Efficient Object Localization Using Sparsely Distributed Passive RFID Tags

Yang, Po; Wu, Wenyan; Moniri, M.; Chibelushi, Claude C.

doi:10.1109/tie.2012.2230596

Cited by 170 publications

(85 citation statements)

References 27 publications

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“…Recently, new technologies and concepts such as the 'Internet of Things' [3] (IoT) have been emerging as new tools that transform healthcare industry. In the early 21 st century, Radio Frequency Identification (RFID) technology [4][5][6] is regarded as the new IoT solution to solve many healthcare challenges. In the last few 5 years, advance in wearable technology have promoted renewed interests in IoT enabled healthcare fields.…”

Section: Introductionmentioning

confidence: 99%

Improving the Validity of Lifelogging Physical Activity Measures in an Internet of Things Environment

Yang

Hanneghan

et al. 2015

2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable,

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Abstract-Recently, the popular use of wearable devices and mobile apps makes the effectively capture of lifelogging physical activity data in an Internet of Things (IoT) environment possible. The effective collection of measures of physical activity in the long term is beneficial to interdisciplinary healthcare research and collaboration from clinicians, researchers to patients. However, due to heterogeneity of connected devices and rapid change of diverse life patterns in an IoT environment, lifelogging physical activity information captured by mobile devices usually contains much uncertainty. In this paper, we provide a comprehensive review of existing life-logging physical activity measurement devices, and identify regular and irregular uncertainties of these activity measures in an IoT environment. We then project the distribution of irregular uncertainty by defining a walking speed related score named as Daily Activity in Physical Space (DAPS). Finally, we present an ellipse fitting model based validity improvement method for reducing uncertainties of life-logging physical activity measures in an IoT environment. The experimental results reflect that the proposed method effectively improves the validity of physical activity measures in a healthcare platform.

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Section: Introductionmentioning

confidence: 99%

Improving the Validity of Lifelogging Physical Activity Measures in an Internet of Things Environment

Yang

Hanneghan

et al. 2015

2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable,

Self Cite

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“…As a promising low-cost technology, RFID is widely utilized in various applications ranging from inventory control [17] [19], supply chain management [11] to tracking/location [14] [29]. A standard RFID system has two types of devices: a set of RFID tags and one or multiple RFID readers (simply called tags and readers).…”

Section: A Context and Motivationmentioning

confidence: 99%

From Static to Dynamic Tag Population Estimation: An Extended Kalman Filter Perspective

Zhang²,

Wang

2016

IEEE Trans. Commun.

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Tag population estimation has recently attracted significant research attention due to its paramount importance on a variety of radio frequency identification (RFID) applications. However, most, if not all, of existing estimation mechanisms are proposed for the static case where tag population remains constant during the estimation process, thus leaving the more challenging dynamic case unaddressed, despite the fundamental importance of the latter case on both theoretical analysis and practical application.In order to bridge this gap, we devote this paper to designing a generic framework of stable and accurate tag population estimation schemes based on Kalman filter for both static and dynamic RFID systems. Technically, we first model the dynamics of RFID systems as discrete stochastic processes and leverage the techniques in extended Kalman filter (EKF) and cumulative sum control chart (CUSUM) to estimate tag population for both static and dynamic systems. By employing Lyapunov drift analysis, we mathematically characterise the performance of the proposed framework in terms of estimation accuracy and convergence speed by deriving the closed-form conditions on the design parameters under which our scheme can stabilise around the real population size with bounded relative estimation error that tends to zero with exponential convergence rate. Index TermsRFID, tag population estimation, extended Kalman filtre, stochastic stability.

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“…In order to provide positioning and navigation in the indoor environment, various methods based on different technologies such as WSN-based networks [3,4], WIFI network [5,6], and RFID localization technology [7,8] have been proposed and developed. Among various indoor localization schemes, RFID technology has obtained more and more interest in localization systems development for its low cost, easy deployment, and successful utility in harsh environments in recent years [9].…”

Section: Introductionmentioning

confidence: 99%

RFID 3D-LANDMARC Localization Algorithm Based on Quantum Particle Swarm Optimization

Deng

Chen

2018

Electronics

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Location information is crucial in various location-based applications, the nodes in location system are often deployed in the 3D scenario in particle, so that localization algorithms in a three-dimensional space are necessary. The existing RFID three-dimensional (3D) localization technology based on the LANDMARC localization algorithm is widely used because of its low complexity, but its localization accuracy is low. In this paper, we proposed an improved 3D LANDMARC indoor localization algorithm to increase the localization accuracy. Firstly, we use the advantages of the RBF neural network in data fitting to pre-process the acquired signal and study the wireless signal transmission loss model to improve localization accuracy of the LANDMARC algorithm. With the purpose of solving the adaptive problem in the LANDMARC localization algorithm, we introduce the quantum particle swarm optimization (QPSO) algorithm, which has the technology advantages of global search and optimization, to solve the localization model. Experimental results have shown that the proposed algorithm improves the localization accuracy and adaptability significantly, compared with the basic LANDMARC algorithm and particle swarm optimization LANDMARC algorithm, and it can overcome the shortcoming of slow convergence existed in particle swarm optimization.

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Efficient Object Localization Using Sparsely Distributed Passive RFID Tags

Abstract: This is a repository copy of Efficient object localization using sparsely distributed passive RFID tags.

Cited by 170 publications

References 27 publications

Improving the Validity of Lifelogging Physical Activity Measures in an Internet of Things Environment

Improving the Validity of Lifelogging Physical Activity Measures in an Internet of Things Environment

From Static to Dynamic Tag Population Estimation: An Extended Kalman Filter Perspective

RFID 3D-LANDMARC Localization Algorithm Based on Quantum Particle Swarm Optimization

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