Novel RFID and ontology based home localization system for misplaced objects

Huynh, Son Minh; Parry, David; Fong, A.C.M.; Tang, Jie

doi:10.1109/tce.2014.6937324

Cited by 36 publications

(6 citation statements)

References 20 publications

(43 reference statements)

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“…To finish the work, a programming language in embedded "C" is preloaded on the controller. Related reference papers implementing wireless control of robots have been looked at, as stated in [1][2][3][4][5][6][7][8][9]. However, there is still a need for an easy-to-implement, cost-effective automation solution.…”

Section: Introducationmentioning

confidence: 99%

Bluetooth Controlled Car

Thakor¹,

Solanki²,

Gandhi³

2023

JECNAM

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The 21st century is the era in which we currently reside. Right now, the smart phone is the object that matters the most in our daily life. Android-powered smartphones are becoming more powerful and has a growing number of accessories that are beneficial for robots. This project demonstrates Bluetooth connection with a car, specific Bluetooth technology attributes, mobile and robot elements, as well as how to operate a car using a mobile device. We provide a summary of robots that may be moved forward, backward, left, or right by means of an Android app like Arduino or Bluetooth. Bluetooth has changed how people use digital gadgets at home or at work by converting cable-based ones into wireless ones. Here, we're utilising Bluetooth connection, a microcontroller interface, and an Android application. We are using Arduino software to link the Bluetooth module to the microcontroller. Commands issued from an Android device can control robot motion. We came across simple ways to develop a framework for developing an automobile with very low cost although powerful processing and sensing capabilities enabled by a mobile phone used as a control device.

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Section: Introducationmentioning

confidence: 99%

Bluetooth Controlled Car

Thakor¹,

Solanki²,

Gandhi³

2023

JECNAM

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“…The available indoor solutions are highly coupled to the environment and target application. We can find, for instance, smart home systems designed to help us locate misplaced objects using 802.15.4a [ 20 ]; systems to monitor the daily activities of seniors at home using smartwatches and IEEE 802.11 Wireless LAN (Wi-Fi) fingerprinting [ 21 ]; or remote patient monitoring with ZigBee [ 22 ]. Not only the coverage and accuracy are important to select the base positioning technology, the deployment and maintenance costs are also relevant.…”

Section: Introductionmentioning

confidence: 99%

Collaborative Indoor Positioning Systems: A Systematic Review

Pascacio

Casteleyn

Torres-Sospedra

et al. 2021

Sensors

101

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Research and development in Collaborative Indoor Positioning Systems (CIPSs) is growing steadily due to their potential to improve on the performance of their non-collaborative counterparts. In contrast to the outdoors scenario, where Global Navigation Satellite System is widely adopted, in (collaborative) indoor positioning systems a large variety of technologies, techniques, and methods is being used. Moreover, the diversity of evaluation procedures and scenarios hinders a direct comparison. This paper presents a systematic review that gives a general view of the current CIPSs. A total of 84 works, published between 2006 and 2020, have been identified. These articles were analyzed and classified according to the described system’s architecture, infrastructure, technologies, techniques, methods, and evaluation. The results indicate a growing interest in collaborative positioning, and the trend tend to be towards the use of distributed architectures and infrastructure-less systems. Moreover, the most used technologies to determine the collaborative positioning between users are wireless communication technologies (Wi-Fi, Ultra-WideBand, and Bluetooth). The predominant collaborative positioning techniques are Received Signal Strength Indication, Fingerprinting, and Time of Arrival/Flight, and the collaborative methods are particle filters, Belief Propagation, Extended Kalman Filter, and Least Squares. Simulations are used as the main evaluation procedure. On the basis of the analysis and results, several promising future research avenues and gaps in research were identified.

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“…The authors achieved accurate results on a large area but the overall accuracy of the algorithm heavily depends on the reading on the correction node. If the correction node fails to deliver accurate measurements due to change of environments on the area close to the correction node, the algorithm will accumulate more position errors (Huynh et al 2014, Huang et al 2015, Helander and Humpus 2015.…”

Section: Introductionmentioning

confidence: 99%

Robust Trilateration Based Algorithm for Indoor Positioning Systems

Pande

Ibwe

2021

Tanz. J. Sci.

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Abstract Indoor Positioning Systems (IPS) plays crucial roles in indoor environment items positioning used in self-navigating robots and helping hands. To obtain position information, positioning algorithms employing Received Signal Strength Indicator (RSSI) are of great benefits since they reuse the existing radio wireless infrastructures for indoor positioning. However, the changes in the indoor environment decrease the overall accuracy of the developed indoor positioning algorithms. To cope with the challenge of environmental dependency in indoor positioning, a robust algorithm using radio signal identification was developed. The algorithm uses circle expansion and reduction mechanism to achieve better RSSI-Distance relationship. The distances from RSSI-Distance relationship are used in trilateration algorithm for position estimation. Experiments were performed to compare position accuracy of the basic RSSI-Based and the proposed algorithm. Simulation results showed that proposed algorithm showed less average positioning errors by 11.2066% and 3.7279% at path loss coefficients of 3.11 and 3.21, respectively compared to the existing algorithms. Likewise, the proposed algorithm showed 2.7282% increase in positioning error when environment was changed from that of path loss coefficient 3.11 to 3.21. The existing basic algorithms show error fluctuation of 10% with the same environment changes. Keywords: Indoor Positioning System; RFID; RSSI; Trilateration

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Novel RFID and ontology based home localization system for misplaced objects

Cited by 36 publications

References 20 publications

Bluetooth Controlled Car

Bluetooth Controlled Car

Collaborative Indoor Positioning Systems: A Systematic Review

Robust Trilateration Based Algorithm for Indoor Positioning Systems

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