Mechanism analysis of non-inertial particle swarm optimization for Internet of Things in edge computing

2020 ◽  
Vol 94 ◽  
pp. 103803
Author(s):  
Lanlan Kang ◽  
Ruey-Shun Chen ◽  
Wenliang Cao ◽  
Yeh-Cheng Chen ◽  
Yu-Xi Hu
Keyword(s):  
Particle Swarm Optimization ◽  
Internet Of Things ◽  
Particle Swarm ◽  
Edge Computing ◽  
Mechanism Analysis ◽  
Inertial Particle ◽  
Swarm Optimization

scholarly journals Efficient task offloading using particle swarm optimization algorithm in edge computing for industrial internet of things

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Qian You ◽  
Bing Tang
Keyword(s):  
Particle Swarm Optimization ◽  
Energy Consumption ◽  
Internet Of Things ◽  
Particle Swarm ◽  
Edge Computing ◽  
Industrial Internet Of Things ◽  
Resource Constrained ◽  
Swarm Optimization ◽  
Industrial Internet ◽  
Task Offloading

AbstractAs a new form of computing based on the core technology of cloud computing and built on edge infrastructure, edge computing can handle computing-intensive and delay-sensitive tasks. In mobile edge computing (MEC) assisted by 5G technology, offloading computing tasks of edge devices to the edge servers in edge network can effectively reduce delay. Designing a reasonable task offloading strategy in a resource-constrained multi-user and multi-MEC system to meet users’ needs is a challenge issue. In industrial internet of things (IIoT) environment, considering the rapid increase of industrial edge devices and the heterogenous edge servers, a particle swarm optimization (PSO)-based task offloading strategy is proposed to offload tasks from resource-constrained edge devices to edge servers with energy efficiency and low delay style. A multi-objective optimization problem that considers time delay, energy consumption and task execution cost is proposed. The fitness function of the particle represents the total cost of offloading all tasks to different MEC servers. The offloading strategy based on PSO is compared with the genetic algorithm (GA) and the simulated annealing algorithm (SA) through simulation experiments. The experimental results show that the task offloading strategy based on PSO can reduce the delay of the MEC server, balance the energy consumption of the MEC server, and effectively realize the reasonable resource allocation.

scholarly journals Internet of Things Energy Efficient Cluster-Based Routing Using Hybrid Particle Swarm Optimization for Wireless Sensor Network

2021 ◽  
Author(s):  
Senthil G A ◽  
Arun Raaza ◽  
N Kumar
Keyword(s):  
Particle Swarm Optimization ◽  
Internet Of Things ◽  
Optimal Path ◽  
Particle Swarm ◽  
Base Station ◽  
Superior Performance ◽  
Wireless Sensor ◽  
Energy Usage ◽  
Swarm Optimization ◽  
Hybrid Particle Swarm Optimization

Abstract Specialized transducers in Wireless Sensor Networks (WSNs) that offer sensing services to the Internet of Things (IoT) devices have limited storage and energy resources. One of the most vital issues in WSN design is power usage, as it is nearly impossible to recharge or replace sensor nodes’ batteries. A prominent role in conserving power for energy-constrained networks is served by the clustering algorithm. It is possible to reduce network energy usage and network lifespan prolongation by proper balancing of the network load with Cluster Head (CH) election. The single-hop inter-cluster routing technique, in which there is a direct transfer from CHs to the Base Station (BS), is done by the Low Energy Adaptive Clustering Hierarchy (LEACH). However, for networks with large-regions, this technique is not viable. An optimized Orphan-LEACH (O-LEACH) has been proposed in this work to facilitate the formation of a novel process of clustering, which can result in minimized usage of energy as well as enhanced network longevity. Sufficient energy is possessed by the orphan node, which will attempt to be cover the network. The proposed work’s primary novel contribution is the O-LEACH protocol that supplies the entire network’s coverage with the least number of orphaned nodes and has extremely high connectivity rates. A hybrid optimization utilizing Simulated Annealing (SA) with Lightning Search Algorithm (LSA) (SA-LSA), and Particle Swarm Optimization (PSO) with LSA (PSO-LSA) Algorithm is proposed. These proposed techniques effectivelymanage the CH election achieving optimal path routing and minimization in energy usage, resulting in the enhanced lifespan of the WSN. The proposed technique’s superior performance, when compared with other techniques, is confirmed from the outcomes of the experimentations.

