Development of A Wi-Fi Based Indoor Location System Using Artificial Intelligence Techniques (original) (raw)
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Indoor localization techniques using wireless network and artificial neural network
2017
This research focuses on improving indoor localization using wireless network and artificial neural network (ANN). This involves strategic study on wireless signal behavior and propagation inside buildings, suitable propagation model to simulate indoor propagation and evaluations on different localization methods such as distance based, direction based, time based and signature based. It has been identified that indoor signal propagation impairments are severe, non-linear and custom to a specific indoor location. To accommodate these impairments, an ANN is proposed to provide a viable solution for indoor location prediction as it learns the location specific parameters during training, and then performs positioning based on the trained data, while being robust to severe and non-linear propagation effects. The versatility of ANN allows different setup and optimization possibilities to affect location prediction capabilities. This research identified the best feedforward backpropagati...
Indoor Positioning System Using Artificial Neural Network
Journal of Computer Science, 2010
Problem statement: Location knowledge in indoor environment using Indoor Positioning Systems (IPS) has become very useful and popular in recent years. A number of Location Based Services (LBS) have been developed, which are based on IPS, these LBS include asset tracking, inventory management and security based applications. Many next-generation LBS applications such as social networking, local search, advertising and geo-tagging are expected to be used in urban and indoor environments where GNSS either underperforms in terms of fix times or accuracy, or fails altogether. To develop an IPS based on Wi-Fi Received Signal Strength (RSS) using Artificial Neural Networks (ANN), which should use already available Wi-Fi infrastructure in a heterogeneous environment. Approach: This study discussed the use of ANN for IPS using RSS in an indoor wireless facility which has varying human activity, material of walls and type of Wireless Access Points (WAP), hence simulating a heterogeneous environment. The proposed system used backpropogation method with 4 input neurons, 2 output neurons and 4 hidden layers. The model was trained with three different types of training data. The accuracy assessment for each training data was performed by computing the distance error and average distance error. Results: The results of the experiments showed that using ANN with the proposed method of collecting training data, maximum accuracy of 0.7 m can be achieved, with 30% of the distance error less than 1 m and 60% of the distance error within the range of 1-2 m. Whereas maximum accuracy of 1.01 can be achieved with the commonly used method of collecting training data. The proposed model also showed 67% more accuracy as compared to a probabilistic model. Conclusion: The results indicated that ANN based IPS can provide accuracy and precision which is quite adequate for the development of indoor LBS while using the already available Wi-Fi infrastructure, also the proposed method for collecting the training data can help in addressing the noise and interference, which are one of the major factors affecting the accuracy of IPS.
Location determination of mobile devices for an indoor WLAN application using a neural network
Knowledge and Information Systems, 2009
Due to the popularity of location-based services, determining the location of a device at all times has become a subject of great interests. Although many GPS-based applications have been developed and successfully deployed in various fields, their applicabilities are hindered by the obstruction of the objects in the environment. Essentially, as satellite signals cannot penetrate the walls of buildings, the coverage of GPS systems is limited to outdoor environments. To fully exploit the benefit of location-based services, approaches that determine the location of a device in indoor environments need to be established. This study presents a novel location determination mechanism that uses an indoor WLAN and back-propagation neural network (BPN). A museum is taken as the context of the example indoor environment. Location determination is achieved using the combined strengths of 802.11b wireless access signals. With a significant number of access points (APs) installed in the museum, hand-held devices can sense the strengths of the signals from all APs to which the devices can connect. Using a back-propagation network, device locations can be estimated with sufficient accuracy. A novel adaptive algorithm is implemented for enhancing the accuracy of the estimation.
Mobile Information Systems, 2016
In indoor environments, WiFi (RSS) based localization is sensitive to various indoor fading effects and noise during transmission, which are the main causes of localization errors that affect its accuracy. Keeping in view those fading effects, positioning systems based on a single technology are ineffective in performing accurate localization. For this reason, the trend is toward the use of hybrid positioning systems (combination of two or more wireless technologies) in indoor/outdoor localization scenarios for getting better position accuracy. This paper presents a hybrid technique to implement indoor localization that adopts fingerprinting approaches in both WiFi and Wireless Sensor Networks (WSNs). This model exploits machine learning, in particular Artificial Natural Network (ANN) techniques, for position calculation. The experimental results show that the proposed hybrid system improved the accuracy, reducing the average distance error to 1.05 m by using ANN. Applying Genetic A...
Use of Neural Networks in Indoor Geolocation Applications
2007
Main reason to conduct research under geolocation in indoor applications is to create system, that assists the blind or handicapped people to find objects (e.g. keys, documents, medicaments, etc.), which are equipped with small radio personal assistant of the form of transponders (eg. RFID). Person who has personal assistant choose from lists of register things this one, that has been lost. Radio station of known positions measurement distance between lost object and them and pass information about computing location to blind people. Information may have form like: “keys are on desk”. Such system should be reliable, user-friendly and sufficiently accurate, which results in measured localization error of no more then 50 centimeters. Also we require small size and weight, it should uses free of charge waveband and be easy to practicable. The accuracy of radio-signal localization is a difficult problem, especially when the room layout and interior obstacles (e.g. walls, furniture) are ...
Neural Network-based Indoor Localization in WSN Environments
With the advancement of wireless technology even more wireless sensor network (WSN) applications are gaining ground. Their field of application is increasingly widening. This paper examines the WSN application which allows indoor localization based on the Fingerprint (FP) method. The communication between the modules was monitored during the experiment whereby the received radio signal strength indicator (RSSI) values from 5 modules were recorded by a mobile sensor. The received data was used for training of the feed-forward type of neural network. Through use of the trained neural network and the measured RSSI values an indoor localization was realized in a real environment. The neural network-based localization method is analyzed applying the cumulative distribution function (CDF). For the reference model the well-known weighted k-nearest neighbour (WkNN) method was used.
A Neural-Network-Based Indoor Positioning System by Using Sectored Antenna Array
Abstract—This paper presents the development of sectored antenna array and modified probabilistic neural network positioning algorithm for an indoor positioning system (IPS). Firstly, a new hexagonal IPS station is composed of six printed-circuit board Yagi-Uda antennas and Zigbee modules. It is designed to obtain the signals between an object and the station. Then, a modified probabilistic neural network (MPNN) is applied to estimate the accurate position of the object with the signal strength. From the experimental positioning results shown, the developed IPS system has the outperformance in an 8x8 square meters indoor scene. The proposed indoor positioning technique not only has a high positioning accuracy, but also is an effective solution to solve the difficult issue of positioning station deployment.
A new method for improving Wi-Fi-based indoor positioning accuracy
Wi-Fi and smartphone based positioning technologies are play-ing a more and more important role in Location Based Service (LBS) industries due to the rapid development of the smartphone market. However, the low positioning accuracy of these technologies is still an issue for indoor positioning. To ad-dress this problem, a new method for improving the indoor posi-tioning accuracy was developed. The new method initially used the Nearest Neighbor (NN) algorithm of the fingerprinting meth-od to identify the initial position estimate of the smartphone us-er. Then two distance correction values in two roughly perpen-dicular directions were calculated by the path loss model based on the two signal strength indicator (RSSI) values observed. The errors from the path loss model were eliminated by differencing two model-derived distances from the same access point. The new method was tested and the results compared and assessed against that of the commercial Ekahau RTLS system and the NN algorithm. The preliminary results showed that the positioning accuracy has been improved consistently after the new method was applied and the root mean square accuracy improved to 3.3 m from 3.8 m compared with the NN algorithm.