Hybrid Indoor Human Localization System for Addressing the Issue of RSS Variation in Fingerprinting

Bitew, Mekuanint Agegnehu; Hsiao, Rong-Shue; Lin, Hsin‐Piao; Lin, Ding‐Bing

doi:10.1155/2015/831423

Cited by 24 publications

(16 citation statements)

References 23 publications

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“…Additionally, the received signal strength indicator (RSSI) is prone to error and may indicate different locations based on similar RSSI. The dynamic factors including the presence of obstacles and people may cause huge fluctuations in the collected RSSI during the localization process which increases the error [55,56]. Similarly, Wi-Fi based localization is highly sensitive to the wall separations, and floor plans and so they are vulnerable to random noise, path loss, multipath interference, shadowing, and so on [57].…”

Section: Related Workmentioning

confidence: 99%

Application of Deep Convolutional Neural Networks and Smartphone Sensors for Indoor Localization

2019

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Indoor localization systems are susceptible to higher errors and do not meet the current standards of indoor localization. Moreover, the performance of such approaches is limited by device dependence. The use of Wi-Fi makes the localization process vulnerable to dynamic factors and energy hungry. A multi-sensor fusion based indoor localization approach is proposed to overcome these issues. The proposed approach predicts pedestrians' current location with smartphone sensors data alone. The proposed approach aims at mitigating the impact of device dependency on the localization accuracy and lowering the localization error in the magnetic field based localization systems. We trained a deep learning based convolutional neural network to recognize the indoor scene which helps to lower the localization error. The recognized scene is used to identify a specific floor and narrow the search space. The database built of magnetic field patterns helps to lower the device dependence. A modified K nearest neighbor (mKNN) is presented to calculate the pedestrian's current location. The data from pedestrian dead reckoning further refines this location and an extended Kalman filter is implemented to this end. The performance of the proposed approach is tested with experiments on Galaxy S8 and LG G6 smartphones. The experimental results demonstrate that the proposed approach can achieve an accuracy of 1.04 m at 50 percent, regardless of the smartphone used for localization. The proposed mKNN outperforms K nearest neighbor approach, and mean, variance, and maximum errors are lower than those of KNN. Moreover, the proposed approach does not use Wi-Fi for localization and is more energy efficient than those of Wi-Fi based approaches. Experiments reveal that localization without scene recognition leads to higher errors.

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Section: Related Workmentioning

confidence: 99%

Application of Deep Convolutional Neural Networks and Smartphone Sensors for Indoor Localization

2019

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“…Another research work is presented on hybrid indoor localization system based on pyroelectric infrared (PIR) sensors and RF model [13]. This hybrid model works in two stages.…”

Section: Previous Related Workmentioning

confidence: 99%

Hybrid Indoor-Based WLAN-WSN Localization Scheme for Improving Accuracy Based on Artificial Neural Network

Farid

Nordin

Ismail

et al. 2016

Mobile Information Systems

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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 Algorithm (GA) based optimization technique did not incur any further improvement to the accuracy. Compared to the performance of GA optimization, the nonoptimized ANN performed better in terms of accuracy, precision, stability, and computational time. The above results show that the proposed hybrid technique is promising for achieving better accuracy in real-world positioning applications.

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“…Global positioning system (GPS) is advantage of user's position identification in outdoor environments. The GPS is not suitable for indoor environments due to lack of line sight between the satellites and receiver was reported by Mekuanint Agegnehu Bitew et al [20]. Kensuke Onishi [21] has recently published an article describing, Beacons using BLE transmit BLE signals, which received by smartphones running iOS or Android OS and demonstration experiments were alone conducted.…”

Section: Related Workmentioning

confidence: 99%

Direction Detecting System of Indoor Smartphone Users Using BLE in IoT

Kothandaraman¹,

Chellappan²

2016

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Indoor organization user activity's (UA) direction detection monitoring system and also emergency prediction are major challenging tasks in the field of the typical body sensor and indoor fixed sensor networks. In this paper, indoor UA based direction detection monitoring system is achieved by the combination of both the orientation sensor and Bluetooth Low Energy (BLE) in user's smartphones belonging to the Internet of Things (IoT). The orientation sensor senses the actual orientation of the user and BLE transmits the sensed BLE signals to monitoring system using star topology in IoT. In monitoring system, classification algorithm is used to identify the directions of the smartphone users. The emergency situation of the user is also predicted based on signal variation instantly in real time. The user activity's signals are captured using LabVIEW toolkit then applied to various classification algorithms such as RF-91.42%, Ibk-90.55%, j48-85.61%, K*-73.54% are the results obtained. An average of 85% was obtained in all the classification algorithims indicating the consistency and accuracy in detecting the directions of the users. RF was found to be the best among all the classification algorithms. IoT enabled devices have high demand in near coming future, moreover smartphones users increase day by day, hence implementing and maintaining the above said system would be much easier and cheaper compared to other conventional networks.

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Hybrid Indoor Human Localization System for Addressing the Issue of RSS Variation in Fingerprinting

Cited by 24 publications

References 23 publications

Application of Deep Convolutional Neural Networks and Smartphone Sensors for Indoor Localization

Application of Deep Convolutional Neural Networks and Smartphone Sensors for Indoor Localization

Hybrid Indoor-Based WLAN-WSN Localization Scheme for Improving Accuracy Based on Artificial Neural Network

Direction Detecting System of Indoor Smartphone Users Using BLE in IoT

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