Reliability evaluation of iBeacon for micro-localization

Aman, Sayedul; Jiang, Haowen; Quint, Cuyler D.; Yelamarthi, Kumar; Abdelgawad, Ahmed

doi:10.1109/uemcon.2016.7777904

Cited by 21 publications

(11 citation statements)

References 9 publications

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“…In developing a mobility-based, multi-hop clustering scheme, it is assumed that all vehicles in the network are equipped with on-board units (OBU) devices. The vehicles are also equipped with speed-and distance-measuring devices, such as iBeacon location tracking devices [52], GPS devices and digital odometers. Those devices are commonly available on vehicles to track movement and distance travelled.…”

Section: Network Scenario and System Modelmentioning

confidence: 99%

Mobility-Based Clustering Algorithm for Multimedia Broadcasting over IEEE 802.11p-LTE-enabled VANET

Syfullah

Lim²,

Siaw

2019

KSII TIIS

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Vehicular Ad-hoc Network (VANET) facilities envision future Intelligent Transporting Systems (ITSs) by providing inter-vehicle communication for metrics such as road surveillance, traffic information, and road condition. In recent years, vehicle manufacturers, researchers and academicians have devoted significant attention to vehicular communication technology because of its highly dynamic connectivity and self-organized, decentralized networking characteristics. However, due to VANET's high mobility, dynamic network topology and low communication coverage, dissemination of large data packets (e.g. multimedia content) is challenging. Clustering enhances network performance by maintaining communication link stability, sharing network resources and efficiently using bandwidth among nodes. This paper proposes a mobility-based, multi-hop clustering algorithm, (MBCA) for multimedia content broadcasting over an IEEE 802.11p-LTE-enabled hybrid VANET architecture. The OMNeT++ network simulator and a SUMO traffic generator are used to simulate a network scenario. The simulation results indicate that the proposed clustering algorithm over a hybrid VANET architecture improves the overall network stability and performance, resulting in an overall 20% increased cluster head duration, 20% increased cluster member duration, lower cluster overhead, 15% improved data packet delivery ratio and lower network delay from the referenced schemes [46], [47] and [50] during multimedia content dissemination over VANET.

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Section: Network Scenario and System Modelmentioning

confidence: 99%

Mobility-Based Clustering Algorithm for Multimedia Broadcasting over IEEE 802.11p-LTE-enabled VANET

Syfullah

Lim²,

Siaw

2019

KSII TIIS

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“…Mobile devices are considered particularly effective when it comes to iBeacon reception, its portability and convenience allows for collected data to be processed virtually anywhere. Hardware devices, on the other hand, are less versatile because they are limited to one single location and requires a consistent power source for it to function [11]. However, when it comes to beacon detection, there is a large difference between the two.…”

Section: Receiversmentioning

confidence: 99%

A Bluetooth-based Proximity Sensing and Object-tracking System

MikeQu¹,

Lu²,

Sun³

2019

8th International Conference on Natural Language Processing (NLP 2019)

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This paper proposes the concept overall concept of a bluetooth-based proximity IoT device. Its methodology is introduced along with a working prototype. Potential applications and viability for data analysis is also discussed. In summary, such a device accomplishes the goal of closerange information communication in an effective, secure and reliable way through the use of beacons utilizing the BLE (Bluetooth Low Energy) Technology, a variety of different receiver devices that has native support for Bluetooth as well as a database used for data storage and retrieval. The biggest advantage of this technology is that proximity-based Internet has virtually endless potential for applications in the real world, including healthcare, retail and industrial manufacturing.

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“…Therefore, the group of "solid points" represented by iBeacons is distributed on the vertices and square edges. Although the issue of the exact position of solid points may seem attractive to solve, in fact, the different locations of individual iBeacons have very little effect on the accuracy of position determination, see experiment [12]. Then, the receiver inside the vehicle periodically scans the iBeacons around, and it also estimates the distances from each individual transmitter.…”

Section: D Trilaterationmentioning

confidence: 99%

An IoT Approach to Positioning of a Robotic Vehicle

Dvorak

Doležel

2018

Advances in Intelligent Systems and Computing

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This paper presents and evaluates one approach to the problems of automatic control of a vehicle movement in a large outdoor area. The positioning of the vehicle in the area is provided by iBeacons, located at the edges of the given surface. The iBeacon is a small and lowpower device which periodically transmits its UUID (Universally Unique Identifier) number through the interface of a Bluetooth 4.x. The vehicle should be able to calculate its position according to the power of the signal, considering the location of the iBeacons. To be more specific, the triangulation method is applied to determine the position. According to the set of experiments presented at the end of the paper, the position error of a robotic vehicle is mostly less then 1m.

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Reliability evaluation of iBeacon for micro-localization

Cited by 21 publications

References 9 publications

Mobility-Based Clustering Algorithm for Multimedia Broadcasting over IEEE 802.11p-LTE-enabled VANET

Mobility-Based Clustering Algorithm for Multimedia Broadcasting over IEEE 802.11p-LTE-enabled VANET

A Bluetooth-based Proximity Sensing and Object-tracking System

An IoT Approach to Positioning of a Robotic Vehicle

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