High-precision approach to localization scheme of visible light communication based on artificial neural networks and modified genetic algorithms

Guan, Weipeng; Wu, Yuxiang; Xie, Canyu; Chen, Hao; Cai, Ye; Chen, Ying-Cong

doi:10.1117/1.oe.56.10.106103

Cited by 44 publications

(14 citation statements)

References 19 publications

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“…A PD-based VLC system realizes communicating by receiving the light intensity of different LEDs, it costs less, and has a fast response time, which makes it possible to measure a small period of time of signal arrival; however, the main flaw of PD is that it is not stable enough, especially in an outdoor environment. In our previous works [10,11,12,13,14], we have completed a series of research projects regarding VLC-based on PD, and it can be found that PDs are sensitive to the direction of the light beam and the interference of the background light or the reflection of the surrounding objects, the communication based on PDs would be affected and even fail. Therefore, for the weakness of PDs mentioned above, an image sensor performs better in the field of OVLC applications.…”

Section: Introductionmentioning

confidence: 99%

Improved Target Signal Source Tracking and Extraction Method Based on Outdoor Visible Light Communication Using a Cam-Shift Algorithm and Kalman Filter

Huang

Guan

Fan

et al. 2018

Sensors

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An improved Cam-Shift algorithm with a Kalman filter applied to image-sensor based on outdoor visible light communication (OVLC) is presented in this paper. The proposed optimized tracking algorithm is used to track and extract the region of the target signal source Light Emitting Diode (LED) that carries modulated information for data transmission. Extracting the target signal source LED area is the premise of an image-sensor-based VLC system, especially in outdoor dynamic scenes. However, most of the existing VLC studies focus on data transmission rate, visible light positioning, etc. While the actual first step of realizing communication is usually ignored in the field of VLC, especially when the transmitter (signal source LED) or the receiver (image sensor) is moving in a more complex outdoor environment. Therefore, an improved tracking algorithm is proposed in this paper, aiming at solving the problem of extracting the region of the target signal source LED accurately in dynamic scenes with different interferences so as to promote the feasibility of VLC applications in outdoor scenes. The proposed algorithm considers color characteristics and special distribution characteristics of the moving target at the same time. The image is converted to a color probability distribution map based on the color histogram of the target and adaptively adjusts the location and size of the search window based on the results obtained from the previous frame. Meanwhile, it predicts the motion state of the target in the next frame according to the position and velocity information of the current frame to enhance accuracy and robustness of tracking. Experimental results show that the tracking error of the proposed algorithm is 0.85 cm and the computational time of processing one frame is 0.042 s. Besides, results also show that the improved algorithm can track and extract the target signal source LED area completely and accurately in an environment of many interference factors. This study confirms that the proposed algorithm can be applied to an OVLC system with many interferences to realize the actual first step of communication in an image-sensor-based VLC system, laying foundations for subsequent data transmission and other steps.

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Section: Introductionmentioning

confidence: 99%

Improved Target Signal Source Tracking and Extraction Method Based on Outdoor Visible Light Communication Using a Cam-Shift Algorithm and Kalman Filter

Huang

Guan

Fan

et al. 2018

Sensors

Self Cite

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“…In fact, the main advantage of BP neural network is the outstanding ability of non-linear mapping and classification which is ideal for applications in indoor positioning systems [17]. Therefore, the BP algorithm is very suitable for indoor positioning due to the characteristics of indoor scenes [18,19,20,21]. In this paper, we use the Back-Propagation algorithm based on multi-layer feed-forward neural networks to realize high precision indoor positioning.…”

Section: Theory and Methodsmentioning

confidence: 99%

High-Precision Indoor Visible Light Positioning Using Modified Momentum Back Propagation Neural Network with Sparse Training Point

Zhang

Cui

Feng

et al. 2019

Sensors

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In this letter, we propose an indoor visible light positioning technique using a Modified Momentum Back-Propagation (MMBP) algorithm based on received signal strength (RSS) with sparse training data set. Unlike other neural network algorithms that require a large number of training data points to locate accurately, we have realized high-precision positioning for 100 test points with only 20 training points in a 1.8 m × 1.8 m × 2.1 m localization area. In order to verify the adaptability of the MMBP algorithm, we experimentally demonstrate two different training data acquisition methods adopting either even or arbitrary training sets. In addition, we also demonstrate the positioning accuracy of the traditional RSS algorithm. Experimental results show that the average localization accuracy optimized by our proposed algorithm is only 1.88 cm for the arbitrary set and 1.99 cm for the even set, while the average positioning error of the traditional RSS algorithm reaches 14.34 cm. Comparison indicates that the positioning accuracy of our proposed algorithm is 7.6 times higher. Results also show that the performance of our system is higher than some previous reports based on RSS and RSS fingerprint databases using complex machine learning algorithms trained by a large amount of training points.

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“…Analysis [17], [112], [235]- [266] [73], [97], [187], [267]- [291] Mobility Analysis [227], [292]- [310] User Localization [272], [273], [311]- [315] [111], [316]- [334] Wireless Sensor Networks [335]- [346], [346]- [356] Network Control [186], [293], [357]- [368] [234], [368]- [403] Network Security [185], [345], [404]- [419] [223], [420]- [429], [429]- [436] Signal Processing [378], [380], [437]- [444] [322], [445]- [458] Emerging Applications For each domain, we summarize work broadly in tabular form, providing readers with a general picture of individual topics. Most important works in each domain are discussed in more details in text.…”

Section: App-level Mobile Datamentioning

confidence: 99%

Deep Learning in Mobile and Wireless Networking: A Survey

Zhang

Patras

Haddadi

2019

IEEE Commun. Surv. Tutorials

1,344

838

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The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space.In this paper we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-theart in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.

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High-precision approach to localization scheme of visible light communication based on artificial neural networks and modified genetic algorithms

Cited by 44 publications

References 19 publications

Improved Target Signal Source Tracking and Extraction Method Based on Outdoor Visible Light Communication Using a Cam-Shift Algorithm and Kalman Filter

Improved Target Signal Source Tracking and Extraction Method Based on Outdoor Visible Light Communication Using a Cam-Shift Algorithm and Kalman Filter

High-Precision Indoor Visible Light Positioning Using Modified Momentum Back Propagation Neural Network with Sparse Training Point

Deep Learning in Mobile and Wireless Networking: A Survey

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