Mathematical model for a collaborative indoor position system (IPS) and movement detection of devices within IoT environment

Vasconcelos, D. R.; Pimentel, F. L. R.; Andrade, Rossana M. C.; Souza, José Neuman de

doi:10.1145/3019612.3019731

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…With the advancements in artificial intelligence and computer science, the desire for location positioning and indoor navigation is also increasing. On account of the continuous development of outdoor positioning Technological development and its increasingly perfect functions [1] , the Global Positioning System (GPS) has been extensively utilized and developed [2] . However, due to PS signal attenuation, software incompatibility, and other reasons, it has limitations in the application of indoor positioning, and indoor positioning technology requires the continuity and universality of location information services.…”

Section: Introductionmentioning

confidence: 99%

DOA method based on CSI and improved neural network

Sun

2024

J. Phys.: Conf. Ser.

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In wireless communication systems, accurate estimation of signal direction has always been a key research area. Channel State Information (CSI) is an important parameter that can be used to estimate the Direction of Arrival (DOA) of a signal. In order to improve the accuracy and robustness of DOA estimation, this study aims to explore a DOA estimation method based on CSI and improved neural networks. Firstly, the role of CSI in localization technology and related theories are detailed. Subsequently, the application of neural network-based fingerprint matching algorithms, including BP neural networks, Whale Optimization Algorithm (WOA), and chaotic mapping techniques, is analyzed. In light of this, an improved BP neural network integrating chaotic mapping and adaptive weighting is proposed to improve the positioning accuracy and convergence rate of the algorithm. Finally, through the collection and training of experimental data, an algorithm proposed in this article achieves a test set accuracy of 96.4286% and a training set accuracy of 82.7243%. The better training effect is obtained, which verifies the efficacy and superiority of the proposed approach.

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Section: Introductionmentioning

confidence: 99%

DOA method based on CSI and improved neural network

Sun

2024

J. Phys.: Conf. Ser.

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“…By means of a local network or Internet communication technology, we can connect sensors, controllers, machines, persons and objects through IoT technology to obtain information, for remote management, control and services. At present, object location awareness is a very important part of the IoT’s application, but it cannot obtain this through the traditional satellite and cellular location with GPS in indoor environments [ 2 , 3 ]. In order to implement indoor positioning, there are many different technologies, such as WiFi [ 4 ], UWB [ 5 ], Radio Frequency Identification (RFID) [ 6 ], and so on.…”

Section: Introductionmentioning

confidence: 99%

An RFID Indoor Positioning Algorithm Based on Support Vector Regression

et al. 2018

Sensors

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Nowadays, location-based services, which include services to identify the location of a person or an object, have many uses in social life. Though traditional GPS positioning can provide high quality positioning services in outdoor environments, due to the shielding of buildings and the interference of indoor environments, researchers and enterprises have paid more attention to how to perform high precision indoor positioning. There are many indoor positioning technologies, such as WiFi, Bluetooth, UWB and RFID. RFID positioning technology is favored by researchers because of its lower cost and higher accuracy. One of the methods that is applied to indoor positioning is the LANDMARC algorithm, which uses RFID tags and readers to implement an Indoor Positioning System (IPS). However, the accuracy of the LANDMARC positioning algorithm relies on the density of reference tags and the performance of RFID readers. In this paper, we introduce the weighted path length and support vector regression algorithm to improve the positioning precision of LANDMARC. The results show that the proposed algorithm is effective.

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