Positioning Algorithms by Information Fusion in Wireless Sensor Networks

Tang, Liangrui; Gong, Yue; Luo, Yong; Feng, Sen; Zhao, Xiongwen

doi:10.1007/s11277-013-1305-7

Cited by 6 publications

(3 citation statements)

References 7 publications

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“…In another study [22], an adaptive distance estimation algorithm is proposed to reduce the great influence of changing environment by real-time dynamic correction to the shadowing transmission model. Finally, a fuzzy positioning algorithm based on information fusion is presented in a recent study [23], in which the fuzzy theory is used to deal with the randomness and fuzziness in the WSNs, and the information fusion is introduced to improve the localization accuracy.…”

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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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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“…Wu et al proposed a new localization algorithm which the common communication area is divided into a number of spatial grids and the confidence degree of each grid is calculated out according to the probability model of radio signal space transmission [3]. Tang et al presented positioning algorithms based on information fusion, which avoid the high complexity of computation and the requirement of more anchor nodes [4]. Sahu et al proposed an RSSI-based localization scheme that considers the trend of RSSI values obtained from beacons to estimate the position of sensor nodes [5].…”

Section: Introductionmentioning

confidence: 99%

A Novel Error Modified Method for RSSI Location Algorithm Based on Neural Network

Liang

Feng

Yao

2015

AMM

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As the geographical location of sensors is of great importance in the applications of a wireless sensor network, localization attracts significant research interest. Among the localization methods, received signal strength indicator (RSSI) localization algorithm is widely studied due to its low-cost. However, the radio signal transmission is affected by the factors of channel attenuation and noise, etc. Therefore, the actual location is impossible to estimate. To alleviate the effect of the RSSI measure errors, we proposed a novel error modified method for RSSI location algorithm based on neural network, short for RSSI-NN. First, we use the fitting parameters of the monitoring area to determine the parameters of RSSI model. Second, the distance between anchor node i and the unknown node Liis calculated. Then, the error modified algorithm based on neural network is proposed. Finally, we can obtain the modified value of the output layer data. Simulations demonstrate that RSSI-NN achieves lower localization error compared with conventional RSSI-based localization algorithm.

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“…A lot of research has been done on RSSI positioning algorithm, some scholars tend to research on the application of positioning Algorithm [1,2]; many researcher devoted to promote the positioning error in the process of algorithm, they introduce many filtering method and centroid algorithm in their paper [3]; some academic combine the ZigBee and RFID technology in their RSSI positioning algorithm, and test result is very good [4,5]; some professor introduce fuzzy control in their paper [6], and many scholars propose one adaptive distance algorithm [7] to adapt the changing environment.…”

Section: Introductionmentioning

confidence: 99%

An Improved RSSI Positioning Algorithm Based on Reference Distances

Leng

Zhu

Alsharari

et al. 2014

AMR

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Due to the fluctuation of RSSI (Received Signal Strength Indicator) value, the positioning accuracy of indoor localization algorithm that based on RSSI value is not high and unstable, RSSI filtering and reference distance used in this paper. Fluctuation amplitude of RSSI is reduced by mean filtering, and a known distance between anchor nodes introduced when calculating the unknown distance between blind node and anchor node. According to the result of experiment, the positioning accuracy of new localization algorithm can reach 1.45 meter, and it can meet the demand of indoor positioning.

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Positioning Algorithms by Information Fusion in Wireless Sensor Networks

Cited by 6 publications

References 7 publications

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

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

A Novel Error Modified Method for RSSI Location Algorithm Based on Neural Network

An Improved RSSI Positioning Algorithm Based on Reference Distances

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