CV often cannot meet the diverse service demands of the in-vehicle system. As a new computing paradigm, task offloading based on vehicular edge computing has significant advantages in remedying the shortcomings of single-CV computing power and balancing the allocation of computing resources. This paper studied the computational task offloading of high-speed connected vehicles without the help of roadside edge servers in certain geographic areas. User vehicles (UVs) with insufficient computing power offload some of their computational tasks to nearby CVs with abundant resources. We explored the high-speed driving model and task classification model of CVs to refine the task offloading process. Additionally, inspired by game theory, we designed a divergent selection task offloading strategy based on an incentive mechanism (DSIM), in which we balanced the interests of both the user vehicle and service vehicles. CVs that contribute resources are rewarded to motivate more CVs to join. A DSIM algorithm based on a divergent greedy algorithm was introduced to maximize the total rewards of all volunteer vehicles while respecting the will of both the user vehicle and service vehicles. The experimental simulation results showed that, compared with several existing studies, our approach can always obtain the highest reward for service vehicles and lowest latency for user vehicles.

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Divergent Selection Task Offloading Strategy for Connected Vehicles Based on Incentive Mechanism

Agriculture-based Internet of Things (Ag-IoT) can generate near-real-time quantitative data to analyze crop growth. Unmanned Aerial Vehicle (UAV) based mobile edge computing systems is widely used in Ag-IoT due to the low cost, fast movement and ease of operation of UAVs. Nevertheless, sensors in agriculture may be under a tall and dense crop canopy or under the soil where they have difficulty harvesting a steady of energy and have too worse a channel condition to transmit more data. This paper considers a UAV-Sensor Collaboration Wireless Network (USCWN) in which each UAV carries a computation module and a wireless power transfer module to provide service and energy to sensors. Then, we propose an online resource management framework with two sub-algorithms to maximize the network throughput of USCWN. The first is the sensor fuzzy selection algorithm, which determines which UAV the sensor transmits data to, considering the different resource properties between sensors and UAVs without prior knowledge. The other is a semi-distributed dynamic resource allocation algorithm in which the sensors compete with UAV’s energy, computation, and communication resources in a stochastic game. The UAV acts as a game manager to manage sensor resources. The stochastic game is solved using Lyapunov optimization, which yields a coarse correlated equilibrium that is better than the nash equilibrium. Performance evaluation shows that our proposed framework has a higher network throughput than other agricultural works and maintains stable sensor energy.

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An Online Resource Management for Obscured Sensors in Agriculture using UAV

With the booming of intelligent connected vehicles as well as the emergence of edge computing paradigm, complex task offloading becomes a critical yet promising issue in vehicular networks to enable various real-time and scalable future intelligent systems. This article makes the first effort on proposing an end-edge-cloud cooperation architecture together with a tailored memetic algorithm for complex task offloading in heterogeneous vehicular networks. Specifically, we consider the scenario where a complex task consists of multiple subtasks, which require different amount of resources for being processed, and a task is completed only when all of its subtasks have been processed. Then, we formulate a Cooperative Complex Task Offloading (CCTO) problem by considering heterogeneous computation, communication and memory capacities of nodes in vehicular networks, as well as task dependency and mobility of vehicles, targeting at minimizing average service delay of the system. We prove that CCTO is NP-hard by constructing a polynomial time reduction from the parallel machines scheduling problem (PMSP). Further, we propose a memetic computing based algorithm named MDMA (Meme Dependency-aware Memetic Algorithm), which consists of a meme dependency based encoding solution, a mix-strategy for initialization, a dedicated offspring generation scheme, a meme recombined strategy for local search, and a task feature driven method for repairing infeasible solutions. Finally, we build a simulation model and give a comprehensive performance evaluation. The results demonstrate the superiority of MDMA on minimizing the service delay by best exploiting heterogeneous resources in vehicular networks.

A Memetic Algorithm for Cooperative Complex Task Offloading in Heterogeneous Vehicular Networks

The Mobile Edge Computing (MEC)-based task offloading in the Internet of Vehicles (IoV) scenario, which transfers computational tasks to mobile edge nodes and fixed edge nodes with available comput...

LiQingan

Papers

Task Offloading with Task Classification and Offloading Nodes Selection for MEC-Enabled IoV