A Pragmatic Relay Placement Approach in 3-D Space and Q-Learning-Based Transmission Scheme for Reliable Factory Automation Applications

Kapil, Hem; Murthy, C. Siva Ram

doi:10.1109/jsyst.2016.2524695

Cited by 14 publications

(2 citation statements)

References 21 publications

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“…• The estimated binarized SLF can be adopted for designing wireless networks in factory environments such as relay node placement techniques [48].…”

Section: Discussionmentioning

confidence: 99%

Binary Radio Tomographic Imaging in Factory Environments Based on LOS/NLOS Identification

et al. 2023

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Radio tomographic imaging (RTI) is a technique for estimating spatial loss fields (SLFs), which maps the quantified attenuation of radio signals at every spatial location within monitored regions. In this study, we investigate RTI techniques in indoor factory environments, where the RTI techniques deteriorate because of severe multipath channels. We propose the binary radio tomographic imaging (binary RTI) method, where the attenuation level of each pixel in a SLF is defined as a binary value. The binary RTI method is suited for factory environments, including metallic objects, because radio signals are almost fully reflected rather than getting absorbed by such objects. In the proposed method, we suppose that transmitted signals are modulated with an orthogonal frequency division multiplexing (OFDM) format, and each receiver is equipped with multiple antenna elements. By adopting the two-dimensional multiple signal classification (MUSIC), the proposed method identifies whether the signals are transmitted in a lineof-sight (LOS) or a non-line-of-sight (NLOS) path. From the LOS/NLOS identification, we propose two algorithms to estimate the binary SLF: a simple greedy algorithm and a relaxation algorithm with lowrank approximation. We evaluate the performance of the proposed method via simulation experiments. To assess the applicability of the proposed method to factory environments, we assume a severe multipath environment where all the objects, wall, and ceiling are perfect electrical conductors, and show that by using an appropriate threshold parameter for the LOS/NLOS identification, the proposed method can estimate the binary SLF in the test environment. INDEX TERMS factory environment, LOS/NLOS identification, radio tomographic imaging

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“…• The estimated binarized SLF can be adopted for designing wireless networks in factory environments such as relay node placement techniques [48].…”

Section: Discussionmentioning

confidence: 99%

Binary Radio Tomographic Imaging in Factory Environments Based on LOS/NLOS Identification

et al. 2023

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“…WSN is a kind of net constructing by some sensors which have possibility of perception, computing, communication, and organizing through Ad Hoc, these nodes collaborate with one another [1][2][3][4][5][6], monitor, perceive, and collect every various environmental data in real-time, we can fuse these data to get detail information, then transmit them to the users [7][8]. Comparing with traditional wireless communication network, WSN shows selforganization, low-cost, and dynamic, so that it can be deployed in the forest [9][10], desert [11], battle field [12][13], and other hostile environments, as well as intelligent home [14][15], factory [16][17], traffic [18][19], and hospital [20][21], their number can be a few to tens of thousands.…”

Section: Introductionmentioning

confidence: 99%

Number of Coherent Nodes Estimation Based on Multiple Feature Extraction in Wireless Sensor Network

Zhen

Zhang

2020

IEEE Access

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Knowing number of nodes is the precondition of their locating and estimation of other parameters in wireless sensor network, actually it can be evaluated according to the signal received by the sensor array. The popular algorithms are only appropriate for the uncorrelated signals and circumstance of Gaussian white noise, the estimation precision will deteriorate at some harsh environments. Hence a new algorithm for determining coherent node number based on multiple feature extraction in wireless sensor network is presented in this paper. First, perform spatial difference smoothing to the array data. Then the eigenvalues and eigenvectors can be acquired by eigen-decomposition, consequently multiple features which are used for training are constructed. Finally, back propagation neural network and particle swarm optimization are exploited for calculating node number. Simulation results demonstrate that the algorithms has good performances under the background of colored noise and small samples.

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Relay node placement for building wireless sensor networks with reconfigurability provision

Yang

et al. 2020

Ad Hoc Networks

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A Pragmatic Relay Placement Approach in 3-D Space and Q-Learning-Based Transmission Scheme for Reliable Factory Automation Applications

Cited by 14 publications

References 21 publications

Binary Radio Tomographic Imaging in Factory Environments Based on LOS/NLOS Identification

Binary Radio Tomographic Imaging in Factory Environments Based on LOS/NLOS Identification

Number of Coherent Nodes Estimation Based on Multiple Feature Extraction in Wireless Sensor Network

Relay node placement for building wireless sensor networks with reconfigurability provision

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