An Empirical Study of the Transmission Power Setting for Bluetooth-Based Indoor Localization Mechanisms

Castillo-Cara, Manuel; Lovón-Melgarejo, Jesús; Bravo-Rocca, Gusseppe; Orozco-Barbosa, Luis; García-Varea, Ismael

doi:10.3390/s17061318

Cited by 42 publications

(37 citation statements)

References 22 publications

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“…In the second case, the fingerprinting method [ 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 ], several measurements are taken at each possible position, so that there is a pre-established map of distance-RSSI values. With this approach the accuracy of the system is greater compared to the previous one, but we need an exhausted characterization of the environment.…”

Section: Introductionmentioning

confidence: 99%

A Bluetooth Low Energy Indoor Positioning System with Channel Diversity, Weighted Trilateration and Kalman Filtering

Paterna

Calveras

Paradells

et al. 2017

Sensors

169

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Indoor Positioning Systems (IPS) using Bluetooth Low Energy (BLE) technology are currently becoming real and available, which has made them grow in popularity and use. However, there are still plenty of challenges related to this technology, especially in terms of Received Signal Strength Indicator (RSSI) fluctuations due to the behaviour of the channels and the multipath effect, that lead to poor precision. In order to mitigate these effects, in this paper we propose and implement a real Indoor Positioning System based on Bluetooth Low Energy, that improves accuracy while reducing power consumption and costs. The three main proposals are: frequency diversity, Kalman filtering and a trilateration method what we have denominated “weighted trilateration”. The analysis of the results proves that all the proposals improve the precision of the system, which goes up to 1.82 m 90% of the time for a device moving in a middle-size room and 0.7 m for static devices. Furthermore, we have proved that the system is scalable and efficient in terms of cost and power consumption. The implemented approach allows using a very simple device (like a SensorTag) on the items to locate. The system enables a very low density of anchor points or references and with a precision better than existing solutions.

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Section: Introductionmentioning

confidence: 99%

A Bluetooth Low Energy Indoor Positioning System with Channel Diversity, Weighted Trilateration and Kalman Filtering

Paterna

Calveras

Paradells

et al. 2017

Sensors

169

View full text Add to dashboard Cite

show abstract

“…Furthermore, a comparison with other tracking technologies might be considered. In particular, the application of the Bluetooth low energy (BLE) technology for indoor positioning purposes was investigated in [ 2 ] This technology offers the advantage of an easy implementation, because BLE functionality is embedded in widespread commercial devices. Compared to our prototype magnetic tracking system, its longer operational range allows for suitable application to many indoor scenarios.…”

Section: Experimental Magnetic Tracking System (Mts)mentioning

confidence: 99%

“…Various techniques have been proposed to solve this problem. Several approaches are based on radio propagation, such as Ultra Wide Band [ 1 ], Bluetooth [ 2 ], and Zig-Bee [ 3 ] technologies. Alternative techniques are based on the propagation of acoustic waves, such as the ultrasound techniques proposed in [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Measurement Models for 3D Magnetic Localization and Tracking

Angelis

Moschitta

et al. 2017

Sensors

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In this paper, we consider magnetic positioning and tracking of objects and provide a comparison of the characteristics of two major measurement models: the magnetic dipole model and the mutual inductance model. The numerical results obtained by applying these models to a short-range position measurement application, with a maximum operating distance of approximately 50 cm, are compared. Based on the results of this comparison, a prototype 9-sensor array is developed, experimental tests are performed, and extensive measurement results are presented. Outcomes show the feasibility of tracking the position and orientation of a mobile coil in real time with a median positioning error below 1 cm and a worst-case error of about 2 cm and 11 degrees inside a spatial region of 30 × 30 × 30 cm3 operational volume.

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“…However, the location of people or object in the network is the basic information for communication. Current wireless localization technologies include GPS-based [Wang, Lu, Wang et al (2017)], infrared-based [Park, Cho, Kim et al (2014)], Bluetooth-based [Castillo-Cara, Lovón-Melgarejo, Bravo-Rocca et al (2017)], signal transmission, the nodes are distributed unevenly, which will result in A* B* A* B* dh . Therefore, we can intuitively understand that if the nodes in the network are evenly distributed, the Euclidean distance between pairs of nodes is proportional to the hop count; conversely, the proportional relationship between the Euclidean distance and the hop count no longer holds.…”

Section: Introductionmentioning

confidence: 99%

A Novel Multi-Hop Algorithm for Wireless Network with Unevenly Distributed Nodes

Liu

Yang²,

Yan³

et al. 2019

Computers, Materials &Amp; Continua

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Node location estimation is not only the promise of the wireless network for target recognition, monitoring, tracking and many other applications, but also one of the hot topics in wireless network research. In this paper, the localization algorithm for wireless network with unevenly distributed nodes is discussed, and a novel multi-hop localization algorithm based on Elastic Net is proposed. The proposed approach is formulated as a regression problem, which is solved by Elastic Net. Unlike other previous localization approaches, the proposed approach overcomes the shortcomings of traditional approaches assume that nodes are distributed in regular areas without holes or obstacles, therefore has a strong adaptability to the complex deployment environment. The proposed approach consists of three steps: the data collection step, mapping model building step, and location estimation step. In the data collection step, training information among anchor nodes of the given network is collected. In mapping model building step, the mapping model among the hop-counts and the Euclidean distances between anchor nodes is constructed using Elastic Net. In location estimation step, each normal node finds its exact location in a distributed manner. Realistic scenario experiments and simulation experiments do exhibit the excellent and robust location estimation performance. (2009)], WIFI-based and UWB-based [Mazhar, Khan and Sä llberg (2017)], etc. Among them, GPS-based application is the most mature localization application with broadest application, but when there are obstacles, it is very difficult to satisfy the application requirement on the aspect of localization precision; the infrared ray has short propagation distance, which also tends to be interfered with by fluorescent lamp or indoor lighting, and there is certain limitation during localization; both the Bluetooth technology and RFID technology apply to indoor localization, but the Bluetooth technology has poor stability in complicated environment and system, while the RFID technology has short transmission distance, and it does not have the communication capacity; during the WIFI localization, the transmitter and receiver can only cover an area within 90 m, but it tends to be interfered with by other signals, and its locator involves high energy consumption; the UWB localization method can provide centimeter-level localization precision, but it is difficult to capture the UWB signal, and it also tends to cause significant interference with other signals. With the introduction of new wireless networks such as IoT (Internet of Things) and Mesh, wireless nodes can be randomly deployed in various types of monitoring environment, and the location information can be exchanged through the methods of multi-hop and self-organization. The node localization method based on multi-hop and self-organization is called the "multi-hop localization" method. The multi-hop localization method can be roughly divided into two groups of range-based and range-free methods according to whether...

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An Empirical Study of the Transmission Power Setting for Bluetooth-Based Indoor Localization Mechanisms

Cited by 42 publications

References 22 publications

A Bluetooth Low Energy Indoor Positioning System with Channel Diversity, Weighted Trilateration and Kalman Filtering

A Bluetooth Low Energy Indoor Positioning System with Channel Diversity, Weighted Trilateration and Kalman Filtering

Comparison of Measurement Models for 3D Magnetic Localization and Tracking

A Novel Multi-Hop Algorithm for Wireless Network with Unevenly Distributed Nodes

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