Wireless Localization Based on RSSI Fingerprint Feature Vector

Zhang, Aiguo; Yuan, Ying; Wu, Qunyong; Zhu, Shengqi; Deng, Jing

doi:10.1155/2015/528747

Cited by 27 publications

(15 citation statements)

References 2 publications

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“…Fingerprinting is a localization method comprising two phases. In the first phase-learning-vectors composed of the RSSI values and optional extra features measured by a measuring device in the known locations are collected [7]. These reference values-the calibrated datasetare saved together with the location coordinates into the fingerprint database for the needs of localization.…”

Section: Fingerprintingmentioning

confidence: 99%

Improving Indoor Localization Using Bluetooth Low Energy Beacons

Kriz

Malý

Kozel

2016

Mobile Information Systems

205

145

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The paper describes basic principles of a radio-based indoor localization and focuses on the improvement of its results with the aid of a new Bluetooth Low Energy technology. The advantage of this technology lies in its support by contemporary mobile devices, especially by smartphones and tablets. We have implemented a distributed system for collecting radio fingerprints by mobile devices with the Android operating system. This system enables volunteers to create radio-maps and update them continuously. New Bluetooth Low Energy transmitters (Apple uses its “iBeacon” brand name for these devices) have been installed on the floor of the building in addition to existing WiFi access points. The localization of stationary objects based on WiFi, Bluetooth Low Energy, and their combination has been evaluated using the data measured during the experiment in the building. Several configurations of the transmitters’ arrangement, several ways of combination of the data from both technologies, and other parameters influencing the accuracy of the stationary localization have been tested.

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Section: Fingerprintingmentioning

confidence: 99%

Improving Indoor Localization Using Bluetooth Low Energy Beacons

Kriz

Malý

Kozel

2016

Mobile Information Systems

205

145

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“…Wi-Fi-based fingerprint localization technology is one of the most popular localization technologies based on 2D modeling [30,31]. Fingerprint localization technology mainly uses the signal as the fingerprint step which is stored in the database.…”

Section: Id:p0200mentioning

confidence: 99%

Localization of a Mobile Device with Sensor Using a Cascade Artificial Neural Network-Based Fingerprint Algorithm

Erdem¹,

Tuncer²,

Doğan³

2019

IJCIS

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One of the important functions of sensor networks is that they collect data from the physical environment and transmit them to a center for processing. The location from which the collected data is obtained is crucial in many applications, such as search and rescue, disaster relief, and target tracking. In this respect, determination of the location with low-cost, scalable, and efficient algorithms is required. This study presents the implementation of a fingerprint-based location determination algorithm by using the cascade artificial neural network (ANN). A 15.6 × 13.8 m 2 implementation area, in which an anchor node is placed at each corner, is divided into grids with a 60-cm edge. The proposed algorithm consists of two phases: offline and online. In the offline phase, first a mobile device with an Xbee sensor, which is able to move sensitively and communicate with anchor nodes, is used. With this device, the implementation area is visited, and at each grid point, received signal strength indicator (RSSI) values and real distances measured from the anchor nodes are recorded in a database. The training of the cascade ANN is done using the database for both range and location determination. In the online phase, the RSSIs measured by the anchor nodes are provided as the input to the cascade ANN algorithm by means of a mobile device in any coordinate. The location of the mobile device and its distance to the anchor nodes are determined with minimum error. To show the superiority of the proposed method, the results obtained are compared with those in the literature and it has been shown that this location determination is made with a smaller error.

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“…The possible positioning technologies can be: IEEE 802.11a/b/g/n WLANs or Wi-Fi (Zhang, Yuan, Wu, Zhu, & Deng, 2015), Bluetooth, Bluetooth Low Energy (Kriz, Maly, & Kozel, 2016), ZigBee (Fang et al 2012), Ultra-wideband, (UWB), Field strength systems, Radio Frequency Identification (RFID), infrared technologies, visible light communications, passive visible light, passive sound-based technologies, ultrasound, audible sound, next-generation indoor positioning systems, etc. (Brena et al 2017;Deak, Curran, & Condell, 2012).All these technologies apply to indoor positioning with a variety of different methods.…”

Section: Indoor Positioning Systemsmentioning

confidence: 99%

Usability of open-source hardware based platform for indoor positioning systems

Dobrilovic¹,

Petrović²,

Malić³

2018

JEMC

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The application of indoor location systems (also known as indoor positioning systems, IPS) has significantly increased in the past decade. Those systems find their role in the broad range of possible implementations, especially in the applications in the area of resource management and location tracking. Their applications can be in safety management, material, construction, and inventory management. Since, those systems are designed for indoor and closed areas where GPS, GLONASS, and other navigation systems are not applicable, a different approach should be used to determine the location. This paper presents a brief overview of indoor localization technologies and methods. The focus of this paper is on RSSI based methods for positioning. The contribution of this paper is in analyses of usability of platforms built on Arduino/Genuino development boards and similar devices and open-source hardware for usage in RSSI based indoor positioning systems. The presented platform is designed and evaluated with the two experiments, with two different technologies.

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Wireless Localization Based on RSSI Fingerprint Feature Vector

Cited by 27 publications

References 2 publications

Improving Indoor Localization Using Bluetooth Low Energy Beacons

Improving Indoor Localization Using Bluetooth Low Energy Beacons

Localization of a Mobile Device with Sensor Using a Cascade Artificial Neural Network-Based Fingerprint Algorithm

Usability of open-source hardware based platform for indoor positioning systems

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