A Novel Indoor Positioning Algorithm for Wireless Sensor Network Based on Received Signal Strength Indicator Filtering and Improved Taylor Series Expansion

Ren, Jin; Huang, Songyang; Song, Wei; Han, Jun

doi:10.18280/ts.360113

Cited by 11 publications

(9 citation statements)

References 11 publications

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“…The basic principles of the protocol are [11,12]: 1. Routing: the path discovery is based on the quality of the signal, the density of nodes and active path is represented by its costs;…”

Section: Routing Protocol -Rplmentioning

confidence: 99%

Detection and Cooperative Communications for Deployment Sensor Networks

Benaissa¹,

Lahfa²,

Naima³

et al. 2021

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In a sensor network, and more specifically with a single-hop deployment policy, sensor measurements contain a lot of redundancy either in the measurement dimensions of a single sensor, or between the measurement dimensions of different sensors due to of the spatial correlation either in the temporal dimension of the measurements. The goal is to reduce this redundancy by deploying fewer sensors, while ensuring high measurement accuracy and maximizing service life. The proposed method minimizes the complexity in terms of communication and calculation and maximizes the lifetime of the network based on an aggregation and consensus system to reduce the spatio-temporal dimension of the data captured and consequently the number of sensors deployed. The results show a visible performance compared to the standard method of transmission on the free platform of the COOJA/Contiki simulator allowing to simulate network connections of wireless sensors and to interact with them.

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“…The basic principles of the protocol are [11,12]: 1. Routing: the path discovery is based on the quality of the signal, the density of nodes and active path is represented by its costs;…”

Section: Routing Protocol -Rplmentioning

confidence: 99%

Detection and Cooperative Communications for Deployment Sensor Networks

Benaissa¹,

Lahfa²,

Naima³

et al. 2021

View full text Add to dashboard Cite

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“…In TDOA positioning, high-precision clock synchronization is required, and the positioning error will increase when the tag is away from the hyperbolic asymptote [ 8 ]. When adapting TOF ranging to construct a set of hyperbolic equations, the Chan–Taylor method is often used to solve the tag coordinates [ 9 , 10 , 11 ], which is time-consuming in computation; alternatively, the centroid algorithm is presented to obtain the initial tag positions, and the final tag coordinates can be optimized by the Taylor iterations to improve the positioning performance [ 12 , 13 ], especially in serious noise environments. However, in the Chan–Taylor iterations, the covariance matrix of TDOA measurements is employed, thus the final tag positioning will be affected by TDOA hyperbolic properties [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Research on UWB Indoor Positioning System Based on TOF Combined Residual Weighting

Yang

Zhu

2023

Sensors

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The performance of TDOA positioning based on UWB is limited by the hyperbolic characteristics of TDOA, especially for tags away from the hyperbolic asymptote. Aiming at this problem, a new UWB indoor positioning system is proposed. Firstly, TOF ranging is adopted to build the positioning equations; then the weighted centroid algorithm of four base stations is presented to compute the initial rough position of the tag; and the residual weighting is introduced to optimize the initial tag position; then, the corresponding nonlinear positioning equations, which will be algebraically transformed to one distribution function, are solved, and the optimal tag coordinates can be obtained by the Newton iteration method. Simulation experiments have verified the positioning reliability of the proposed algorithm under different noise environments and for different tag positions.

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“…Since the proposal of the China Manufacturing 2025 strategy, the penetration of Internet of Things (IoT) in the production control of modern manufacturing workshop has prompted advanced intelligent and information-based manufacturing techniques, such as manufacturing logistics [8][9][10][11]. Combined with wireless sensor network (WSN) and swarm intelligence autonomous perception, these emerging techniques support the whole-process perception of intelligent manufacturing, and provide data support to the rational allocation of production resources and anomaly detection [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Digital Production Control of Manufacturing Workshop Based on Internet of Things

Wang¹,

Li²,

Nie³

2021

Int. j. simul. model.

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The penetration of Internet of things (IoT) in the production control of modern manufacturing workshop has prompted advanced intelligent and information-based manufacturing techniques. However, the relevant studies fail to collect and process the various manufacturing parameters in real time, and rarely consider the coordination between manufacturing workshop and machines. This paper explores the digital production control of manufacturing workshop based on the IoT. Firstly, block diagrams were drawn for IoT-based digital workshop production control and software design of digital production control system, and a multi-objective optimization model was established for IoT-based digital workshop production control, aiming to optimize delivery time, machine load, and machine utilization rate. Next, the order sequencing method was detailed for digital workshop production control. Finally, machine competency and collaborative efficiency were introduced as optimization objectives, and an IoT-based strategy was designed for collaborative allocation of sub-tasks for workshop production. Our method was proved effective through experiments.

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A Novel Indoor Positioning Algorithm for Wireless Sensor Network Based on Received Signal Strength Indicator Filtering and Improved Taylor Series Expansion

Cited by 11 publications

References 11 publications

Detection and Cooperative Communications for Deployment Sensor Networks

Detection and Cooperative Communications for Deployment Sensor Networks

Research on UWB Indoor Positioning System Based on TOF Combined Residual Weighting

Digital Production Control of Manufacturing Workshop Based on Internet of Things

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