A Localization Algorithm Based on AOA for Ad-Hoc Sensor Networks

Lee, Yang Sun; Park, Jang Woo; Barolli, Leonard

doi:10.1155/2012/986327

Cited by 49 publications

(30 citation statements)

References 14 publications

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“…Based on range free positioning technologies including DV-Hop, Centroid location and APIT location [2][3][4]. These algorithms only need to locate the nodes according to the connectivity of the network, the localization precision is low; The localization of ranging technology mainly includes AOA(angle of arrival), TOOA(time of arrival), TDOA (time difference of arrival) and based on RSSI [5][6][7]. These algorithms need to measure the distance or angle information between the nodes to be located and anchor nodes to achieve the location.…”

Section: Introductionmentioning

confidence: 99%

Application of Indoor Positioning Based on Kpca and Bp Neural Network Algorithm

Zi-ming¹,

He²

2017

Topics in Intelligent Computing and Industry Design (ICID)

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As the location method of indoor fingerprint position needs to be conducted in a region with signal coverage, the signal intensity's information of all Access Points has to be collected to build a fingerprint database. And not all information in the database will be of positive service to the positional accuracy. On account of increasing data dimensions in the database, rising complexity of algorithm and the climbing number of required experimental samples, a dimension disaster could be arisen. Based on KPCA algorithmic method, in this paper, positioning performance will be improved even under noisy circumstance by preprocessing the position fingerprint data and some data space occupied by positioning system will be saved through decreasing the data dimensions and reduced information of redundancy.

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Section: Introductionmentioning

confidence: 99%

Application of Indoor Positioning Based on Kpca and Bp Neural Network Algorithm

Zi-ming¹,

He²

2017

Topics in Intelligent Computing and Industry Design (ICID)

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“…Range-based algorithms, such as the time of arrival (TOA) [10], time difference of arrival (TDOA) [11,12], angle of arrival (AOA) [13] and received signal strength (RSS) [14], calculate distances between neighbor nodes by some typical hardware. By contrast, range-free algorithms, such as DV-Hop [15], DV-RND [16], RSD [17] and LEAP [18], design some distance estimation algorithms to calculate distances between nodes by the communication and connectivity.…”

Section: Introductionmentioning

confidence: 99%

A Localization Algorithm for Wireless Sensor Networks

Wen

Meng

et al. 2015

Frontiers in Internet Technologies

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Abstract. Node localization is important in many WSN applications. Most of existing algorithms are not applicable for networks in a concave area. To address this problem, a novel localization algorithm is proposed. The key of this algorithm is the design of neighborhood function which only uses distances between neighbor nodes to generate a new set of estimated locations from an old set. Both range-based and range-free localization can use it to estimate locations of nodes if distances between neighbor nodes are measured by hardware or estimated by distance estimation algorithms. Simulation results indicate that this algorithm can achieve accurate and reliable localization results in a concave area.

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“…Several heuristic approaches are found in the literature for node placement problems in WMNs [8]- [17].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Giant Component and Number of Covered Users in Wireless Mesh Networks: A Comparison Study

Koliçi

Barolli

Sakamoto

et al. 2015

2015 IEEE 29th International Conference on Advanced Information Networking and Applications

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'ús personal d'aquest material. S'ha de demanar permís a l'IEEE per a qualsevol altre ús, incloent la reimpressió/reedició amb fins publicitaris o promocionals, la creació de noves obres col·lectives per a la revenda o redistribució en servidors o llistes o la reutilització de parts d'aquest treball amb drets d'autor en altres treballs. Abstract-One of the key advantages of Wireless Mesh Networks (WMNs) is their importance for providing costefficient broadband connectivity. There are issues for achieving the network connectivity and user coverage, which are related with the node placement problem. We already implemented 3 simulation systems (WMN-HC, WMN-SA and WMN-GA) in our previous work. In this work, we present a comparative study by applying these 3 simulation systems for optimal distribution of WMN router nodes in order to provide the best network connectivity and user coverage. From the simulation results, we conclude that for small instances WMN-HC and WMN-SA have better performance than WMN-GA. However, for large instance the performance of WMN-GA simulation system is better.

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A Localization Algorithm Based on AOA for Ad-Hoc Sensor Networks

Cited by 49 publications

References 14 publications

Application of Indoor Positioning Based on Kpca and Bp Neural Network Algorithm

Application of Indoor Positioning Based on Kpca and Bp Neural Network Algorithm

A Localization Algorithm for Wireless Sensor Networks

Optimization of Giant Component and Number of Covered Users in Wireless Mesh Networks: A Comparison Study

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