Adaptive Neuro-Fuzzy Location Indicator in Wireless Sensor Networks

Mhamdi

2021

IJEECS

Locating services have come under the spotlight in recent years in various applications. However, locating methods that use received signal strength have low accuracy due to signal fluctuations. For this purpose, we present a Wi-Fi based locating system using artificial neural network to enhance the positioning process performances. We optimized the Levenberg Marquardt algorithm to propose the better configuration of the multi-layer time-delay perception neural network. We achieved an average error of 10.3 centimeters with a grid of 0.4 meter in four tests. Yet, due to the instability of the received signal strength RSS-based locating systems present a limitation in the resolution finesse that depends on the grid size.

Section: Related Workmentioning

confidence: 99%

Real-time RSS-based positioning system using neural network algorithm

Mhamdi

2021

IJEECS

“…Baccar et al created a target map of an environment through fuzzy location indicator (FLI), and collected RSS records according to that FLI. Finally, they fed the RSS values into a neuro fuzzy classifier for indoor location identification [ 27 ]. A cost effective IPS was proposed by Yoo et al, who confirm the location including floor without the radio map and positions of Wi-Fi APs.…”

Section: Associated Work On Ipsmentioning

confidence: 99%

A Self Regulating and Crowdsourced Indoor Positioning System through Wi-Fi Fingerprinting for Multi Storey Building

Rana

Prieto

Dey

et al. 2018

Sensors

Unobtrusive indoor location systems must rely on methods that avoid the deployment of large hardware infrastructures or require information owned by network administrators. Fingerprinting methods can work under these circumstances by comparing the real-time received RSSI values of a smartphone coming from existing Wi-Fi access points with a previous database of stored values with known locations. Under the fingerprinting approach, conventional methods suffer from large indoor scenarios since the number of fingerprints grows with the localization area. To that aim, fingerprinting-based localization systems require fast machine learning algorithms that reduce the computational complexity when comparing real-time and stored values. In this paper, popular machine learning (ML) algorithms have been implemented for the classification of real time RSSI values to predict the user location and propose an intelligent indoor positioning system (I-IPS). The proposed I-IPS has been integrated with multi-agent framework for betterment of context-aware service (CAS). The obtained results have been analyzed and validated through established statistical measurements and superior performance achieved.

Anais Do XVI Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2019)

“…Neste sentido, classificadores Fuzzy Auto-Organizáveis (SOF -do inglês Self-Organising Fuzzy Logic Classifiers), propostos por [Gu and Angelov 2018] vêm sendo amplamente utilizados para geolocalização, tanto interna quanto externa [Baccar and Bouallegue 2015], [Onofre et al 2016], [Peña-Rios et al 2017], [Baccar et al 2017]. Tendo em vista que dados de RF têm uma alta incerteza associada, levando-se em consideração, que dependendo da intensidade do sinal o objeto pode se encontrar mais próximo de um ambiente que de outro, classificadores fuzzy auto organizáveis representam uma abordagem promissora na solução do problema proposto.…”

Section: Introductionunclassified

Self-Organizing Fuzzy Rule-Based Approach for Dealing with the Classification of Indoor Environments for IoT Applications

Dias

Aguiar

Hell

et al. 2019

Atualmente, grande parte dos sensores utilizados em Internet das Coisas adota tecnologia sem fio, a fim de facilitar a construção de redes de sensoriamento. Neste sentido, a classificação do tipo de ambiente no qual estes sensores estão localizados exerce um importante papel no desempenho de tais redes de sensoriamento, uma vez que pode ser utilizada na determinação de níveis mais eficientes de consumo de energia dos sensores que as compõe. Assim, neste trabalho é apresentada uma abordagem baseada em Classificadores Fuzzy Auto-organizáveis para a classificação de ambientes internos a partir de medições em tempo real do sinal de radiofrequência de uma rede de sensoriamento sem fio em um ambiente real. Os resultados experimentais apresentados mostram que a abordagem proposta obteve alto desempenho com baixo custo computacional na solução do problema apresentado.