Path Loss Model-Based PSO for Accurate Distance Estimation in Indoor Environments

Hashim, Huda Ali; Mohammed, Salim Latif; Gharghan, Sadik Kamel

doi:10.12720/jcm.13.12.712-722

Cited by 13 publications

(2 citation statements)

References 59 publications

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“…The path loss model using distance estimation is employed by Hashim et al 21 for the indoor environment. Initially, the conventional log‐normal shadowing model (LNSM) was used to derive the path loss model by collecting RSSI from the ZigBee network.…”

Section: Related Workmentioning

confidence: 99%

Path loss model based on exponential water cycle algorithm for wireless sensor network

Rayar,

Naik

2023

Int J Communication

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SummaryWireless sensor networks (WSNs) are frequently employed in the agriculture field to improve the quality and crop yield. The WSN might reduce the quality of the communication link because of the absorption, dispersion, and attenuation through the leaves of plants. Therefore, estimating the path loss due to signal attenuation before WSN deployment is crucial for the smooth operation of the network. In this research paper, three innovative path loss models are defined based on the MATLAB curve fitting tool: polynomial water cycle (PWC), exponential water cycle (EWC), and Gaussian water cycle (GWC) algorithm. Here, the path loss between the router node and the coordinator node is modeled on the basis of the received signal strength indicator (RSSI) and time of arrival (TOA) measurements in a sugarcane field. The correlation coefficient between the RSSI measurement and the distance must be increased to create a precise path loss model. This paper integrates the exponential, polynomial, and Gaussian functions with the water cycle algorithm (WCA) to evaluate the optimal coefficients that would lead to precise path loss models. The performance of the proposed models that determines the optimum linear fit between RSSI and distance is validated using the correction coefficient . The results show that the proposed path loss model is superior to existing path loss models. The correlation coefficient of the proposed EWC model is 0.9993, whereas the existing PE‐PSO, LNSM, and PSO‐Exponential models yield 0.98, 0.87, and 0.93, respectively. Also, the proposed models attain the best mean absolute error (MAE) of 0.2187, 0.2951, and 0.3457 dBm for EWC, PWC, and GWC algorithms, respectively.

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Section: Related Workmentioning

confidence: 99%

Path loss model based on exponential water cycle algorithm for wireless sensor network

Rayar,

Naik

2023

Int J Communication

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“…Various methods can be used to describe the relationship between the RSSI and distance, of which the log-normal shadowing model (LNSM) is one of the most popular [14]. This technique was chosen in this study due to its simplicity and low cost [15].…”

Section: Introductionmentioning

confidence: 99%

Improved Control System Based on PSO and ANN for Social Distancing for Patients With COVID-19

et al. 2022

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The World Health Organization has declared the COVID-19 pandemic, with most countries being affected by this virus both socially and economically. It thus became necessary to develop solutions to help monitor and control disease spread by controlling medical workers' movements and warning them against approaching infected individuals in isolation rooms. This paper introduces a control system that uses improved particle swarm optimization (PSO), and artificial neural network (ANN) approaches to achieve social distancing. The distance between medical workers carrying mobile nodes and the beacon node (isolation room) was determined using the ZigBee wireless protocol's received signal strength indicator (RSSI). Two path loss models were developed to determine the distance from patients with COVID-19: the first is a log-normal shading model (LNSM), and the second is a polynomial function (POL). The coefficient values of the POL model were controlled based on PSO to improve model performance. A random-nonlinear time variation controller-PSO (RNT-PSO) approach was developed to avoid the local minima of the conventional PSO. As a result, social distancing for COVID-19 can be accurately determined. The measured RSSI and the distance were used as ANN inputs, while three control signals (alarming, warning, and closing) were used as ANN outputs. The results revealed that the hybrid model between POL and RNT-PSO, called RNT-PSO-POL, improved the system's performance by reducing the mean absolute error of distance to 1.433 m, compared to 1.777 m for the LNSM. The results show that the ANN achieves robust performance in terms of mean squared error.INDEX TERMS ANN, COVID-19, control system, distance estimation, improved, PSO, RSSI Thus, distancing between people has become crucial for

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Air pollution forecasting based on wireless communications: review

Fadhil,

Gharghan,

Saeed

2023

Environ Monit Assess

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Path Loss Model-Based PSO for Accurate Distance Estimation in Indoor Environments

Cited by 13 publications

References 59 publications

Path loss model based on exponential water cycle algorithm for wireless sensor network

Path loss model based on exponential water cycle algorithm for wireless sensor network

Improved Control System Based on PSO and ANN for Social Distancing for Patients With COVID-19

Air pollution forecasting based on wireless communications: review

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