Particle Swarm Optimization towards Data Collection in the Internet of Things with a Mobile Sink

2020 ◽  
Author(s):  
Chunshen Hong
Keyword(s):  
Particle Swarm Optimization ◽  
Data Collection ◽  
Internet Of Things ◽  
Particle Swarm ◽  
Mobile Sink ◽  
The Internet ◽  
Swarm Optimization ◽  
The Internet Of Things

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.

scholarly journals An Unconventional Routing Method using Particle Swarm Optimization for the Internet of Things

2020 ◽  
Vol 9 (6) ◽  
pp. 267-271
Keyword(s):  
Particle Swarm Optimization ◽  
Internet Of Things ◽  
Design Principle ◽  
Fault Tolerant ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Novel Approach ◽  
Network Operations ◽  
Prime Objective ◽  
The Internet Of Things

The study introduces novel analytical modeling of a multipath fault-tolerant routing approach where the design principle is formulated based on a bio-inspired optimization modeling of swarm optimization principles. The prime objective of this novel approach is to deal with network failures of Internet-of-Things (IoT) in a faster manner and recover the network operations as early as possible without compromising much energy. This way, the network becomes more reliable and sustainable even if any events occur that make the senor node functionally disabled and even if any types of path failures take place, regardless of energy consumption factors in IoT routing scenarios. However, the approach also capable of handling energy problems during the IoT routing scenario to a significant extent. Further, the outcome of the study shows that the fault-tolerance routing approach based on unconventional particle swarm optimization (FT-PSO) attains better results as compared to the existing baselines.

scholarly journals Virtual Angle Boundary-Aware Particle Swarm Optimization to Maximize the Coverage of Directional Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2868
Author(s):  
Gong Cheng ◽  
Huangfu Wei
Keyword(s):  
Particle Swarm Optimization ◽  
Sensor Networks ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
The Internet ◽  
Directional Sensor Networks ◽  
Swarm Optimization ◽  
Coverage Optimization ◽  
Boundary Constraint ◽  
Directional Sensor

With the transition of the mobile communication networks, the network goal of the Internet of everything further promotes the development of the Internet of Things (IoT) and Wireless Sensor Networks (WSNs). Since the directional sensor has the performance advantage of long-term regional monitoring, how to realize coverage optimization of Directional Sensor Networks (DSNs) becomes more important. The coverage optimization of DSNs is usually solved for one of the variables such as sensor azimuth, sensing radius, and time schedule. To reduce the computational complexity, we propose an optimization coverage scheme with a boundary constraint of eliminating redundancy for DSNs. Combined with Particle Swarm Optimization (PSO) algorithm, a Virtual Angle Boundary-aware Particle Swarm Optimization (VAB-PSO) is designed to reduce the computational burden of optimization problems effectively. The VAB-PSO algorithm generates the boundary constraint position between the sensors according to the relationship among the angles of different sensors, thus obtaining the boundary of particle search and restricting the search space of the algorithm. Meanwhile, different particles search in complementary space to improve the overall efficiency. Experimental results show that the proposed algorithm with a boundary constraint can effectively improve the coverage and convergence speed of the algorithm.

Data collection of safety accidents in sports training of athletes with Internet of Things technology

2021 ◽  
pp. 1-7
Author(s):  
Zhiping Wang ◽  
Xinxin Zheng ◽  
Zhichen Yang
Keyword(s):  
Information Technology ◽  
Data Collection ◽  
Internet Of Things ◽  
The Internet ◽  
Sports Training ◽  
Accident Data ◽  
And Performance ◽  
Control Technologies ◽  
Internet Of Things Technology ◽  
The Internet Of Things

The Internet of Things (IoT) technology is an information technology developed in recent years with the development of electronic sensors, intelligence, network transmission and control technologies. This is the third revolution in the development of information technology. This article aims to study the algorithm of the Internet of Things technology, through the investigation of the hazards of athletes’ sports training, scientifically and rationally use the Internet of Things technology to collect data on safety accidents in athletes’ sports training, thereby reducing the risk of athletes’ sports training and making athletes better. In this article, the methods of literature research, analysis and condensing, questionnaire survey, theory and experiment combination, etc., investigate the safety accident data collection of the Internet of Things technology in athletes’ sports training, and provide certain theories and methods for future in-depth research practice basis. The experimental results in this article show that 82% of athletes who are surveyed under the Internet of Things technology will have partial injuries during training, reducing the risk of safety accidents in athletes’ sports training, and better enabling Chinese athletes to achieve a consistent level of competition and performance through a virtuous cycle of development.

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.

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