ZigBee based wireless indoor localization with sensor placement optimization towards practical home sensing*

Shimosaka, Masamichi; Saisho, Osamu; Sunakawa, Takuya; Koyasu, Hidenori; Maeda, Keisuke; Kawajiri, Ryoma

doi:10.1080/01691864.2015.1132636

Cited by 13 publications

(14 citation statements)

References 3 publications

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“…Indeed, this method exploits information on angles or distances to determine the locations of the sensors. These data can be obtained using metrics such as TOA (Time of Arrival), AOA (Angle of Arrival), TDOA (Time Difference of Arrival) and RSSI (Received Signal Strength Indicator) [12] and [13]. Range-based methods are characterized by a fine resolution.…”

Section: Classification Of Localization Methodsmentioning

confidence: 99%

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3d Indoor Localization Through a Wireless Acoustic sensor Networks

Nasri¹,

Rached²,

Chenini³

et al. 2018

PIER B

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GPS is well recognized as the best procedure for outdoor localization. However, it presents limits in indoor localization due to particular geometric difficulties that necessitate specific solution to locate a target inside a building. Also, radio frequency technologies have many disadvantages in indoor localization. Bluetooth and Radio Frequency Identification (RFID) are unsuitable for realtime localization because of latency. Ultra-wideband (UWB) localization needs an expensive hardware. Zigbee presents a high interference with wide range of signal frequency because it operates in unlicensed Industrial, Scientific and Medical ISM bands. Light waves also present some limitations due to interferences from fluorescent light and sunlight. The IR based indoor system has expensive system hardware and maintenance cost. To overcome limits and non-availability of radio waves and light waves, an acoustic solution using an array of microphones is presented as a solution for indoor localization, and an optimized deployment is used to improve precision and restrain error. The aim of this work is to propose a 3D indoor audio localization approach inspired by the principle of functioning of the human ear. In order to achieve our goal, we will use a genetic algorithm to obtain the optimized deployment of the used hardware.

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Section: Classification Of Localization Methodsmentioning

confidence: 99%

“…Table 4 presents a comparison between real and estimated angles. The angles are determined by applying geometric formulas (Equations (10), (12) and (13)). Once the angles are calculated, the direction and distance between the sensors and the source as well as the source position are estimated.…”

Section: Proposed Approach For 3d Localizationmentioning

confidence: 99%

3d Indoor Localization Through a Wireless Acoustic sensor Networks

Nasri¹,

Rached²,

Chenini³

et al. 2018

PIER B

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“…where ϕ(x) is a feature vector of RSSI. The feature design of ϕ(x) follows Shimosaka et al [16]. The m-th element…”

Section: Classificationmentioning

confidence: 99%

“…and [z] + = max(0, z), E denotes the set of edges in the graph structure and λ 1 and λ 2 are positive constants related to the strength of regularization. The loss term definition follows Shimosaka et al [16], i.e., hinge loss taking the estimation error distance into account.…”

Section: Multi-task Training Settingmentioning

confidence: 99%

Efficient calibration for rssi-based indoor localization by bayesian experimental design on multi-task classification

Shimosaka

Saisho

2016

Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing

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RSSI-based indoor localization is getting much attention. Thanks to a number of researchers, the localization accuracy has already reached a sufficient level. However, it is still not easy-to-use technology because of its heavy installation cost. When an indoor localization system is installed, it needs to collect RSSI data for training classifiers. Existing techniques need to collect enough data at each location. This is why the installation cost is very heavy. We propose a technique to gather data efficiently by using machine learning techniques. Our proposed algorithm is based on multi-task learning and Bayesian optimization. This algorithm can remove the need to collect data of all location labels and select location labels to acquire new data efficiently. We verify this algorithm by using a Wi-Fi RSSI dataset collected in a building. The empirical results suggest that the algorithm is superior to an existing algorithm applying single-task learning and Active Class Selection.

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“…In contrary to our solution, it presents a single-frequency method that also requires the mobile terminal to emit signals. Similarly, paper [ 12 ] uses emitted ZigBee signals for indoor localization. In contrast to our work it requires a difficult and time-consuming initial setup phase, in which a larger number of receivers (1 device per 1 m 2 ) is used; this procedure must be redone when rearranging the indoors, thus this method is impractical for quickly changing environments.…”

Section: Introductionmentioning

confidence: 99%

MFAM: Multiple Frequency Adaptive Model-Based Indoor Localization Method

Tuta¹,

Jurič²

2018

Sensors

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This paper presents MFAM (Multiple Frequency Adaptive Model-based localization method), a novel model-based indoor localization method that is capable of using multiple wireless signal frequencies simultaneously. It utilizes indoor architectural model and physical properties of wireless signal propagation through objects and space. The motivation for developing multiple frequency localization method lies in the future Wi-Fi standards (e.g., 802.11ah) and the growing number of various wireless signals present in the buildings (e.g., Wi-Fi, Bluetooth, ZigBee, etc.). Current indoor localization methods mostly rely on a single wireless signal type and often require many devices to achieve the necessary accuracy. MFAM utilizes multiple wireless signal types and improves the localization accuracy over the usage of a single frequency. It continuously monitors signal propagation through space and adapts the model according to the changes indoors. Using multiple signal sources lowers the required number of access points for a specific signal type while utilizing signals, already present in the indoors. Due to the unavailability of the 802.11ah hardware, we have evaluated proposed method with similar signals; we have used 2.4 GHz Wi-Fi and 868 MHz HomeMatic home automation signals. We have performed the evaluation in a modern two-bedroom apartment and measured mean localization error 2.0 to 2.3 m and median error of 2.0 to 2.2 m. Based on our evaluation results, using two different signals improves the localization accuracy by 18% in comparison to 2.4 GHz Wi-Fi-only approach. Additional signals would improve the accuracy even further. We have shown that MFAM provides better accuracy than competing methods, while having several advantages for real-world usage.

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ZigBee based wireless indoor localization with sensor placement optimization towards practical home sensing*

Cited by 13 publications

References 3 publications

3d Indoor Localization Through a Wireless Acoustic sensor Networks

3d Indoor Localization Through a Wireless Acoustic sensor Networks

Efficient calibration for rssi-based indoor localization by bayesian experimental design on multi-task classification

MFAM: Multiple Frequency Adaptive Model-Based Indoor Localization Method

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