Foglight: Visible Light-Enabled Indoor Localization System for Low-Power IoT Devices

Ma, Shang; Liu, Qiong; Sheu, Phillip C.‐Y.

doi:10.1109/jiot.2017.2776964

Cited by 54 publications

(33 citation statements)

References 39 publications

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“…Lately there has been a lot of research into light based indoor localization systems. These systems provide great accuracy, although they are not viable candidate for wide deployments, for example, Ma et al [ 27 ] developed system with average error of 1.7 mm when localizing 56 points in 1 m 2 . Presented system cannot be efficiently scaled, as it requires a projector projecting images above the evaluation space.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%

MFAM: Multiple Frequency Adaptive Model-Based Indoor Localization Method

Tuta¹,

Jurič²

2018

Sensors

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“…This alternating property can be used to modulate light by changing projected images. FogLight [46] exploits this projection property to design a VLL system based on encoded projection. FogLight uses off-the-shelf DLP projectors and light sensors for high-resolution localization, and it leverages the alternating or fast-flipping property of the DLP to project a binary pattern image, which is actually an encode of the projected area.…”

Section: Encoded Projectionmentioning

confidence: 99%

“…For more detail regarding the specific method, please refer to its original paper. Among all the reviewed methods summarized in Table 3, FogLight [46] has the best reported accuracy. Methods from [49,50,53,56,65,68] also have relatively high accuracy.…”

Section: Comparison Of the Reviewed Vll Systemsmentioning

confidence: 99%

“…For example, as shown in Figure 3a, the mobile user holds his smartphone, in which the camera acts as the receiver, and the location of the smartphone is calculated by the VLL system. Such a setting (or a similar setting where a photo-diode is attached to the subject instead of the smartphone's camera) has been used in [42][43][44][45][46][47][48][49][50][51][52].…”

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confidence: 99%

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Recent Advances in Indoor Localization via Visible Lights: A Survey

Rahman

Wang

2020

Sensors

108

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Because of the limitations of the Global Positioning System (GPS) in indoor scenarios, various types of indoor positioning or localization technologies have been proposed and deployed. Wireless radio signals have been widely used for both communication and localization purposes due to their popular availability in indoor spaces. However, the accuracy of indoor localization based purely on radio signals is still not perfect. Recently, visible light communication (VLC) has made use of electromagnetic radiation from light sources for transmitting data. The potential for deploying visible light communication for indoor localization has been investigated in recent years. Visible-light-based localization enjoys low deployment cost, high throughput, and high security. In this article, the most recent advances in visible-light-based indoor localization systems have been reviewed. We strongly believe that visible-light-based localization will become a low-cost and feasible complementary solution for indoor localization and other smart building applications.

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“…The solution to indoor localization for various location-based IoT applications with acceptable simplicity, robustness, accuracy and responsiveness still needs to be explored. In [42], Foglight: visible light-enabled indoor localization for low-power IoT devices using a hybrid VLC-WiFi network was investigated. The PHY and MAC used to optimize the link reliability of a short-range VLC are given in IEEE 802.15.7 [28].…”

Section: Oiot: Applications and Challengesmentioning

confidence: 99%

Optical Internet of Things within 5G: Applications and Challenges

Teli

Zvánovec

Ghassemlooy

2018

2018 IEEE International Conference on Internet of Things and Intelligence System (IOTAIS)

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The fifth generation (5G) telecommunications standards are being developed to meet the growing demands for high-speed wireless networks (i.e., few tens of Gigabits per second). The 5G standard stems largely from an increasing number of users and plethora of different devices, collectively referred to as smart devices, connecting to a network as part of Internet-of-Things (IoT). A few potential technologies have emerged for 5G such as millimeter waves, massive multiple-input multiple-output, and small cell communications. Although these technologies would satisfy the requirements of 5G, there is a complementary alternative wireless technology of optical wireless communications (OWC), which is being considered. As part of OWC, visible light communications (VLC) and optical camera communications (OCC) are the most attractive options for 5G networks and beyond. VLC with huge frequency spectrum integrated with IoT can open up a wide range of indoor and outdoor applications as part of future smart environments. This paper provides an overview of the all-optical IoT (OIoT) focusing on VLC and OCC based potential applications and challenges as part of 5G standards. Keywords-Optical wireless communications (OWC); visible light communications (VLC); optical camera communications (OCC); Internet of things (IoT); light emitting diodes (LEDs). I.

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Foglight: Visible Light-Enabled Indoor Localization System for Low-Power IoT Devices

Cited by 54 publications

References 39 publications

MFAM: Multiple Frequency Adaptive Model-Based Indoor Localization Method

MFAM: Multiple Frequency Adaptive Model-Based Indoor Localization Method

Recent Advances in Indoor Localization via Visible Lights: A Survey

Optical Internet of Things within 5G: Applications and Challenges

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