Advanced Smoothing Approach of RSSI and LQI for Indoor Localization System

Halder, Sharly Joana; Giri, Paritosh; Kim, Wooju

doi:10.1155/2015/195297

Cited by 23 publications

(5 citation statements)

References 16 publications

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“…Each beacon node includes Zigbee wireless protocols (XBee Series 2) powered from the main source, eliminating concerns about power consumption. The beacon nodes N1, N2, and N3 are fixed at the coordinates (0, 0), (0, 30), and (40,15), respectively, to allow the collection of different RSSI values for ANN training, testing, and validation as shown in Figure 1. The beacon nodes are fixed on a surface 1.5 m above the ground to avoid the effect of the boundary of the first Fresnel Zone, h O (h O = 0.5 √ λd).…”

Section: Wireless Sensor Network For Elderly Fall Detectionmentioning

confidence: 99%

Accurate Fall Detection and Localization for Elderly People Based on Neural Network and Energy-Efficient Wireless Sensor Network

et al. 2018

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Falls are the main source of injury for elderly patients with epilepsy and Parkinson's disease. Elderly people who carry battery powered health monitoring systems can move unhindered from one place to another according to their activities, thus improving their quality of life. This paper aims to detect when an elderly individual falls and to provide accurate location of the incident while the individual is moving in indoor environments such as in houses, medical health care centers, and hospitals. Fall detection is accurately determined based on a proposed sensor-based fall detection algorithm, whereas the localization of the elderly person is determined based on an artificial neural network (ANN). In addition, the power consumption of the fall detection system (FDS) is minimized based on a data-driven algorithm. Results show that an elderly fall can be detected with accuracy levels of 100% and 92.5% for line-of-sight (LOS) and non-line-of-sight (NLOS) environments, respectively. In addition, elderly indoor localization error is improved with a mean absolute error of 0.0094 and 0.0454 m for LOS and NLOS, respectively, after the application of the ANN optimization technique. Moreover, the battery life of the FDS is improved relative to conventional implementation due to reduced computational effort. The proposed FDS outperforms existing systems in terms of fall detection accuracy, localization errors, and power consumption.Energies 2018, 11, 2866 2 of 32 to detect emergency conditions and enable caregivers to respond efficiently. A fall is one of the key factors that can lead to injuries and decrease quality of life, at times resulting in the death of elderly persons. People's rate of falling increases with their age. Falls occur frequently in medical health care centers, hospitals, or houses, with approximately 30% of falls causing injury. Falls in hospitals occur in the rooms of the patients (84%) and during transfer from one place to another (19%). Furthermore, the majority of falls occur in areas adjacent to chairs and beds [2]. Most people who experience falls need special care in a nursing home or hospital, thereby restricting their life activities. The hazard issues of fall or slight fall, especially of the elderly, can be aggravated by chronic diseases, such as osteoporosis, delirium, and dementia [3]. The degree of danger from a fall for aging persons is frequently decided by the location of the fall, time of fall detection, duration and time of transfer and rescue services. Therefore, automatic detection of elderly people's falls along with the locations of the incident is important so that medical rescue staff can be dispatched immediately and so that the family of the elderly can be informed about the incident through a specific wireless network or mobile telephone.The development of microelectromechanical technologies allows the integration of different sensors, and a wireless network is commonly used. Wireless sensor networks (WSNs) comprise a number of tiny and small sensor nodes which are deployed ...

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Section: Wireless Sensor Network For Elderly Fall Detectionmentioning

confidence: 99%

Accurate Fall Detection and Localization for Elderly People Based on Neural Network and Energy-Efficient Wireless Sensor Network

et al. 2018

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“…where RSSI is the mean RSSI; m is the number of measurements and RSSI is the RSSI of the ith beacon. 1) Feedback filter Feedback filter was described in [8] and [9] to eliminate the large differences in the measured value of RSSI. Its principle is to add a weighted value α to the RSSI to correct RSSI values.…”

Section: B Rssi Processingmentioning

confidence: 99%

Optimized indoor positioning for static mode smart devices using BLE

Nguyen

Johnson

Nguyen

et al. 2017

2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)

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Bluetooth Low Energy (BLE) technology and BLE-based devices such as iBeacons have become popular recently. In this work, an optimized indoor positioning approach using BLE for detecting a smart device's location in an indoor environment is proposed. The first stage of the proposed approach is a calibration stage for initialization. The Received Signal Strength Indicator (RSSI) is collected and pre-processed for a stable outcome, in the second stage. Then the distance is estimated by using the processed RSSI and calibrated factors in the third stage. The final stage is the position estimation using the outputs from the previous steps. The positioning technique, which is an improved Least Square estimation is evaluated against the other well-known techniques such as, Trilateration-Centroid, classic Least Square Estimation in estimating the user's location in the 2D plane. Experimental results show that our proposed approach has promising results by achieving an accuracy of positioning within 0.2 to 0.35m.

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“…For simplicity, we assume −75 dBm gives 1% reliability and −95 dBm provides 0.5% of reliability for getting optimal values of α 1 and α 2 from the proposed (9). For further details see [26]. When R is <R target we can calculate α 2 , similarly α 1 can be obtained when R is >R target from (9)…”

Section: Parameters Selection and Optimisationmentioning

confidence: 99%