scholarly journals Efficient Computation Offloading in Multi-Tier Multi-Access Edge Computing Systems: A Particle Swarm Optimization Approach

2019 ◽  
Vol 10 (1) ◽  
pp. 203 ◽  
Author(s):  
Luan N. T. Huynh ◽  
Quoc-Viet Pham ◽  
Xuan-Qui Pham ◽  
Tri D. T. Nguyen ◽  
Md Delowar Hossain ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Particle Swarm Optimization ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Edge Computing ◽  
Computation Offloading ◽  
Mixed Integer ◽  
5G Networks ◽  
Swarm Optimization ◽  
Multi Access

In recent years, multi-access edge computing (MEC) has become a promising technology used in 5G networks based on its ability to offload computational tasks from mobile devices (MDs) to edge servers in order to address MD-specific limitations. Despite considerable research on computation offloading in 5G networks, this activity in multi-tier multi-MEC server systems continues to attract attention. Here, we investigated a two-tier computation-offloading strategy for multi-user multi-MEC servers in heterogeneous networks. For this scenario, we formulated a joint resource-allocation and computation-offloading decision strategy to minimize the total computing overhead of MDs, including completion time and energy consumption. The optimization problem was formulated as a mixed-integer nonlinear program problem of NP-hard complexity. Under complex optimization and various application constraints, we divided the original problem into two subproblems: decisions of resource allocation and computation offloading. We developed an efficient, low-complexity algorithm using particle swarm optimization capable of high-quality solutions and guaranteed convergence, with a high-level heuristic (i.e., meta-heuristic) that performed well at solving a challenging optimization problem. Simulation results indicated that the proposed algorithm significantly reduced the total computing overhead of MDs relative to several baseline methods while guaranteeing to converge to stable solutions.

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.

scholarly journals LRP Model and Algorithm of Expressway Logistics Network Planning Based on Internet of Things

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zheng Kou ◽  
Man Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Internet Of Things ◽  
Particle Swarm Optimization Algorithm ◽  
Network Planning ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Actual Distribution ◽  
Logistics Network ◽  
Internet Of Things Technology ◽  
The Internet Of Things

With the continuous improvement of the expressway logistics network, the location-routing problems (LRP) have become the obstacle to be overcome in the development of related industries. Based on the needs of modernization, in the era of the Internet of Things, classic traffic path planning algorithms can no longer meet the increasingly diverse needs, and related research results are not ideal. To reduce logistics costs and meet customer needs, this paper studies transportation route planning models and algorithms based on Internet of Things technology and particle swarm optimization. Firstly, the LRP model of expressway logistics network planning analyzes the achievement of goals, lists the assumptions, and builds the LRP model of expressway logistics network planning based on the mathematical model of path planning. Then the model is optimized and solved based on the particle swarm optimization algorithm. In order to verify the effectiveness and feasibility of the algorithm, MATLAB is used to simulate the algorithm. Finally, the LRP particle swarm optimization model of highway logistics network planning is put into the actual distribution work of a logistics company to analyze the change of distribution cost and investigate the related satisfaction. Experimental data show that the improved particle swarm optimization algorithm in this paper begins to converge in the 100th generation, the shortest running time is 57s, and the value of the objective function fluctuates slightly around 880. This shows that the model algorithm in this paper has strong search ability and stability. In the simulation experiment, the optimal objective function value of the model is 1001 yuan, which can be used to formulate the optimal distribution scheme. In the actual distribution work, the total cost of distribution before and after the application of the model was 12176.99 yuan and 9978.4 yuan, the fuel consumption cost decreased by 2097.23 yuan, and the penalty cost decreased by 85%. In the satisfaction survey, the satisfaction of all people was 9 points or above, with an average score of 9.42 points. This shows that the LRP particle swarm optimization model of expressway logistics network planning based on the Internet of Things technology can effectively save distribution costs and improve satisfaction.

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