Slow-movement particle swarm optimization algorithms for scheduling security-critical tasks in resource-limited mobile edge computing

2020 ◽  
Vol 112 ◽  
pp. 148-161 ◽  
Author(s):  
Yi Zhang ◽  
Yu Liu ◽  
Junlong Zhou ◽  
Jin Sun ◽  
Keqin Li
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Slow Movement ◽  
Swarm Optimization ◽  
Resource Limited

scholarly journals Particle Swarm Optimization-Based Secure Computation Efficiency Maximization in a Power Beacon-Assisted Wireless-Powered Mobile Edge Computing NOMA System

Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5540 ◽  
Author(s):  
Carla E. Garcia ◽  
Mario R. Camana ◽  
Insoo Koo
Keyword(s):  
Particle Swarm Optimization ◽  
Multiple Access ◽  
Particle Swarm ◽  
Secure Computation ◽  
Edge Computing ◽  
Computation Offloading ◽  
Processing Unit ◽  
Mobile Edge Computing ◽  
Swarm Optimization ◽  
Computation Efficiency

In this paper, we aim to provide reliable user connectivity and enhanced security for computation task offloading. Physical layer security is studied in a wireless-powered non-orthogonal multiple access (NOMA) mobile edge computing (MEC) system with a nonlinear energy-harvesting (EH) user and a power beacon (PB) in the presence of an eavesdropper. To further provide a friendly environment resource allocation design, wireless power transfer (WPT) is applied. The secure computation efficiency (SCE) problem is solved by jointly optimizing the transmission power, the time allocations for energy transfer, the computation time, and the central processing unit (CPU) frequency in the NOMA-enabled MEC system. The problem is non-convex and challenging to solve because of the complexity of the objective function in meeting constraints that ensure the required quality of service, such as the minimum value of computed bits, limitations on total energy consumed by users, maximum CPU frequency, and minimum harvested energy and computation offloading times. Therefore, in this paper, a low-complexity particle swarm optimization (PSO)-based algorithm is proposed to solve this optimization problem. For comparison purposes, time division multiple access and fully offloading baseline schemes are investigated. Finally, simulation results demonstrate the superiority of the proposed approach over baseline schemes.

Research on Task-Offloading Decision Mechanism in Mobile Edge Computing based Internet of Vehicle

2021 ◽  
Author(s):  
Jun Cheng ◽  
Dejun Guan
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Edge Computing ◽  
Computation Offloading ◽  
Mobile Edge Computing ◽  
Cloud Infrastructure ◽  
Swarm Optimization ◽  
Decision Mechanism ◽  
The Impact ◽  
Task Offloading

Abstract As a technology integrated with Internet of Things (IoT), mobile edge computing (MEC) can provide real-time and low latency services to the underlying network, and improve the storage and computation ability of the networks instead of central cloud infrastructure. In Mobile Edge Computing based Internet of Vehicle(MEC-IoV), the vehicle users can deliver their tasks to the associated MEC servers Based on offloading policy, which improves the resource utilization and computation performance greatly. However, how to evaluate the impact of uncertain interconnection between the vehicle users and MEC servers on offloading decision-making and avoid serious degradation of the offloading efficiency are important problems to be solved. In this paper, a task-offloading decision mechanism with particle swarm optimization for IoV-based edge computing is proposed. First, a mathematical model to calculate the computation offloading cost for cloud-edge computing system is defined. Then, the particle swarm optimization (PSO) is applied to convert the offloading of task into the process and obtain the optimal offloading strategy. Furthermore, to avoid falling into local optimization, the inertia weight factor is designed to change adaptively with the value of the objective function. The experimental results show that the proposed offloading strategy can effectively reduce the energy consumption of terminal devices while guarantee the service quality of users.

scholarly journals Research on task-offloading decision mechanism in mobile edge computing-based Internet of Vehicle

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Jun Cheng ◽  
Dejun Guan
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Edge Computing ◽  
Computation Offloading ◽  
Mobile Edge Computing ◽  
Cloud Infrastructure ◽  
Swarm Optimization ◽  
Decision Mechanism ◽  
The Impact ◽  
Task Offloading

AbstractAs a technology integrated with Internet of things, mobile edge computing (MEC) can provide real-time and low-latency services to the underlying network and improve the storage and computation ability of the networks instead of central cloud infrastructure. In mobile edge computing-based Internet of Vehicle (MEC-IoV), the vehicle users can deliver their tasks to the associated MEC servers based on offloading policy, which improves the resource utilization and computation performance greatly. However, how to evaluate the impact of uncertain interconnection between the vehicle users and MEC servers on offloading decision-making and avoid serious degradation of the offloading efficiency are important problems to be solved. In this paper, a task-offloading decision mechanism with particle swarm optimization for MEC-IoV is proposed. First, a mathematical model to calculate the computation offloading cost for cloud-edge computing system is defined. Then, the particle swarm optimization is applied to convert the offloading of task into the process and obtain the optimal offloading strategy. Furthermore, to avoid falling into local optimization, the inertia weight factor is designed to change adaptively with the value of the objective function. The experimental results show that the proposed offloading strategy can effectively reduce the energy consumption of terminal devices while guarantee the service quality of users.

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