Indoor localization algorithm based on hybrid annealing particle swarm optimization

Zhao, Rentao; Shi, Yang

doi:10.1109/icaci.2018.8377479

Cited by 8 publications

(10 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If there are not enough data sets for training, it cannot converge to the best local minimum or global minimum. In [21], the authors propose an improved algorithm for hybrid annealing particle swarm optimization (HAPSO). The proposed method improved the convergence speed and accuracy of PSO based on the annealing mechanism.…”

Section: Related Workmentioning

confidence: 99%

“…The PSO, which is then performed in a limited region, is an intelligent evolutionary computation algorithm that uses intelligent particles to find the optimal location of the user. The PSO has many advantages, such as high location accuracy, few parameters, and simple implementation [21,28]. During the search, all particles within the cluster share their optimal position.…”

Section: Proposed Indoor Positioningmentioning

confidence: 99%

See 1 more Smart Citation

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

Kim

2021

Applied Sciences

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Proposed Indoor Positioningmentioning

confidence: 99%

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

Kim

2021

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…However, there are some inherent problems needed to be addressed when the original PSO algorithm is used in indoor VLP systems, such as premature convergence and low convergence accuracy. Aiming at the above problems of the original PSO algorithm, a hybrid annealing PSO algorithm was proposed in [18] to improve the average positioning accuracy and accelerate the convergence speed. In [19] and [20], trilateral localization was used to reduce the number of iterations and improve the localization accuracy by limiting the generation area.…”

Section: Introductionmentioning

confidence: 99%

“…iii. The proposed IPSO-Min-Max algorithm is compared with the existing PSO [17] and its improved versions [18]- [20] in positioning accuracy and real-time performance to demonstrate the effectiveness of our algorithm.…”

Section: Introductionmentioning

confidence: 99%

Indoor Visible Light Positioning Based on Improved Particle Swarm Optimization Method With Min-Max Algorithm

Wang¹,

Liang²,

Li³

et al. 2022

IEEE Access

View full text Add to dashboard Cite

In this paper, an improved particle swarm optimization (IPSO) algorithm is proposed to solve the problem of premature convergence and redundant particles of the original particle swarm optimization (PSO) used in visible light positioning (VLP) systems. In the proposed IPSO algorithm, an adaptive particle initialization method based on Min-Max algorithm is used to adjust the number of particles and ensure that there are always particles near the target node (TN). Moreover, a nonlinear decreasing strategy of inertia weight is designed to ensure the stability of particle velocity during the iterative process. Simulation results show that, compared with the original PSO algorithm, the averaged positioning accuracy of the proposed IPSO-Min-Max algorithm is enhanced significantly at the expense of limited time consumption. What's more, we also find that for the proposed IPSO-Min-Max algorithm the increase of particle generation spacing will reduce the positioning delay but with the penalty in positioning accuracy. Therefore, it is necessary to select an appropriate particle spacing value according to specific requirements.INDEX TERMS Visible light positioning (VLP), particle swarm optimization (PSO), received signal strength (RSS), Min-Max algorithm.

show abstract

“…This makes the PSO inefficient for a global solution; however, local search time is reduced by increasing the inertia weight [16]. On the other hand, Simulated Annealing (SA) is inspired from the natural process of annealing in metallurgy in which after a long cooling period the system always converges to global optima [17]. Hence, PSO suffers from the instability of particles during global convergence and SA jumps to the convergence of global optima.…”

mentioning

confidence: 99%

Towards Fast Response, Reduced Processing and Balanced Load in Fog-Based Data-Driven Smart Grid

et al. 2018

View full text Add to dashboard Cite

The integration of the smart grid with the cloud computing environment promises to develop an improved energy-management system for utility and consumers. New applications and services are being developed which generate huge requests to be processed in the cloud. As smart grids can dynamically be operated according to consumer requests (data), so, they can be called Data-Driven Smart Grids. Fog computing as an extension of cloud computing helps to mitigate the load on cloud data centers. This paper presents a cloud-fog-based system model to reduce Response Time (RT) and Processing Time (PT). The load of requests from end devices is processed in fog data centers. The selection of potential data centers and efficient allocation of requests on Virtual Machines (VMs) optimize the RT and PT. A New Service Broker Policy (NSBP) is proposed for the selection of a potential data center. The load-balancing algorithm, a hybrid of Particle Swarm Optimization and Simulated Annealing (PSO-SA), is proposed for the efficient allocation of requests on VMs in the potential data center. In the proposed system model, Micro-Grids (MGs) are placed near the fogs for uninterrupted and cheap power supply to clusters of residential buildings. The simulation results show the supremacy of NSBP and PSO-SA over their counterparts.

show abstract

Indoor localization algorithm based on hybrid annealing particle swarm optimization

Cited by 8 publications

References 10 publications

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

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

Indoor Visible Light Positioning Based on Improved Particle Swarm Optimization Method With Min-Max Algorithm

Towards Fast Response, Reduced Processing and Balanced Load in Fog-Based Data-Driven Smart Grid

Contact Info

Product

Resources

About