A ZigBee-based framework for approximating sensor range and bearing

Hevrdejs, Kyle; Knoll, Jacob; Miah, Suruz

doi:10.1109/ccece.2017.7946789

Cited by 8 publications

(2 citation statements)

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“…In a wireless sensor network, the difference in the strength of the transmitted and received signals is expressed as the loss factor. 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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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 current indoor positioning solutions include WiFi [ 1 ], Bluetooth [ 2 ], ultra-wide band (UWB) [ 3 , 4 , 5 ], ZigBee [ 6 ], inertial measurement unit (IMU) [ 7 , 8 , 9 ], and so on. The inertial navigation system is a self-contained system that calculates the position, velocity, and attitude of a vehicle from the outputs of inertial measurement units (IMUs).…”

Section: Introductionmentioning

confidence: 99%

A Federated Derivative Cubature Kalman Filter for IMU-UWB Indoor Positioning

Tang

2020

Sensors

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The inertial measurement unit and ultra-wide band signal (IMU-UWB) combined indoor positioning system has a nonlinear state equation and a linear measurement equation. In order to improve the computational efficiency and the localization performance in terms of the estimation accuracy, the federated derivative cubature Kalman filtering (FDCKF) method is proposed by combining the traditional Kalman filtering and the cubature Kalman filtering. By implementing the proposed FDCKF method, the observations of the UWB and the IMU can be effectively fused; particularly, the IMU can be continuously calibrated by UWB so that it does not generate cumulative errors. Finally, the effectiveness of the proposed algorithm is demonstrated through numerical simulations, in which FDCKF was compared with the federated cubature Kalman filter (FCKF) and the federated unscented Kalman filter (FUKF), respectively.

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