On indoor position location with wireless LANs (original) (raw)
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Wireless Indoor Localization Using Fingerprinting Technique
Journal of Advanced Research in Dynamical and Control Systems, 2020
Indoor positioning has gained more interest as one of the upcoming applications due to its use in a variety of services. Multiple technologies such as Bluetooth, Wi-Fi, RFID. However, Wi-Fi based localization in indoor environment offers significant advantages utilizing installed wireless infrastructures and good performances with low cost. With this study, we aim to provide a compromise between accurate positioning and feasibility of the system for practical applications. For this purpose, we minimize the fluctuations of Wi-Fi received signal strength (RSS) by filtering and we combine two approaches to locate a mobile user. At first, we implemeted the traditional fingerprinting technique that uses a real time matching of pre-recorded received signal strength (RSS) from the location data of the user transmitted to nearby access points (AP). Secondly, we used distance-based trilateration technique which determines positions using three known access points. The combination of the two methods provides enhancement of accuracy and wide indoor locating coverage. Regardless the locating data number, experiment confirmed a significant and a consistent performance in term of execution time and accuracy.
Sensors
The Global Positioning System demonstrates the significance of Location Based Services but it cannot be used indoors due to the lack of line of sight between satellites and receivers. Indoor Positioning Systems are needed to provide indoor Location Based Services. Wireless LAN fingerprints are one of the best choices for Indoor Positioning Systems because of their low cost, and high accuracy, however they have many drawbacks: creating radio maps is time consuming, the radio maps will become outdated with any environmental change, different mobile devices read the received signal strength (RSS) differently, and peoples' presence in LOS between access points and mobile device affects the RSS. This research proposes a new Adaptive Indoor Positioning System model (called DIPS) based on: a dynamic radio map generator, RSS certainty technique and peoples' presence effect integration for dynamic and multi-floor environments. Dynamic in our context refers to the effects of people and device heterogeneity. DIPS can achieve 98% and 92% positioning accuracy for floor and room positioning, and it achieves 1.2 m for point positioning error. RSS certainty enhanced the positioning accuracy for floor and room for different mobile devices by 11% and 9%. Then by considering the peoples' presence effect, the error is reduced by 0.2 m. In comparison with other works, DIPS achieves better positioning without extra devices.
Location Fingerprinting Technique for WLAN Device-Free Indoor Localization System
Wireless Personal Communications, 2016
Device-free indoor localization (DFIL) system can locate the position of human body in the indoor environment by observing the changes in the received signal strength indicator (RSSI) of the wireless local area network (WLAN). The accuracy of a DFIL system is depreciated, as the change in the indoor environment due to furniture and other infrastructure movement. This paper investigates the development of testbed of the WLAN network for measuring the RSSI in various indoor environment, as the initial step for designing the fingerprinting-based algorithms for WLAN network. The database of RSSI fingerprint is created initially and then a fingerprint-based algorithm is developed for locating the position of a human body in the indoor environment. The localization algorithm tests the minimum distance in the RSSI values related to the different test points in the indoor environment. This work further demonstrates that how the fingerprints of RSSI are collected and which network configurations generate the most reliable RSSI measurement. For the first phase of designing the testbed, the configurations of different equipment and various tools are elaborated in the indoor environment. For the second phase the RSSI is measured in different propagation indoor environment. The extensive experiments were performed that allow quantification of how changes in an environment affect accuracy. Thus, it is demonstrated that each link offers a viable approach to developing a more robust system for device-free localization that is less susceptible to changes in the environment.
The Novel Performance Evaluation Method of the Fingerprinting-Based Indoor Positioning
IEICE Transactions on Information and Systems, 2016
In this work, the novel fingerprinting evaluation parameter, which is called the punishment cost, is proposed. This parameter can be calculated from the designed matrix, the punishment matrix, and the confusion matrix. The punishment cost can describe how well the result of positioning is in the designated grid or not, by which the conventional parameter, the accuracy, cannot describe. The experiment is done with real measured data on weekdays and weekends. The results are considered in terms of accuracy and the punishment cost. Three well-known machine learning algorithms, i.e. Decision Tree, k-Nearest Neighbors, and Artificial Neural Network, are verified in fingerprinting positioning. In experimental environment, Decision Tree can perform well on the data from weekends whereas the performance is underrated on the data from weekdays. The k-Nearest Neighbors has proper punishment costs, even though it has lower accuracy than that of Artificial Neural Network, which has moderate accuracies but lower punishment costs. Therefore, other criteria should be considered in order to select the algorithm for indoor positioning. In addition, punishment cost can facilitate the conversion spot positioning to floor positioning without data modification.
