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Academic literature on the topic 'Link Quality indicator (LQI)'

Author: Grafiati
Published: 4 June 2021
Last updated: 3 February 2022

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Journal articles on the topic "Link Quality indicator (LQI)"

1

Ning, Xuan Jie, Hai Zhao, Mao Fan Yang, and Hua Feng Chai. "A Link Evaluation Method Employing Statistical Means of Received Signal Strength Indicator and Link Quality Indicator for Wireless Sensor Networks." Applied Mechanics and Materials 470 (December 2013): 722–28. http://dx.doi.org/10.4028/www.scientific.net/amm.470.722.

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This paper is concerned with a wireless receiving link evaluation method using statistical means of received signal strength indicator (RSSI) and link quality indicator (LQI) based on the IEEE 802.15.4 protocol for wireless sensor networks. Traditional methods using single RSSI and single LQI based on the IEEE 802.11 protocol have the disadvantage of the inaccurate evaluation. In this paper, we carry out a quantitative emulation experiment via computing statistical means of RSSI and LQI based on wireless sensor networks protocol of IEEE 802.15.4. Tested numerical values are analyzed using MATLAB and SPSS by defining the wireless link evaluation sensitivity. Result curves of RSSI to packet reception rate (PRR) and LQI to PRR we finally derive are shown that statistical means of RSSI and LQI can obtain the status information of receiving links more accurately, compared with the traditional wireless link evaluation using single RSSI and single LQI.
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2

Liu, Wei, Yu Xia, Daqing Zheng, Jian Xie, Rong Luo, and Shunren Hu. "Environmental Impacts on Hardware-Based Link Quality Estimators in Wireless Sensor Networks." Sensors 20, no. 18 (2020): 5327. http://dx.doi.org/10.3390/s20185327.

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Hardware-based link quality estimators (LQEs) in wireless sensor networks generally use physical layer parameters to estimate packet reception ratio, which has advantages of high agility and low overhead. However, many existing studies didn’t consider the impacts of environmental changes on the applicability of these estimators. This paper compares the performance of typical hardware-based LQEs in different environments. Meanwhile, aiming at the problematic Signal-to-Noise Ratio (SNR) calculation used in existing studies, a more reasonable calculation method is proposed. The results show that it is not accurate to estimate the packet reception rate using the communication distance, and it may be useless when the environment changes. Meanwhile, the fluctuation range of the Received Signal Strength Indicator (RSSI) and SNR will be affected and that of Link Quality Indicator (LQI) is almost unchanged. The performance of RSSI based LQEs may degrade when the environment changes. Fortunately, this degradation is mainly caused by the change of background noise, which could be compensated conveniently. The best environmental adaptability is gained by LQI and SNR based LQEs, as they are almost unaffected when the environment changes. Moreover, LQI based LQEs are more accurate than SNR based ones in the transitional region. Nevertheless, compared with SNR, the fluctuation range of LQI is much larger, which needs a larger smoothing window to converge. In addition, the calculation of LQI is typically vendor-specific. Therefore, the tradeoff between accuracy, agility, and convenience should be considered in practice.
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3

Li, Jie, Yang Pan, Shijian Ni, and Feng Wang. "A Hybrid Reliable Routing Algorithm Based on LQI and PRR in Industrial Wireless Networks." Wireless Communications and Mobile Computing 2021 (September 4, 2021): 1–16. http://dx.doi.org/10.1155/2021/6039900.

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In Industrial Wireless Networks (IWNs), the communication through Machine-to-Machine (M2M) is often affected by the noise in the industrial environment, which leads to the decline of communication reliability. In this paper, we investigate how to improve route stability through M2M in an industrial environment. We first compare different link quality estimations, such as Signal-Noise Ratio (SNR), Received Signal Strength Indicator (RSSI), Link Quality Indicator (LQI), Packet Reception Ratio (PRR), and Expected Transmission Count (ETX). We then propose a link quality estimation combining LQI and PRR. Finally, we propose a Hybrid Link Quality Estimation-Based Reliable Routing (HLQEBRR) algorithm for IWNs, with the object of maximizing link stability. In addition, HLQEBRR provides a recovery mechanism to detect node failure, which improves the speed and accuracy of node recovery. OMNeT++-based simulation results demonstrate that our HLQEBRR algorithm significantly outperforms the Collection Tree Protocol (CTP) algorithm in terms of end-to-end transmission delay and packet loss ratio, and the HLQEBRR algorithm achieves higher reliability at a small additional cost.
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4

Zhang, Jie, Wu Jun Yao, and Hai Bin Yang. "An Adaptive Error Control Scheme in Wireless Sensor Networks Based on LQI." Applied Mechanics and Materials 263-266 (December 2012): 915–19. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.915.

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Aiming at the character of high bit error rate and energy constraints on WSN, this paper proposed an adaptive error control scheme based on link quality indicator(LQI). The PHY specification of IEEE802.15.4 provided accurate measurement of channel quality for WSN, according to the quantitative relationship between LQI and bit error rate, this paper divided the channel quality into eight levels non-uniformly, furthermore, eight different BCH code were chosen correspondingly. The motes choose optimal BCH code as its error correction scheme in real time. Experimental results show the scheme is high in energy efficiency, meanwhile, drops the error probability effectively.
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5

Bronk, Krzysztof, Adam Lipka, and Rafał Niski. "Link Quality Assessment Algorithm for Heterogeneous Self-organizing Maritime Communications Network." Journal of Telecommunications and Information Technology 2 (June 29, 2018): 32–40. http://dx.doi.org/10.26636/jtit.2018.121217.

