scholarly journals WiFi Positioning in 3GPP Indoor Office with Modified Particle Swarm Optimization

2021 ◽  
Vol 11 (20) ◽  
pp. 9522
Author(s):  
Sung Hyun Oh ◽  
Jeong Gon Kim
Keyword(s):  
Big Data ◽  
Particle Swarm Optimization ◽  
Industrial Revolution ◽  
Particle Swarm ◽  
Outdoor Environment ◽  
Convergence Time ◽  
Indoor Environments ◽  
Search Region ◽  
Swarm Optimization ◽  
Limited Region

With the start of the Fourth Industrial Revolution, Internet of Things (IoT), artificial intelligence (AI), and big data technologies are attracting global attention. AI can achieve fast computational speed, and big data makes it possible to store and use vast amounts of data. In addition, smartphones, which are IoT devices, are owned by most people. Based on these advantages, the above three technologies can be combined and effectively applied to navigation technology. In the case of an outdoor environment, global positioning system (GPS) technology has been developed to enable relatively accurate positioning of the user. However, due to the problem of radio wave loss because of many obstacles and walls, there are obvious limitations in applying GPS to indoor environments. Hence, we propose a method to increase the accuracy of user positioning in indoor environments using wireless-fidelity (Wi-Fi). The core technology of the proposed method is to limit the initial search region of the particle swarm optimization (PSO), an intelligent particle algorithm; doing so increases the probability that particles converge to the global optimum and shortens the convergence time of the algorithm. For this reason, the proposed method can achieve fast processing time and high accuracy. To limit the initial search region of the PSO, we first build an received signal strength indicator (RSSI) database for each sample point (SP) using a fingerprinting scheme. Then, a limited region is established through a fuzzy matching algorithm. Finally, the particles are randomly distributed within a limited region, and then the user’s location is positioned through a PSO. Simulation results confirm that the method proposed in this paper achieves the highest positioning accuracy, with an error of about 1 m when the SP interval is 3 m in an indoor environment.

scholarly journals Research on Big Data Digging of Hot Topics about Recycled Water Use on Micro-Blog Based on Particle Swarm Optimization

2018 ◽  
Vol 10 (7) ◽  
pp. 2488 ◽  
Author(s):  
Hanliang Fu ◽  
Zhaoxing Li ◽  
Zhijian Liu ◽  
Zelin Wang
Keyword(s):  
Big Data ◽  
Particle Swarm Optimization ◽  
Key Words ◽  
Water Use ◽  
Particle Swarm ◽  
Recycled Water ◽  
Swarm Optimization ◽  
The Public ◽  
Key Factor ◽  
Important Significance

The public’s acceptance level of recycled water use is a key factor that affects the popularization of this technology; therefore, it is critical to know the public’s attitude in order to make guiding policies effectively and scientifically. To examine the major focuses and hot topics among the public about recycled water use, one of the major platforms for social opinion in China, the micro blog, is used as a source to obtain data related to the topic. Through the “follow-be followed” and “forward-dialogue” behaviors, a network of discussion of recycled water use among micro-blog users has been constructed. Improved particle swarm optimization has been used to allow deep digging for key words. Ultimately, key words about the topic of have been clustered into three categories, namely, the popularization status of recycled water use, the main application, and the public’s attitude. The conclusion accurately describes the concerns of Chinese citizens regarding recycled water use, and has important significance for the popularization of this technology.

Particle Swarm Optimization based dictionary learning for remote sensing big data

2015 ◽  
Vol 79 ◽  
pp. 43-50 ◽  
Author(s):  
Lizhe Wang ◽  
Hao Geng ◽  
Peng Liu ◽  
Ke Lu ◽  
Joanna Kolodziej ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Big Data ◽  
Particle Swarm Optimization ◽  
Dictionary Learning ◽  
Particle Swarm ◽  
Swarm Optimization

scholarly journals An Enhanced Framework To Secure Big Data Based on Hybrid Machine Learning Technique:ANN-PSO

2021 ◽  
Vol 9 (6) ◽  
pp. 76-84
Author(s):  
Salim Raza Qureshi
Keyword(s):  
Big Data ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Smart Devices ◽  
Data Sets ◽  
Academic Impact ◽  
Swarm Optimization ◽  
Data Source

With the advancement of smart devices and cloud computing, more and more public health data can be collected from various sources and analyzed in unprecedented ways. The enormous social and academic impact of this development has led to a global buzz for bigdata. Moreover, due to the massive data source, the security of big data in the cloud is becoming an important issue. In these days, various issues have arisen in the field of big data security, such as Infrastructure security, data confidentiality, data management and data integrity. In this paper, we propose a novel technique based on Artificial Neural Network-and Particle Swarm Optimization Algorithm (ANNPSO) for enabling a highly secured framework. The ANN-PSO method was created to predict health status from a database and its functions were selected from these data sets. The particle swarm optimization algorithm matches the ANN for better results by reducing errors. The results show the potential of the ANNPSO-based methodology for satisfactory health prediction results. This proposed approach will be tested using large medical data in a Hadoop environment. The proposed work will be carried out in the JAVA work phase.

scholarly journals Convergence Time Analysis of Particle Swarm Optimization Based on Particle Interaction

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
Chao-Hong Chen ◽  
Ying-ping Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Numerical Experiments ◽  
Particle Interaction ◽  
Particle Swarm ◽  
Convergence Time ◽  
Statistical Interpretation ◽  
Time Analysis ◽  
Swarm Optimization ◽  
The Social ◽  
Theoretical Results

We analyze the convergence time of particle swarm optimization (PSO) on the facet of particle interaction. We firstly introduce a statistical interpretation of social-only PSO in order to capture the essence of particle interaction, which is one of the key mechanisms of PSO. We then use the statistical model to obtain theoretical results on the convergence time. Since the theoretical analysis is conducted on the social-only model of PSO, instead of on common models in practice, to verify the validity of our results, numerical experiments are executed on benchmark functions with a regular PSO program.

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