IoT-based system for indoor location using bluetooth low energy

Teran, Marco; Aranda, Juan; Carrillo, Henry; Méndez, Diego; Parra, Carlos

doi:10.1109/colcomcon.2017.8088211

Cited by 68 publications

(35 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The calculation will be done using (3) and Equation (4) Where (x, y) is real position and ( , ) is predicted position. Both trained models were compared with weighted sum or weighted centroid method to measure the performance using mean of Euclidean distance (5) and cumulative distributive function of estimated distances. The cumulative distribution is to find accuracy at certain percentile.…”

Section: Regression Based Fingerprint Methodsmentioning

confidence: 99%

“…Finally, by using testing data, the performance of the model is calculated using mean of distance (5) and cumulative distribution function (CDF). Using both combined result of distance from both x and y value predicted with their labels calculated using Pythagoras rule.…”

Section: √ ∑ [( )]mentioning

confidence: 99%

“…There are many factor that affect the BLE radio propagation of the signals in indoor environments as BLE using radio signals, e.g., multipath effect, causing a random behavior in the Received Signal Strength (RSS) measurements caused by reflection [5], movement rate of human [6], and fast fading when measuring within a little time [7]. To solve these problems, fingerprinting method is needed to estimate indoor position that needs estimation algorithm to ensure accuracy of position.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Indoor Positioning System using Regression-based Fingerprint Method

Ghozali¹,

Kusuma²

2019

IJACSA

View full text Add to dashboard Cite

Indoor Positioning System has opportunity to be used in different business platform. Based on past research, optimized localization method for Bluetooth Low Energy (BLE) to predict position of person or object with high accuracy has not been found yet. Most recent research that have solve Received Signal Strength (RSS) inconsistent value is using fingerprint method. This paper proposed a deep regression machine learning using convolutional neural network (CNN) with regression-based fingerprint model to estimate real position. The model used 5 nearest fingerprints as reference RSS values with their location (x or y) label as inputs to produce output of single value position (x or y), then repeat the process to produce second value of position to create complete coordinate of estimated position. To evaluate the proposed model, a comparison between training data with validation data using Root Mean Squared Error (RMSE) is used. The comparisons are with Multilayer Perceptron model and with the weighted sum method as benchmark. The experiment Gave results of mean distance and 90th percentile distance between proposed model with the benchmark. CNN model achieved accuracies of lower than 330cm at 90th percentile with mean distance lower than 185cm. Weighted sum model achieved accuracies lower than 360cm at 90th percentile with mean distance higher than 185cm, and MLP is in between them. The result demonstrates that the proposed method outperformed the benchmark methods.

show abstract

Section: Regression Based Fingerprint Methodsmentioning

confidence: 99%

Section: √ ∑ [( )]mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Indoor Positioning System using Regression-based Fingerprint Method

Ghozali¹,

Kusuma²

2019

IJACSA

View full text Add to dashboard Cite

show abstract

“…Plane or axis is useful to identify the parts of body and combine the several overlying identifications to section for single identity of human. In Received Signal Strength (RSS) foot printing method there are two major features one is an acquisition scheme and another one is a server [9]. Radio frequency signal faces various issues such as locating the position owing to signal transmission.…”

Section: Introductionmentioning

confidence: 99%

Human Identification in Smart Home Environment

Priyadharshini¹,

Umamakeswari²

2019

IJEAT

View full text Add to dashboard Cite

People moved their life as automatic systems, like smart home. But here major issues is security, the sensitive personal information have been raised the privacy concerns such as (modifying the data, unauthorized access of data) among peoples would like to share their personal information are only utilized for their benefits, rather than being utilized for malignant purpose. Person Identification in smart home with real time is the major challenging in monitoring system. Capturing the still and live images with high efficiency is the complex task. This system provides the efficiency in higher rate with quick response. Data are lively gathered and lively compared with the data, so the process is undergoing the HAAR and the Convolutional Neural Network (CNN). Capturing and labeling the images is done with the live processing then the live video processing is taken the frames of images. So, the data are not stored for long time, and compared the still and live frames in the known frame databases it matches the features and notify the system with two categories as known and unknown.

show abstract

“…This access allows for taking action in a faster way, with better results and much more data involved [1,2]. The data that compose these kinds of systems can go from temperature [3], outdoor location [4], indoor location [5], storage stock, humidity or other industry related variables [6]. In short, any sensor that can be connected to the Internet makes part of an IoT paradigm [7].…”

Section: Introductionmentioning

confidence: 99%

Wearable-Based Human Activity Recognition Using an IoT Approach

Castro

Coral

Rodríguez

et al. 2017

JSAN

View full text Add to dashboard Cite

This paper presents a novel system based on the Internet of Things (IoT) to Human Activity Recognition (HAR) by monitoring vital signs remotely. We use machine learning algorithms to determine the activity done within four pre-established categories (lie, sit, walk and jog). Meanwhile, it is able to give feedback during and after the activity is performed, using a remote monitoring component with remote visualization and programmable alarms. This system was successfully implemented with a 95.83% success ratio.

show abstract

IoT-based system for indoor location using bluetooth low energy

Cited by 68 publications

References 12 publications

Indoor Positioning System using Regression-based Fingerprint Method

Indoor Positioning System using Regression-based Fingerprint Method

Human Identification in Smart Home Environment

Wearable-Based Human Activity Recognition Using an IoT Approach

Contact Info

Product

Resources

About