Localization Based on RSSI Exploiting Gaussian and Averaging Filter in Wireless Sensor Network

Mahapatra, Ranjan Kumar; Shet, N. Shekar V.

doi:10.1007/s13369-017-2826-2

Cited by 33 publications

(15 citation statements)

References 38 publications

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“…The propagation of the radio signal is often accompanied by a loss of energy intensity, and the loss of energy intensity and the propagation distance of the radio signal conform to the corresponding mathematical relationship. After the mobile terminal measures the strength of the radio signal emitted by the access point, the radio strength is further converted into communication distance by utilizing the path loss model [13], [15], [17], [23]. The strength of the radio signal declines as the propagated distance increases in the process of transmission, and the relationship between the communication distance and the radio signal strength can be expressed as:…”

Section: Self-adaptive Dynamic Ranging Modelmentioning

confidence: 99%

“…However, due to the severely complicated indoor environment, wireless signals are vulnerable to sudden impact in the process of transmission. Especially with the increase of transmission distance, the possibility of multipath interference becomes greater [15]. In addition, the related paper indicated that in the RSSI-based positioning system, the RSSI values are reliable only when the mobile terminal is near the access point.…”

Section: International Journal Of Computer and Communication Engineeringmentioning

confidence: 99%

“…Consequently, no additional software or hardware manipulation is required for WLAN infrastructures to implement positioning. Additionally, in the actual application process, RSSI-based WLAN localization technology also possesses the features of high-cost performance, strong expansibility and low energy consumption [4], [9], [12], which means that researchers have increasingly supported the use of indoor localization systems based on RSSI [13]- [15]. The literature has reported that the RSSI-based positioning accuracy under the current WLAN environment is maintained approximately within 2-5 m, [4] whereas in practice, most location-based services have higher requirements for accuracy and real-time performance [16], [17].…”

Section: Introductionmentioning

confidence: 99%

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Self-Adaptive Dynamic Ranging Model-Based Real-Time Hybrid Algorithm for Accurate Indoor Localization

Jiang¹,

Huang²,

Xiao³

et al. 2020

IJCCE

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The reliability of location information still maintains a crucial impact on restricting the development of location based services in indoor environment. However, in wireless local area network, received signal strength indicator (RSSI) is prone to be interfered by indoor complex environment, resulting in low accuracy and instability of real-time positioning. Here, the new self-adaptive dynamic ranging model-based real-time hybrid algorithm was proposed to realize accurate and undisturbed localization in indoor scenes. A self-adaptive dynamic ranging model was initially constructed to update the environmental parameters and correct the ranging values of mobile terminals in real time. Based on this model, a hybrid KNN algorithm and a hybrid Bayesian algorithm were severally presented. Location fingerprint database and real-time RSSI data of test points were then obtained through data acquisition. Finally, the acquired data was further used to verify the two hybrid algorithms proposed, and compared with the results of several conventional algorithms. As a result, the stability and accuracy of dual hybrid algorithms were better than those of the traditional ones. The range of average location error of both hybrid algorithms maintained 1.26-1.38 m, which was significantly lower than the error level of 2-5 m under the current WLAN environment. This newly proposed hybrid algorithm could effectively improve the stability and accuracy of indoor localization with real-time positioning algorithm, providing a promising solution for RSSI-based indoor positioning system.

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Section: Self-adaptive Dynamic Ranging Modelmentioning

confidence: 99%

Section: International Journal Of Computer and Communication Engineeringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Self-Adaptive Dynamic Ranging Model-Based Real-Time Hybrid Algorithm for Accurate Indoor Localization

Jiang¹,

Huang²,

Xiao³

et al. 2020

IJCCE

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“…In [52], a localization method is proposed to adopt assistant nodes with similar RSS sequences as auxiliary nodes to implement the accurate position. In [53]- [55], fingerprints can be constructed based on the Voronoi diagram according to the signal propagation model [56] and the signal attenuation parameter [57]. Reference [58] applies the surface fitting technique to construct RSS spatial distribution functions.…”

Section: Introductionmentioning

confidence: 99%

Progressive RSS Data Augmenter With Conditional Adversarial Networks

et al. 2020

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Accuracies of most fingerprinting approaches for WiFi-based indoor localization applications are affected by the qualities of fingerprint databases, which are time-consuming and labor-intensive. Recently, many methods have been proposed to reduce the localization accuracy reliance on the qualities of the established fingerprint databases. However, studies on establishing fingerprint databases are relatively rare under the condition of sparse reference points. In this paper, we propose a novel data augmenter based on the adversarial networks to build fingerprint databases with sparse reference points. Additionally, two conditions of these networks are designed to generate data effectively and stably, which are 0-1 sketch and Gaussian sketch. Based on the networks, we design two augmenters with different cyclic training strategies to evaluate the augmenting effects comparatively. Meanwhile, five quantitative evaluation metrics of the augmenters are proposed from two perspectives of the artificial experiences and the data features, and some of them are also used as the gradient penalties for generators. Finally, experiments corresponding to these metrics and localization accuracies demonstrate that the data augmenter with the 0-1 sketch adversarial network is more efficient, effective and stable totally. INDEX TERMS CGAN, sketch, quantitative evaluation metrics, RSS.

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“…The problem can be classified by three main methods that can be employed for the tracking application according to the type of physical measurement read at sensors. The main types of measurements are the time delay of arrival (TDOA) [7,8], the direction of arrival (DOA) [9,10], and the received signal strength (RSS) [11][12][13][14][15][16][17][18][19] of the signal generated from the tracked target, respectively. RSS measurement of the sensors represents received energy or power emitted by the target.…”

Section: Introductionmentioning

confidence: 99%

Tracking by Risky Particle Filtering over Sensor Networks

Lim

Park

2020

Sensors

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The system of wireless sensor networks is high of interest due to a large number of demanded applications, such as the Internet of Things (IoT). The positioning of targets is one of crucial problems in wireless sensor networks. Particularly, in this paper, we propose minimax particle filtering (PF) for tracking a target in wireless sensor networks where multiple-RSS-measurements of received signal strength (RSS) are available at networked-sensors. The minimax PF adopts the maximum risk when computing the weights of particles, which results in the decreased variance of the weights and the immunity against the degeneracy problem of generic PF. Via the proposed approach, we can obtain improved tracking performance beyond the asymptotic-optimal performance of PF from a probabilistic perspective. We show the validity of the employed strategy in the applications of various PF variants, such as auxiliary-PF (APF), regularized-PF (RPF), Kullback–Leibler divergence-PF (KLDPF), and Gaussian-PF (GPF), besides the standard PF (SPF) in the problem of tracking a target in wireless sensor networks.

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Localization Based on RSSI Exploiting Gaussian and Averaging Filter in Wireless Sensor Network

Cited by 33 publications

References 38 publications

Self-Adaptive Dynamic Ranging Model-Based Real-Time Hybrid Algorithm for Accurate Indoor Localization

Self-Adaptive Dynamic Ranging Model-Based Real-Time Hybrid Algorithm for Accurate Indoor Localization

Progressive RSS Data Augmenter With Conditional Adversarial Networks

Tracking by Risky Particle Filtering over Sensor Networks

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