Localization in wireless sensor networks using a mobile anchor node

Hu, Zhen; Gu, Dongbing; Song, Zhengxun; Li, Hongzuo

doi:10.1109/aim.2008.4601728

Cited by 25 publications

(10 citation statements)

References 21 publications

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“…Zhen Hu et al [26] proposed a radio-frequency (RF) based mobile anchor centroid localization method (MACL) for WSNs. In this method, a mobile anchor node moves in the sensing field and broadcast its current location periodically.…”

Section: Reviews On Mobile Anchor Based Localization Techniquesmentioning

confidence: 99%

Meta-heuristic approaches for minimizing error in localization of wireless sensor networks

Sivakumar

Venkatesan

2015

Applied Soft Computing

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Section: Reviews On Mobile Anchor Based Localization Techniquesmentioning

confidence: 99%

Meta-heuristic approaches for minimizing error in localization of wireless sensor networks

Sivakumar

Venkatesan

2015

Applied Soft Computing

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“…According to S-CURVES, the deployed area is scanned towards y-axis then turned to x-axis ad son on like SCAN algorithm but taking S shape. Authors in (Hu, Gu et al 2008) presents a new algorithm called MACL, where a mobile robot follows a spiral shape while broadcasting its position every step in time. G. Han et al (Han, Xu et al 2013) proposed a static path method called LMAT, where the mobile robot moves according to triangle path.…”

Section: Related Workmentioning

confidence: 99%

An Enhanced Animator System for Simulating Robot Movement in Wireless Sensor Networks

Shehab¹,

Naguib

2023

Preprint

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Optimizing robots’ path have recently gained a great attention and considered an open research challenge due to their potential to work in dynamic and complex environments. Most researchers working in the field of single anchor-assisted localization in wireless Sensor Networks (WSNs), often run their simulations based on NS-2 simulator which needs a movement scenario file that stores the coordinates of mobile robot trajectory. To address such issues, this paper proposes an object-oriented graphical user interface (GUI) system that can be used as an animator and also as a scenario generator tool for the movement of mobile robot. Our animator system enables researchers to generate their own scenario files regards to different static path planning mechanisms. Besides, this paper proposes a genetic algorithm based path planning method with the assistance of Bézier curve technique to work in a dynamic environment. Our proposal improves the robot performance, compared to others, in terms of the path length, the smoothness of the path, processing time.

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“…The authors in [41] presented a localization technique called mobile anchor centroid localization (MACL) using only one mobile anchor node. This method is based on the radio frequency.…”

Section: Related Workmentioning

confidence: 99%

Mobile Anchor and Kalman Filter Boosted Bounding Box for Localization in Wireless Sensor Networks

et al. 2022

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Event detection is usually the primary purpose of wireless sensor networks (WSNs). Therefore, it is crucial to determine where and when an event occurs in order to map the event to its spatio-temporal domain. In WSN localization, a few anchor nodes are those aware of their locations via the Global Positioning System (GPS), which is energy-consuming. Non-anchor nodes self-localize by gathering information from anchor nodes to estimate their positions using a localization technique. Traditional algorithms use at least three static anchors for the localization process. Recently, researchers opted to replace multiple static anchors by a single mobile anchor during the localization process. This paper proposes a Kalman filter based on bounding box localization algorithm (KF-BBLA) in WSNs with mobile anchor node. We present a new mobile anchor localization strategy to minimize energy, hardware costs, and computation complexity, while improving accuracy and cost-effectiveness. Network connectivity measurement and the bounding box localization method are used in order to identify the bounded possible localization zone. The Kalman filter is then used to minimize the uncertainty produced by the connectivity process. We aim also to minimize the localization inaccuracies generated by the bounding box algorithm. Simulation results show that our proposed approach significantly reduces the localization error compared to other localization algorithms chosen from the recent literature by up to 20%. We use the cumulative distribution function (CDF) as an indicator to assess the accuracy of our proposed algorithm.

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Localization in wireless sensor networks using a mobile anchor node

Cited by 25 publications

References 21 publications

Meta-heuristic approaches for minimizing error in localization of wireless sensor networks

Meta-heuristic approaches for minimizing error in localization of wireless sensor networks

An Enhanced Animator System for Simulating Robot Movement in Wireless Sensor Networks

Mobile Anchor and Kalman Filter Boosted Bounding Box for Localization in Wireless Sensor Networks

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