A Comparison of RSSI Filtering Techniques for Range-based Localization

Koledoye, Moses A.; Martini, Daniele De; Rigoni, Simone; Facchinetti, Tullio

doi:10.1109/etfa.2018.8502556

Cited by 19 publications

(6 citation statements)

References 19 publications

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“…It is also applicable to any crane data logging device. It is also a widely used method to filter data coming from localization devices to avoid holes or jumps in the data (Ahn and Ko, 2009; Koledoye et al. , 2018).…”

Section: Methodsmentioning

confidence: 99%

Identifying tower crane activities with data: the case of the concrete pouring

Danel

Lafhaj

Puppala

et al. 2022

ECAM

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PurposeThe crane plays an essential role in modern construction sites as it supports numerous operations and activities on-site. Additionally, the crane produces a big amount of data that, if analyzed, could significantly affect productivity, progress monitoring and decision-making in construction projects. This paper aims to show the usability of crane data in tracking the progress of activities on-site.Design/methodology/approachThis paper presents a pattern-based recognition method to detect concrete pouring activities on any concrete-based construction sites. A case study is presented to assess the methodology with a real-life example.FindingsThe analysis of the data helped build a theoretical pattern for concrete pouring activities and detect the different phases and progress of these activities. Accordingly, the data become useable to track progress and identify problems in concrete pouring activities.Research limitations/implicationsThe paper presents an example for construction practitioners and researcher about a practical and easy way to analyze the big data that comes from cranes and how it is used in tracking projects' progress. The current study focuses only on concrete pouring activities; future studies can include other types of activities and can utilize the data with other building methods to improve construction productivity.Practical implicationsThe proposed approach is supposed to be simultaneously efficient in terms of concrete pouring detection as well as cost-effective. Construction practitioners could track concrete activities using an already-embedded monitoring device.Originality/valueWhile several studies in the literature targeted the optimization of crane operations and of mitigating hazards through automation and sensing, the opportunity of using cranes as progress trackers is yet to be fully exploited.

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Section: Methodsmentioning

confidence: 99%

Identifying tower crane activities with data: the case of the concrete pouring

Danel

Lafhaj

Puppala

et al. 2022

ECAM

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“…The RSSI filtering is used to increase the accuracy and reliability of wireless communication systems, particularly in range-based localization [28]. RSSI is typically measured in decibels (dB) and indicates the power level of the signal as it arrives at the receiver.…”

Section: Received Signal Strength Indicator Filtermentioning

confidence: 99%

“…The filter operates by summing the RSSI values over a specific time and dividing them by the number of values in the period. When the current RSSI values are added, the oldest value is subtracted from the total, and the new average is computed [28]. CMA has the advantage of significantly accounting for past data by accounting for the most recent data point.…”

Section: Cumulative Moving Averagementioning

confidence: 99%

Improving ventilation classification in under-actuated zones: a k-nearest neighbor and data preprocessing approach

Yaddarabullah,

Rahman,

Saad

2024

IJEECS

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This study investigates the use of k-nearest neighbors (k-NN) for classifying occupant positions in under-actuated zones, aiming to enhance ventilation control. The focus is on evaluating different data preprocessing techniques, particularly cumulative moving average (CMA), Kalman filtering (KF), and their combination, to boost the k-NN model's reliability and accuracy. The research uses received signal strength indicator (RSSI) data in a controlled setting. The methodology involves dividing the dataset into training and testing subsets and using root mean squared error (RMSE) to determine the best k value for model validation. The study performs a comparative analysis of the k-NN model's performance with both original and preprocessed RSSI data, focusing on metrics such as accuracy, precision, recall, F1-score, and RMSE. The findings emphasize the significant impact of the combined CMA-KF preprocessing technique in improving the model's accuracy and reliability. Specifically, this approach achieved an accuracy of 98.58%. The RMSE values are particularly noteworthy, exhibiting a perfect fit (RMSE of 0) for training data and a remarkably low RMSE of 0.119 for testing data, confirming the model's high accuracy and predictive capability.

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“…Thus, the mean filter is widely recommended because it has similar accuracy and anti-interference performance [11]. Considering their relatively small computational overheads and the fact they can be used in a real-time context, the rolling mean filter, exponential moving mean filer, and moving median filter have been discussed [12]. However, these filters are typically intended to mitigate these influences by smoothing and could suffer from left-skewed distributions caused by RSSI multipath propagation [15].…”

Section: Rssi Filtering Technologiesmentioning

confidence: 99%

“…Still, they may not effectively deal with the ever-changing dynamics of the indoor environment [ 10 ] and have left-skewed distributions [ 11 ]. The mean filter [ 12 ] is widely accepted because it has similar accuracy and anti-interference performance, but has less burden in filtering computation [ 13 , 14 ]. Besides smoothing, RSSI screening is another effective filtering method, e.g., by selecting the max N RSSIs (N = 13 is optimal) [ 13 ], and the least variance RSSIs over time [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

An RSSI Classification and Tracing Algorithm to Improve Trilateration-Based Positioning

Shi

Liu

et al. 2020

Sensors

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Received signal strength indicator (RSSI)-based positioning is suitable for large-scale applications due to its advantages of low cost and high accuracy. However, it suffers from low stability because RSSI is easily blocked and easily interfered with by objects and environmental effects. Therefore, this paper proposed a tri-partition RSSI classification and its tracing algorithm as an RSSI filter. The proposed filter shows an available feature, where small test RSSI samples gain a low deviation of less than 1 dBm from a large RSSI sample collected about 10 min, and the sub-classification RSSIs conform to normal distribution when the minimum sample count is greater than 20. The proposed filter also offers several advantages compared to the mean filter, including lower variance range with an overall range of around 1 dBm, 25.9% decreased sample variance, and 65% probability of mitigating RSSI left-skewness. We experimentally confirmed the proposed filter worked in the path-loss exponent fitting and location computing, and a 4.45-fold improvement in positioning stability based on the sample standard variance, and positioning accuracy improved by 20.5% with an overall error of less than 1.46 m.

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A Comparison of RSSI Filtering Techniques for Range-based Localization

Cited by 19 publications

References 19 publications

Identifying tower crane activities with data: the case of the concrete pouring

Identifying tower crane activities with data: the case of the concrete pouring

Improving ventilation classification in under-actuated zones: a k-nearest neighbor and data preprocessing approach

An RSSI Classification and Tracing Algorithm to Improve Trilateration-Based Positioning

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