An effective signal strength-based wireless location estimation system for tracking indoor mobile users

Ng, Joseph Kee‐Yin; Lam, Kam-Yiu; Cheng, Quan Jia; Shum, Kevin Chin Yiu

doi:10.1016/j.jcss.2013.01.016

Cited by 28 publications

(11 citation statements)

References 13 publications

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“…It is important to notice that there are other research works that also aggregated several signal-strength samples, although not to find a representative signal-strength value. Some of these works used signal aggregation to perform base transceiver station (BTS) localization [ 20 ] or to estimate user location [ 14 , 21 ], mostly based on the RADAR system [ 22 ]. These studies are not that related to the problem we are referring to in this paper, which is to aggregate signal strength into a representative value to estimate the mathematical expectation of the signal strength.…”

Section: Related Workmentioning

confidence: 99%

“…Nevertheless, we did not find any discussion or argument on why to use log-scaled signal values in more recent papers. Indeed, many of these papers manipulated dBm values without mentioning the correspondence between dBm and watt values [ 13 , 15 , 20 , 21 , 27 , 36 , 37 , 38 , 39 , 40 , 41 ], and moreover, some of them manipulated signal-strength values without reporting the unit of measurement employed [ 12 , 14 , 29 , 42 , 43 ]. Many of the papers that followed these wrong methodologies got log-scaled signal-strength measurements directly from mobile operating systems (Android or iOS) [ 1 , 8 , 11 , 12 , 13 , 15 , 20 , 23 , 26 , 27 , 36 , 37 , 38 , 40 ].…”

Section: Common Pitfalls In Using Log-scaled Signal Strengthmentioning

confidence: 99%

“…In addition to the above, there are other research papers that also followed the incorrect methodology of calculating the arithmetic mean of logarithmic signal-strength values [ 12 , 17 , 20 , 21 , 36 , 37 , 38 , 40 ].…”

Section: Common Pitfalls In Using Log-scaled Signal Strengthmentioning

confidence: 99%

See 2 more Smart Citations

Improving Signal-Strength Aggregation for Mobile Crowdsourcing Scenarios

Madariaga

Bustos-Jiménez

et al. 2021

Sensors

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Due to its huge impact on the overall quality of service (QoS) of wireless networks, both academic and industrial research have actively focused on analyzing the received signal strength in areas of particular interest. In this paper, we propose the improvement of signal-strength aggregation with a special focus on Mobile Crowdsourcing scenarios by avoiding common issues related to the mishandling of log-scaled signal values, and by the proposal of a novel aggregation method based on interpolation. Our paper presents two clear contributions. First, we discuss the misuse of log-scaled signal-strength values, which is a persistent problem within the mobile computing community. We present the physical and mathematical formalities on how signal-strength values must be handled in a scientific environment. Second, we present a solution to the difficulties of aggregating signal strength in Mobile Crowdsourcing scenarios, as a low number of measurements and nonuniformity in spatial distribution. Our proposed method obtained consistently lower Root Mean Squared Error (RMSE) values than other commonly used methods at estimating the expected value of signal strength over an area. Both contributions of this paper are important for several recent pieces of research that characterize signal strength for an area of interest.

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Section: Related Workmentioning

confidence: 99%

Section: Common Pitfalls In Using Log-scaled Signal Strengthmentioning

confidence: 99%

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Improving Signal-Strength Aggregation for Mobile Crowdsourcing Scenarios

Madariaga

Bustos-Jiménez

et al. 2021

Sensors

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show abstract

“…Shang et al [14] presented a new location algorithm based on RSSI vector similarity degree and designed a location system to locate sensor nodes indoors. Ng et al [15] introduced an improved algorithm, which can track moving crowd by building an indoor positioning and tracking system. This technique was applied by Oliveira et al [16] into the relative positioning of mobile robot.…”

Section: Literature Reviewmentioning

confidence: 99%

An Improved RSSI-Based Positioning Method Using Sector Transmission Model and Distance Optimization Technique

Zhu

Alsharari

2015

International Journal of Distributed Sensor Networks

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This paper focuses on the positioning algorithm suitable for harsh indoor environment such as manufacturing workshop in which the interferences from different directions cannot be neglected. The positioning algorithm is an improved Received Signal Strength Indication-(RSSI-) based ranging method. To preprocess the RSSI data Gaussian filter and mean filter are adopted. A sector transmission model is constructed and applied to divide the area around the anchor node into several sectors, and the shadowing model of each sector is measured. Then the average of all RSSI values in a particular sector is considered as the final RSSI value for distance calculation. The calculated distances designated for trilateration are also optimized to eliminate the abnormal distances from positioning calculations. Positioning experiment is designed via ZigBee facilities. The results show that the proposed algorithm greatly improves the positioning accuracy and stability; for instance, the average positioning error is reduced from 1.32 m to 0.79 m. Moreover, this study also proves that the node position has great influence on the positioning accuracy.

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“…This is because the widespread deployment of WiFi access points (APs) enables users to obtain their locations at anytime and anywhere in public places. Various WiFi-based indoor localization schemes mainly fall into four categories: angle of arrival-based [ 1 ], time of arrival-based [ 2 ], signal propagation model-based [ 3 ], and fingerprint-based [ 4 ]. Since the fingerprint-based localization has a superior performance, it becomes the hot-pot of research.…”

Section: Introductionmentioning

confidence: 99%

An Indoor Localization System Using Residual Learning with Channel State Information

Wang

Zhang

et al. 2021

Entropy

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With the increasing demand of location-based services, neural network (NN)-based intelligent indoor localization has attracted great interest due to its high localization accuracy. However, deep NNs are usually affected by degradation and gradient vanishing. To fill this gap, we propose a novel indoor localization system, including denoising NN and residual network (ResNet), to predict the location of moving object by the channel state information (CSI). In the ResNet, to prevent overfitting, we replace all the residual blocks by the stochastic residual blocks. Specially, we explore the long-range stochastic shortcut connection (LRSSC) to solve the degradation problem and gradient vanishing. To obtain a large receptive field without losing information, we leverage the dilated convolution at the rear of the ResNet. Experimental results are presented to confirm that our system outperforms state-of-the-art methods in a representative indoor environment.

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An effective signal strength-based wireless location estimation system for tracking indoor mobile users

Cited by 28 publications

References 13 publications

Improving Signal-Strength Aggregation for Mobile Crowdsourcing Scenarios

Improving Signal-Strength Aggregation for Mobile Crowdsourcing Scenarios

An Improved RSSI-Based Positioning Method Using Sector Transmission Model and Distance Optimization Technique

An Indoor Localization System Using Residual Learning with Channel State Information

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