An Improved Monte Carlo Localization Algorithm for Mobile Wireless Sensor Networks

Luan, Jingye; Zhang, Ruida; Zhang, Baihai; Cui, Lingguo

doi:10.1109/iscid.2014.217

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…A framework can only be designed by combining all of the techniques that can serve for the different applications. Recent research on localization algorithms in WSNs has appeared in (Luan et al, 2014;Cao, 2011;Bianchi et al, 2018). Here, we highlight the more popular localization techniques and range-free algorithms of localization, including the mobile reference nodes.…”

Section: Localization Related Workmentioning

confidence: 99%

Localization in Wireless Sensor Networks for Accurate Event Detection

Sharma

Singh

2021

International Journal of Healthcare Information Systems and Informatics

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Event detection at its initial stage is considerably most demanding and more importantly challenging to reduce the causes and damages. The GPS-enabled sensor nodes are possibly a solution for the location estimation, but having GPS receiver in each sensor node makes the network costly. In this paper, the authors have presented a UNL, unknown node localization, method for the estimation of sensor location. The proposed method is based on RSSI, and there is no requirement of extra hardware and communication of data among the sensor nodes. The experiments are conducted in order to investigate the localization accuracy of UNL method, and they analyzed that the proposed method is simple as there is less computation and communication overhead. The proposed algorithm is further compared with other existing localization methods for the accurate estimation of unknown nodes. The experimental results show the effectiveness of the algorithm and its capability for locating the unknown nodes in a network more accurately.

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Section: Localization Related Workmentioning

confidence: 99%

Localization in Wireless Sensor Networks for Accurate Event Detection

Sharma

Singh

2021

International Journal of Healthcare Information Systems and Informatics

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“…Localization accuracy can be improved by combining SMC schemes and the genetic algorithm as in Genetic and Weighting Monte Carlo Localization (GWMCL) [83]. Crossover and mutation can be used to draw samples from a virtual anchor node.…”

Section: State-of-the-art Smc Localization Schemesmentioning

confidence: 99%

“…Storing posterior location information of estimated location, velocity, direction, and motion type in the table needs more memory and can slow the localization process in SMCLA [78]. Another localization scheme is genetic algorithm implemented in SMC technique to filter out invalid samples [83]; the genetic algorithm requires large data and high execution time, so it is unsuitable for thin devices like sensor node.…”

Section: Comparison Of the Localization Accuracymentioning

confidence: 99%

Sequential Monte Carlo Localization Methods in Mobile Wireless Sensor Networks: A Review

et al. 2017

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The advancement of digital technology has increased the deployment of wireless sensor networks (WSNs) in our daily life. However, locating sensor nodes is a challenging task in WSNs. Sensing data without an accurate location is worthless, especially in critical applications. The pioneering technique in range-free localization schemes is a sequential Monte Carlo (SMC) method, which utilizes network connectivity to estimate sensor location without additional hardware. This study presents a comprehensive survey of state-of-the-art SMC localization schemes. We present the schemes as a thematic taxonomy of localization operation in SMC. Moreover, the critical characteristics of each existing scheme are analyzed to identify its advantages and disadvantages. The similarities and differences of each scheme are investigated on the basis of significant parameters, namely, localization accuracy, computational cost, communication cost, and number of samples. We discuss the challenges and direction of the future research work for each parameter.

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“…However, the MCB still has a large and fuzzy sampling point set, and there is also much room for improvement in the prediction of the motion direction of the node. In addition to the above improved strategies, there are many researches on the localization algorithm of wireless mobile sensor networks based on Monte Carlo localization algorithm, such as adaptive weight [14], virtual anchor node [15], model prediction [16], fusion posture estimation [17] etc. Aiming at the problem of MCB algorithm, this paper proposes an improved MCB for mobile sensor networks.…”

Section: Introductionmentioning

confidence: 99%

An improved MCB localization algorithm based on weighted RSSI and motion prediction

Zhou

Tian

Zhong³

2020

COMSIS J

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Aiming at the problem of low sampling efficiency and high demand for anchor node density of traditional Monte Carlo Localization Boxed algorithm, an improved algorithm based on historical anchor node information and the received signal strength indicator (RSSI) ranging weight is proposed which can effectively constrain sampling area of the node to be located. Moreover, the RSSI ranging of the surrounding anchors and the neighbor nodes is used to provide references for the position sampling weights of the nodes to be located, an improved motion model is proposed to further restrict the sampling area in direction. The simulation results show that the improved Monte Carlo Localization Boxed (IMCB) algorithm effectively improves the accuracy and efficiency of localization.

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An Improved Monte Carlo Localization Algorithm for Mobile Wireless Sensor Networks

Cited by 5 publications

References 11 publications

Localization in Wireless Sensor Networks for Accurate Event Detection

Localization in Wireless Sensor Networks for Accurate Event Detection

Sequential Monte Carlo Localization Methods in Mobile Wireless Sensor Networks: A Review

An improved MCB localization algorithm based on weighted RSSI and motion prediction

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