Using iBeacon for newborns localization in hospitals

Li, Zhouchi; Yang, Yang; Pahlavan, Kaveh

doi:10.1109/ismict.2016.7498906

Cited by 20 publications

(14 citation statements)

References 5 publications

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“…Palumbo et al [25] combine the stigmergic marking approach with the Min-Max algorithm and draw the location estimation from the stigmergic map. Two other BLE-based approaches [26], [27] adopt the same idea of deriving the location using the beacon with the strongest RSSI value.…”

Section: A Indoor Localizationmentioning

confidence: 99%

A Sensing Framework for Indoor Spatial Awareness for Blind and Visually Impaired Users

Yang¹,

Ganz²

2019

IEEE Access

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In this paper, we present Bluetooth low energy (BLE)-based sensing framework that provides real time spatial awareness for blind and visually impaired (BVI) users while navigating independently through large public venues. The proposed framework includes the following functionalities using only the received signal strength indicator obtained from the BLE sensors: 1) determining the location of the user, 2) estimating the user's moving direction, and 3) detecting the proximity of landmarks next to the user. We evaluate these functionalities individually. Moreover, we incorporate the proposed framework in PERCEPT indoor navigation system and test it with BVI users in a large public venue. Testing results show that the location, moving direction, and landmark proximity computed by the framework, although not very accurate, provide sufficient information to enable BVI users to independently navigate in large indoor venues. This conclusion aligns with Nobel Prize winning findings that confirm the spatial nature of the entorhinal-hippocampal system and the existence of a positioning system in the brain.

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Section: A Indoor Localizationmentioning

confidence: 99%

A Sensing Framework for Indoor Spatial Awareness for Blind and Visually Impaired Users

Yang¹,

Ganz²

2019

IEEE Access

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“…To analyze the relation between RSS and the least location error (CRLB), we can apply partial differential to Eq. (25) [10,19]. Then we have…”

Section: Cramer-rao Lower Boundmentioning

confidence: 99%

“…In this case, the location is out of coverage and cannot used for position detection. Considering the matter of coverage, it is essential to determine the probability that one location is within the coverage before we start to calculate CRLB [9,10].…”

Section: Introductionmentioning

confidence: 99%

Precision of RSS-Based Localization in the IoT

Ying

Pahlavan

2019

Int J Wireless Inf Networks

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Currently, the most popular indoor geolocation technique used in smart device is the RSS-based Wi-Fi localization. The general accuracy of Wi-Fi localization in around 10-15 m. To improve this accuracy in indoor areas, site survey is commonly used to collect a signature data base. With the emergence of smart buildings and the IoT to connect the sensors for the smart building, a number of low power sensor devices with diversified power levels will be deployed in the building. The radiated RSS from these devices can be used to improve the precision of the RSS based localization. In this paper we introduce an analytical frame work for calculation of the CRLB in an environment with variable radiated powers from sensors to complement the Wi-Fi localization. We use the analytical framework to demonstrate the accuracy of localization in the third floor of the Atwater Kent Laboratory at Worcester Polytechnic Institue, for existin Wi-Fi localization and variety of sensor deployment scenarios. The existing CRLB calculation for the RSS-Based localization use the differential value of the received signal strength from given reference points to calculate the variance of the location estimate. As a result, effect of the transmitted power from the sources is eliminated. In another words this approach cannot demonstrate the effects of transmitted power of the reference device on the performance of the system. By introducing the concept of certainty in the measurement of the RSS, we also introduce a novel model for calculation of the performance of RSS based localization with inclusion of the effects of the transmitted power from the reference sources.

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“…In recent years, a number of researchers have benefited from RF cloud features to introduce innovative cyberspace applications. As a simple example, using an algorithm measuring variations of the RSS above its average value, one could detect the number of people attending a class [26], or monitor newborn babies in a hospital [27]. More complex cyberspace applications using opportunistic signals available in the RF cloud is achievable by using artificial intelligence algorithms and taking advantage of more complex features of the signal, such as CIR, CSI, TOA, and DOA.…”

Section: ) Motion Related Cyberspace Applicationsmentioning

confidence: 99%

RF Cloud for Cyberspace Intelligence

Pahlavan

Ying

et al. 2020

IEEE Access

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Wireless information networks have become a necessity of our day-today life. Over a billion Wi-Fi access points, hundreds of thousands of cell towers, and billions of IoT devices, using a variety of wireless technologies, create the infrastructure that enables this technology to access everyone, everywhere. The radio signal carrying the wireless information, propagates from antennas through the air and creates a radio frequency (RF) cloud carrying a huge amount of data that is commonly accessible by anyone. The big data of the RF cloud includes information about the transmitter type and addresses, embedded in the information packets; as well as features of the RF signal carrying the message, such as received signal strength (RSS), time of arrival (TOA), direction of arrival (DOA), channel impulse response (CIR), and channel state information (CSI). We can benefit from the big data contents of the messages as well as the temporal and spatial variations of their RF propagation characteristics to engineer intelligent cyberspace applications. This paper provides a holistic vision of emerging cyberspace applications and explains how they benefit from the RF cloud to operate. We begin by introducing the big data contents of the RF cloud. Then, we explain how innovative cyberspace applications are emerging that benefit from this big data. We classify these applications into three categories: wireless positioning systems, gesture and motion detection technologies, and authentication and security techniques. We explain how Wi-Fi, cell-tower, and IoT wireless positioning systems benefit from big data of the RF cloud. We discuss how researchers are studying applications of RF cloud features for motion, activity and gesture detection for human-computer interaction, and we show how authentication and security applications benefit from RF cloud characteristics.

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Using iBeacon for newborns localization in hospitals

Cited by 20 publications

References 5 publications

A Sensing Framework for Indoor Spatial Awareness for Blind and Visually Impaired Users

A Sensing Framework for Indoor Spatial Awareness for Blind and Visually Impaired Users

Precision of RSS-Based Localization in the IoT

RF Cloud for Cyberspace Intelligence

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