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The article introduces a method of performing a radio link quality assessment based on the Link Quality Indicator (LQI) which will be calculated for every system that is available. The method presented has been developed during the netBaltic project completed in Poland and generally applies to the so-called maritime zone A, i.e. the sea area where ships are still within the range of shore-based radio communication systems, particularly 3G/LTE cellular networks. The algorithm was developed based on the results of measurements obtained during two separate campaigns. That measurement data served as a basis for the method’s initial assumptions and was utilized during the method’s verification.
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6

El Madani, Bouchra, Anne Paule Yao, and Abdelouahid Lyhyaoui. "Combining Kalman Filtering with ZigBee Protocol to Improve Localization in Wireless Sensor Network." ISRN Sensor Networks 2013 (March 21, 2013): 1–7. http://dx.doi.org/10.1155/2013/252056.

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We propose a low-cost and low-power-consumption localization scheme for ZigBee-based wireless sensor networks (WSNs). Our design is based on the link quality indicator (LQI)—a standard feature of the ZigBee protocol—for ranging and the ratiometric vector iteration (RVI)—a light-weight distributed algorithm—modified to work with LQI measurements. To improve performance and quality of this system, we propose three main ideas: a cooperative approach, a coefficient delta () to regulate the speed of convergence of the algorithm, and finally the filtering process with the extended Kalman filter. The results of experiment simulations show acceptable localization performance and illustrate the accuracy of this method.
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7

Joana Halder, Sharly, and Wooju Kim. "A Fusion Approach of RSSI and LQI for Indoor Localization System Using Adaptive Smoothers." Journal of Computer Networks and Communications 2012 (2012): 1–10. http://dx.doi.org/10.1155/2012/790374.

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Due to the ease of development and inexpensiveness, indoor localization systems are getting a significant attention but, with recent advancement in context and location aware technologies, the solutions for indoor tracking and localization had become more critical. Ranging methods play a basic role in the localization system, in which received signal strength indicator- (RSSI-) based ranging technique gets the most attraction. To predict the position of an unknown node, RSSI measurement is an easy and reliable method for distance estimation. In indoor environments, the accuracy of the RSSI-based localization method is affected by strong variation, specially often containing substantial amounts of metal and other such reflective materials that affect the propagation of radio-frequency signals in nontrivial ways, causing multipath effects, dead spots, noise, and interference. This paper proposes an adaptive smoother based location and tracking algorithm for indoor positioning by making fusion of RSSI and link quality indicator (LQI), which is particularly well suited to support context aware computing. The experimental results showed that the proposed mathematical method can reduce the average error around 25%, and it is always better than the other existing interference avoidance algorithms.
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8

Li, Xiaoyu, Osamu Yoshie, and Daoping Huang. "A passive method for privacy protection in the perceptual layer of IoTs." International Journal of Pervasive Computing and Communications 13, no. 2 (2017): 194–210. http://dx.doi.org/10.1108/ijpcc-03-2017-0025.

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Purpose The purpose of this paper is to detect the existence of unknown wireless devices which could result negative means to the privacy. The perceptual layer of internet of things (IoTs) suffers the most significant privacy disclosing because of limited hardware resources, huge quantity and wide varieties of sensing equipment. Determining whether there are unknown wireless devices in the communicating environment is an effective method to implement the privacy protection for the perceptual layer of IoTs. Design/methodology/approach The authors use horizontal hierarchy slicing (HHS) algorithm to extract the morphology feature of signals. Meanwhile, partitioning around medoids algorithm is used to cluster the HHS curves and agglomerative hierarchical clustering algorithm is utilized to distinguish final results. Link quality indicator (LQI) data are chosen as the network parameters in this research. Findings Nowadays data encryption and anonymization are the most common methods to protect private information for the perceptual layer of IoTs. However, these efforts are ineffective to avoid privacy disclosure if the communication environment exists unknown wireless nodes which could be malicious devices. How to detect these unknown wireless devices in the communication environment is a valuable topic in the further research. Originality/value The authors derive an innovative and passive unknown wireless devices detection method based on the mathematical morphology and machine learning algorithms to detect the existence of unknown wireless devices which could result negative means to the privacy. The simulation results show their effectiveness in privacy protection.
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9

Luo, Jian, Liu Yu, Dafang Zhang, Zhen Xia, and Wei Chen. "A New Link Quality Estimation Mechanism Based on LQI in WSN." Information Technology Journal 12, no. 8 (2013): 1626–31. http://dx.doi.org/10.3923/itj.2013.1626.1631.

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10

Jia, Chenhao, Linlan Liu, Xiaole Gu, and Manlan Liu. "A novel link quality prediction algorithm for wireless sensor networks." Computer Science and Information Systems 14, no. 3 (2017): 719–34. http://dx.doi.org/10.2298/csis161220025j.

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Ahead knowledge of link quality can reduce the energy consumption of wireless sensor networks. In this paper, we propose a cloud reasoning-based link quality prediction algorithm for wireless sensor networks. A large number of link quality samples are collected from different scenarios, and their RSSI, LQI, SNR and PRR parameters are classified by a self-adaptive Gaussian cloud transformation algorithm. Taking the limitation of nodes? resources into consideration, the Apriori algorithm is applied to determine association rules between physical layer and link layer parameters. A cloud reasoning algorithm that considers both short- and long-term time dimensions and current and historical cloud models is then proposed to predict link quality. Compared with the existing window mean exponentially weighted method, the proposed algorithm captures link changes more accurately, facilitating more stable prediction of link quality
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