Research on improved localization algorithms RSSI-based in wireless sensor networks

Qiao, Jianhua; Hou, Jiwei; Gao, Jushuai; Wu, Yan

doi:10.1088/1361-6501/ac22f1

Cited by 7 publications

(3 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The SNs can impart among themselves utilizing radio transmissions [2]. For most existing utilization of WSNs, the node localization (NL) is urgent [3]. For instance, in the primary observing application, we can reason that the construction is in a bad way assuming issue is distinguished by at least one sensor in the organization of sensors mounted wherever on the design.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Aquila Optimization Algorithm-Based Node Localization Scheme for Wireless Sensor Networks

Agarwal¹,

Gokilavani²,

Nagarajan³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

In recent times, wireless sensor network (WSN) finds their suitability in several application areas, ranging from military to commercial ones. Since nodes in WSN are placed arbitrarily in the target field, node localization (NL) becomes essential where the positioning of the nodes can be determined by the aid of anchor nodes. The goal of any NL scheme is to improve the localization accuracy and reduce the localization error rate. With this motivation, this study focuses on the design of Intelligent Aquila Optimization Algorithm Based Node Localization Scheme (IAOAB-NLS) for WSN. The presented IAOAB-NLS model makes use of anchor nodes to determine proper positioning of the nodes. In addition, the IAOAB-NLS model is stimulated by the behaviour of Aquila. The IAOAB-NLS model has the ability to accomplish proper coordinate points of the nodes in the network. For guaranteeing the proficient NL process of the IAOAB-NLS model, widespread experimentation takes place to assure the betterment of the IAOAB-NLS model. The resultant values reported the effectual outcome of the IAOAB-NLS model irrespective of changing parameters in the network.

show abstract

Section: Introductionmentioning

confidence: 99%

Intelligent Aquila Optimization Algorithm-Based Node Localization Scheme for Wireless Sensor Networks

Agarwal¹,

Gokilavani²,

Nagarajan³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…The principle of ranging positioning technology is to measure the distance, time difference, or angle information between anchor points firstly, and then calculate the specific coordinates of to-be-positioned points by utilizing positioning algorithms such as trilateral measurement, triangulation, or maximum likelihood estimation. Typical ranging techniques include time of arrival (ToA), time difference of arrival (TDoA) [ 7 , 8 ], angle of arrival (AoA) [ 9 , 10 ], frequency difference of arrival (FDoA) [ 11 , 12 ], and RSSI [ 13 , 14 ]. The LDPLM model is a classic RSSI-based ranging algorithm, which realizes ranging by establishing a mapping relationship between RSSI and distance.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Calibration Algorithm Based on RSSI and LDPLM for Indoor Ranging and Positioning

Yang

Deng

Lin

et al. 2022

Sensors

View full text Add to dashboard Cite

The positioning algorithm based on received signal strength indication (RSSI) and the logarithmic distance path loss model (LDPLM) is widely used in indoor positioning scenarios due to its convenient detection and low costs. However, the classic LDPLM with fixed coefficients and fixed error estimation usually reduces the ranging accuracy, but it is rarely studied in previous literature. This study proposes an adaptive calibration ranging algorithm based on LDPLM, which consists of two parts: coefficient adaptive algorithm and error correction algorithm. The coefficient adaptive algorithm is derived by utilizing the error theory and the least squares method. The error correction algorithm is defined as the linear regression equation, in which coefficients are determined by the least squares method. In addition, to reduce the influence of RSSI’s fluctuation on ranging accuracy, we propose a simple but effective filtering algorithm based on Gaussian. The experimental results show that compared with the classic LDPLM and polynomial fitting model, the ranging accuracy of the proposed algorithm is improved by 58% and 51%, respectively, and the positioning cumulative prediction error of the proposed model is reduced by 69% and 80%, respectively.

show abstract

“…In real-world installations, relating RSSI to distance and acquiring it is difficult. Signal intensity is affected by three processes: path loss, fading, and shadowing [15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Investigation of Error Probability for Grid and Random Uniform Topologies in a Variable Noise Environment

Iskandarani

2022

Journal of Computer Networks and Communications

View full text Add to dashboard Cite

Investigation of the effect of topology on the communication efficiency of WSN nodes is carried out through simulation of 9 nodes in a 12 by 12 meters area. The obtained data and plots indicate that the grid topology is a more efficient and stable topology to use in comparison to the random uniform topology. The deduction is supported by probability of error as a function of error distribution values. Five different noise levels are used in the simulation (0dBm, −20 dBm, −40 dBm, −60 dBm, −80 dBm, and −100 dBm) with an output power of −10 dBm. The work shows that at −60 dBm redistribution of probability of error as a function of error values started to occur with higher level error values associated with the random uniform topology compared with the grid topology occurring at −60 dBm noise. The work also shows the relationship between received signal strength indicator (RSSI) and probability of error which decreases as RSSI increase in a similar manner as signal to noise ratio (SNR). Both RSSI and SNR are related through the mathematical model presented in the paper which is based on the path loss model. Common features between the error probability model and Gaussian interpolation function are also presented. A simplified 1-D design model is also presented to enable initial topology considerations. Criteria are also established to enable relating SNR, RSSI, topology, and WSN incremental position.

show abstract

Research on improved localization algorithms RSSI-based in wireless sensor networks

Cited by 7 publications

References 33 publications

Intelligent Aquila Optimization Algorithm-Based Node Localization Scheme for Wireless Sensor Networks

Intelligent Aquila Optimization Algorithm-Based Node Localization Scheme for Wireless Sensor Networks

An Adaptive Calibration Algorithm Based on RSSI and LDPLM for Indoor Ranging and Positioning

Investigation of Error Probability for Grid and Random Uniform Topologies in a Variable Noise Environment

Contact Info

Product

Resources

About