An indoor localization system considering channel interference and the reliability of the RSSI measurement to enhance location accuracy

Iterative localization algorithms are critical part in the control of mobile autonomous robots because they feed fundamental position information to the robots. In a harsh unknown environment, the estimation of environmental noise is hardly obtained during the movement of the robots. It means that the state-of-the-art methods, which increase localization accuracy using error management, are unsuitable. In this paper, we deduced an upper bound of the localization error without knowing the precise model of environment noise when the anchor nodes have position errors. Utilizing the minimum upper bound, we can construct an optimal localization linear system of iterative localization algorithms based on least square. An algorithm of generating localization linear system is proposed by using the minimum upper bound. The algorithm reduces the impact of the shortage of environmental information on localization error propagation. Our simulation results show that the algorithm is insensitive to noise and can improve the localization accuracy by constructing a proper localization linear system with a high probability.

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“…The nodes are numbered by their orders. The fourth node is a pseudo-anchor whose coordinate is (25,25). The fifth node is a pseudo-anchor.…”

Section: Simulation and Discussionmentioning

confidence: 99%

“…According to the radio propagation model of distance-dependent path loss with lognormal fading [25], we have:…”

Section: Error Upper Boundary Of Lls-rssmentioning

confidence: 99%

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Linear system construction of multilateration based on error propagation estimation

Zhang

Gao

et al. 2016

J Wireless Com Network

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“…Table 3 and Table 4 list the performance comparison of 1-D CNN architectures using RSS and CSI, respectively. As shown in Table 3, the first architecture uses two hidden convolution blocks (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32) with kernel size of 26. The next four architectures utilize three hidden convolution blocks (16-32-32) with different kernel sizes and different number of filters.…”

Section: Selection Of Dnn Architecturesmentioning

confidence: 99%

“…For example, the RADAR system [15] first builds a fingerprint map by gathering the WiFi RSS measurements at different locations in the offline training phase, and then estimates the location by minimizing the Euclidean distance between online RSS measurements and fingerprints in the radio map during the online test phase. The work in [16] considers channel interference and the reliability of the RSS measurement and proposes a pre-processing and a post-processing scheme to improve localization accuracy. Reference [17] presents a novel scheme of fingerprint generation, representation and matching, which significantly outperforms existing approaches.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Indoor Localization Using Received Signal Strength and Channel State Information

2019

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Indoor localization has received wide attention recently due to the potential use of wide range of intelligent services. This paper presents a deep learning-based approach for indoor localization by utilizing transmission channel quality metrics, including received signal strength (RSS) and channel state information (CSI). We partition a rectangular room plane into two-dimensional blocks. Each block is regarded as a class, and we formulate the localization as a classification problem. Using RSS and CSI, we develop four deep neural networks implemented with multi-layer perceptron (MLP) and one-dimensional convolutional neural network (1D-CNN) to estimate the location of a subject in a room. The experimental results indicate that the 1D-CNN using CSI information achieves excellent localization performance with much lower network complexity. INDEX TERMS Indoor localization, deep learning, convolutional neural network (CNN), received signal strength (RSS), channel state information (CSI).

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Security and Privacy for the Internet of Medical Things Enabled Healthcare Systems: A Survey

2019

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With the increasing demands on quality healthcare and the raising cost of care, pervasive healthcare is considered as a technological solutions to address the global health issues. In particular, the recent advances in Internet of Things have led to the development of Internet of Medical Things (IoMT). Although such low cost and pervasive sensing devices could potentially transform the current reactive care to preventative care, the security and privacy issues of such sensing system are often overlooked. As the medical devices capture and process very sensitive personal health data, the devices and their associated communications have to be very secured to protect the user's privacy. However, the miniaturized IoMT devices have very limited computation power and fairly limited security schemes can be implemented in such devices. In addition, with the widespread use of IoMT devices, managing and ensuring the security of IoMT systems are very challenging and which are the major issues hindering the adoption of IoMT for clinical applications. In this paper, the security and privacy challenges, requirements, threats, and future research directions in the domain of IoMT are reviewed providing a general overview of the state-of-the-art approaches.

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An indoor localization system considering channel interference and the reliability of the RSSI measurement to enhance location accuracy

Cited by 9 publications

References 9 publications

Linear system construction of multilateration based on error propagation estimation

Linear system construction of multilateration based on error propagation estimation

Deep Learning-Based Indoor Localization Using Received Signal Strength and Channel State Information

Security and Privacy for the Internet of Medical Things Enabled Healthcare Systems: A Survey

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