Modified fingerprinting localization technique of indoor positioning system based on coordinates
Indonesian Journal of Electrical Engineering and Computer Science
The fingerprinting localization technique is the most commonly used localization technique of the indoor positioning system. It is used by several technologies for short and long range position estimation like wireless fidelity and radio frequency. There are several schemes used to estimate a location for the indoor environment but the drawbacks resulted in complexity issues. These drawbacks have negative effects on location estimation. In order to address these drawbacks, this work attempted to explore the fingerprinting localization technique for location estimation of the indoor environment that focuses on position estimation. Results showed that the simplicity of the design of position estimation without compromising the functionality of the operations was observed with 100% accuracy on position estimation.
The past few years have seen wide spread adoption of outdoor positioning services, mainly GPS, being incorporated into everyd ay devices such as smartphones and tablets. While outdoor positioning has been well received by the public, its indoor counterpart has been mostly limited to private use due to its higher costs and complexity for setting up the proper environment . The objective of this research is to provide an affordable mean for indoor localization using wireless local area network (WLAN) Wi-Fi technology. We combined two different Wi-Fi approaches to locate a user. The first method involves the use of matching the pre-recorded received signal strength (RSS) from nearby access points (AP), to the data transmitted from the user on the fly. This is commonly known as "fingerprint matching". The second approach is a distance-based trilateration approach using three known AP coordinates detected on the user"s device to derive the position. The combination of the two steps enhances the accuracy of the user position in an indoor environment allowing location-based services (LBS) such as mobile augmented reality (M AR) to be deployed more effectively in the indoor environment. The mapping of the RSS map can also prove useful to IT planning personnel for covering locations with no Wi-Fi coverage (ie. dead spots). The experiments presented in this research helps provide a foundation for the integration of indoor with outdoor positioning to create a seamless transition experience for users.
In this paper, we propose a new indoor location estimation method which combines the fingerprint technique with the propagation prediction model. The wireless LAN (WLAN) access points (APs) deployed indoors are divided into public APs and private APs. While the fingerprint method can be easily used to public APs usually installed in fixed location, it is difficult to apply the fingerprint scheme to private APs whose location can be freely changed. In the proposed approach, the accuracy of user location estimation is improved by simultaneously utilizing public and private APs. Specifically, the fingerprint method is used to the received signals from public APs and the propagation prediction model is employed to the signals from private APs. The performance of the proposed method is compared with that of conventional indoor location estimation schemes through measurements and numerical simulations in WLAN environments.
Improvements for 802.11-Based Location Fingerprinting Systems
2009 33rd Annual IEEE International Computer Software and Applications Conference, 2009
Position estimation with 802.11 and location fingerprinting has been a topic in research for quite some time already. But there are still some unaddressed issues that are the reason why such systems are not widely used. First, the positioning accuracy still leaves space for improvements. Second, users generally have no information about the quality of the estimated position. Especially in cases where the positioning error is large, the user's trust in the system suffers if he is not notified. Third, most systems that rely on a location fingerprinting approach need a time and effort consuming setup phase in which the training data has to be collected and processed. In this paper, we give an overview of existing possibilities to improve location fingerprinting systems under each of these three aspects. Several alternative solutions for each problem are presented. Finally, a discussion gives an overall picture of the usability of the solutions when considering the system as a whole.
Indonesian Journal of Electrical Engineering and Computer Science
Wireless sensor network (WSN) can be used as a solution to find out the position of an object that cannot be reached by global positioning system (GPS), for example to find out the position of objects in a room known as Indoor Positioning. One method in indoor positioning that can be used is fingerprinting. Inside there are two main work phases, namely training and positioning. The training phase is the process of collecting received signal strength indication (RSSI) data levels from each sensor Node reference that will be used as a reference value for the positioning phase. The more sensor Nodes used, the longer the processing time needed in the training phase. This research focussed on the duration of the training phase, the implementation of which are used 4 sensor Nodes, namely Zigbee (IEEE 802.15.4 protocol) arranged according to mesh network topology, one as Node X (positioning target) and 3 as reference Nodes. There are two methods used in the training phase, namely fixed tar...
On the efficacy of WiFi indoor positioning in a practical setting
2013 IEEE Symposium on Computers and Communications (ISCC), 2013
We implement two popular WiFi, fingerprinting based indoor tracking mechanisms, namely the k-nearest neighbours and probabilistic positioning methods. Both mechanisms are evaluated in the context of an indoor position-tracking tablet application, following an investigation to determine optimal working parameters. Our results indicate that even after significant optimisation, both fingerprinting algorithms are highly sensitive to the location of the access points and do not produce finely grained location results. Although in this case the results are accurate enough for our purposes, factors such as the effect of natural body obstruction of the user as well as the location of the access points used in fingerprinting must be considered carefully if more accuracy is required.