Multi-hop-based Monte Carlo Localization for Mobile Sensor Networks

Yi, Jiyoung; Yang, Sung Mo; Cha, Hojung

doi:10.1109/sahcn.2007.4292828

Cited by 42 publications

(26 citation statements)

References 19 publications

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“…The location of MCL is divided into three steps: initialization, sampling and result output [7] . Among them，sampling is the core step, which is divided into three stages: prediction, filtering and importance sampling.…”

Section: Classical MCL Algorithmmentioning

confidence: 99%

“…Among them, algorithm based on signal with Time-Of-Arrival (TOA) [2] , algorithm based on different signal with Time-Difference-Of-Arrival (TDOA) [3] , algorithm based on signal with Angle-of-Arrival(AOA) [4] and algorithm based on signal with Received-Signal-Strength-Indication(RSSI) [5] , they belong to Range-Based localization algorithm. Centroid algorithm for solving polygon geometric center of gravity based on neighbor nodes [7] , algorithm based on nodes with Distance-Vector-Hop (DV-Hop) [4], Multi-Dimensional Scaling Map( MDS-MAP) algorithm [7] , these algorithms are fixed node localization algorithms and they belong to Rang-Free localization algorithm. The mobile node localization algorithm for WSN 459 was first proposed Monte Carlo location algorithm (MCL) by American Hu and Evans in 2004.…”

Section: Introductionmentioning

confidence: 99%

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Indoor Mobile Node Localization Algorithm based on RSSI and Improved MCL

Liu¹,

Fang²

2018

dtcse

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With the characteristics of mobile sensor network for dynamic change of indoor network structure and node position, in order to overcome the shortage of localization accuracy and sampling efficiency of Monte Carlo localization algorithm in wireless sensor networks (WSN), a location algorithm based on RSSI and improved Monte Carlo localization is proposed to locate indoor mobile nodes. First, the paper describes the classical MCL algorithm and the received signal intensity RSSI model, and then an improved MCL algorithm is designed. The algorithm through introducing the received signal strength indicator model of range prediction, using distance information filtering samples, and finally using the filtered samples of the weighted average to estimate the location of nodes and reducing the sampling area, improves the sampling efficiency and positioning accuracy. The simulation results show that the improved MCL algorithm based on RSSI improves the convergence speed compared with the traditional algorithm, and uses the distance information to filter samples to reduce the computational overhead and improve the positioning accuracy. Under the same conditions, the improved MCL algorithm based on RSSI reduces the positioning error by about 45% compared with the traditional Monte Carlo algorithm.

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Section: Classical MCL Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Indoor Mobile Node Localization Algorithm based on RSSI and Improved MCL

Liu¹,

Fang²

2018

dtcse

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“…MMCL (Multi-hop-based Monte Carlo Localization) combines MCL and DV-Hop (Distance Vector-Hop) algorithms [4] and improve the precise of positioning without the information of the communication radius of nodes when node density is relatively low [5].…”

Section: Related Workmentioning

confidence: 99%

A Localization Algorithm Considering Node Movement for Wireless Sensor Network

Zhang

Shi²,

Yang³

et al. 2017

IJFGCN

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For the application of wireless sensor networks, the perceptive information without location information is meaningless. With the popularity of mobile devices, the mobile node localization problem is attracting more and more researches. This paper, we proposed a localization algorithm. The proposed algorithm minimizes the range of sampling node by using the node's movement trajectory. This improves the efficiency of sampling and the positioning accuracy. Simulation results showed that our algorithm improves the positional accuracy.

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“…Repeating these static localization algorithms can provide location estimates in mobile networks, but this approach is suboptimal due to the lack of additional information provided by the mobility of the sensor nodes. Some works already take this information into account, for example [13,14,15,16,17]. Moreover, the goal of most localization methods [6,8,11,12,13] is just to estimate the position of all the target nodes, without associated uncertainty.…”

Section: Related Workmentioning

confidence: 99%

“…Distribution p(X j,m u ) used for reweighting in Alg. 2 is given by the numerator of (14). For the KDE, we again use a spherical Gaussian Kernel with bandwidth h. Finally, in each iteration of Alg.…”

Section: Population Monte Carlo (Pmc)mentioning

confidence: 99%

Cooperative localization in mobile networks using nonparametric variants of belief propagation

Savic¹,

Zazo²

2013

Ad Hoc Networks

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Of the many state-of-the-art methods for cooperative localization in wireless sensor networks (WSN), only very few adapt well to mobile networks. The main problems of the well-known algorithms, based on nonparametric belief propagation (NBP), are the high communication cost and inefficient sampling techniques. Moreover, they either do not use smoothing or just apply it offline. Therefore, in this article, we propose more flexible and efficient variants of NBP for cooperative localization in mobile networks. In particular, we provide: i) an optional 1-lag smoothing done almost in real-time, ii) a novel low-cost communication protocol based on package approximation and censoring, iii) higher robustness of the standard mixture importance sampling (MIS) technique, and iv) a higher amount of information in the importance densities by using the population Monte Carlo (PMC) approach, or an auxiliary variable. Through extensive simulations, we confirmed that all the proposed techniques outperform the standard NBP method.

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Multi-hop-based Monte Carlo Localization for Mobile Sensor Networks

Cited by 42 publications

References 19 publications

Indoor Mobile Node Localization Algorithm based on RSSI and Improved MCL

Indoor Mobile Node Localization Algorithm based on RSSI and Improved MCL

A Localization Algorithm Considering Node Movement for Wireless Sensor Network

Cooperative localization in mobile networks using nonparametric variants of belief propagation

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