Robot-Assisted Localization Techniques for Wireless Image Sensor Networks

Lee, Huang; Dong, Hattie Z.; Aghajan, Hamid

doi:10.1109/sahcn.2006.288443

Cited by 15 publications

(17 citation statements)

References 15 publications

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“…Such noises due to inaccuracies in measurements are common both in GPS-based location estimator devices [95] and localization techniques adopted to wireless sensor networks [96,97] …”

Section: Methodsmentioning

confidence: 99%

Deployment of a mobile wireless sensor network with k-coverage constraint: a cellular learning automata approach

Esnaashari

Meybodi

2012

Wireless Netw

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Deployment of a wireless sensor network is a challenging problem, especially when the environment of the network does not allow either of the random deployment or the exact placement of sensor nodes. If sensor nodes are mobile, then one approach to overcome this problem is to first deploy sensor nodes randomly in some initial region within the area of the network, and then let the sensor nodes to move around and cooperatively and gradually increase the covered section of the area. Recently, a cellular learning automata-based deployment strategy, called CLA-DS, is introduced in literature which follows this approach and is robust against inaccuracies which may occur in the measurements of sensor positions or in the movements of sensor nodes. Despite its advantages, this deployment strategy covers every point within the area of the network with only one sensor node, which is not enough for applications with k-coverage requirement. In this paper, we extend CLA-DS so that it can address the k-coverage requirement. This extension, referred to as CLA-EDS, is also able to address k-coverage requirement with different values of k in different regions of the network area. Experimental results have shown that the proposed deployment strategy, in addition to the advantages it inherits from CLA-DS, outperforms existing algorithms such as DSSA, IDCA, and DSLE in covering the network area, especially when required degree of coverage differs in different regions of the network.

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“…Such noises due to inaccuracies in measurements are common both in GPS-based location estimator devices [95] and localization techniques adopted to wireless sensor networks [96,97] …”

Section: Methodsmentioning

confidence: 99%

Deployment of a mobile wireless sensor network with k-coverage constraint: a cellular learning automata approach

Esnaashari

Meybodi

2012

Wireless Netw

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“…[24] Agents move and locate to cover the sensor nodes by maintaining a k-hop independent set A dynamic communication infrastructure but cost of agents with advanced hardware for mobility support Melodia et al [25] An event-driven clustering framework where the problem is formulated by integer programming An event-driven dynamic communication infrastructure but cost of agents with advanced hardware for algorithm execution Pathirana et al [26] Localization with an agent by applying extended Kalman filter on received signals Simple and accurate localization but cost of an agent with an advanced hardware for executing complicated algorithms Lee et al [27] Localization with an agent by processing the images of sensor nodes…”

Section: Communication Protocolsmentioning

confidence: 99%

“…The localization accuracy of their algorithm is approximately 1 m measured from the real experiments. Their algorithm performs better than the previous algorithms where the localization accuracies are within 3 m. Lee et al proposed a visionbased technique for localization in sensor networks [27]. Different than the Pathirana et al's approach in Figure 9a, motion plane is parallel to the sensor nodes plane and the agent device is equipped with a camera as shown in Figure 9b.…”

Section: Localization Algorithmsmentioning

confidence: 99%

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A survey of agent technologies for wireless sensor networks

Dağdeviren¹,

Korkmaz²,

Tekbacak³

et al. 2011

IETE Tech Rev

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Wireless sensor networks (WSNs) do not have a fixed infrastructure and consist of sensor nodes that perform sensing and communicating tasks. The WSNs have large application spectrum such as habitat monitoring, military surveillance, and target tracking, where sensor nodes may operate distributed in highly dynamic environments. Battery-constrained sensor nodes may aggregate the sensed data, localize themselves, and route the packets in an energy-efficient and decentralized manner to enable running the applications. Agents are capable of independent and autonomous action, so that they can successfully carry out tasks that have been delegated to them, thus agent-based approaches are very suitable to apply as the solution of the problems occurring in WSNs. So far many agent-based approaches were proposed for WSNs. This paper surveys the agent technologies for sensor networks by providing a classification, objectives and costs of these approaches with the open research problems. To the best of our knowledge, this is the first study that covers the intersection of the agent technology and sensor networks from a wide perspective.

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“…Telsiz duyarga ağlarının genel yapısı (General architecture of wireless sensor networks) TDAlar üzerinde düğümlerin konumlarının bulunması ve ağın bağlılığının denetlenmesi önemli problemlerdir. TDAda konumlandırma iĢlemi, konumunu bilmeyen bir düğümün, konumunu bilen diğer düğümleri temel alarak konumunu bulması iĢlemidir [6][7][8][9][10][11][15][16][17][18][19][20][21][22][23][24][25][26][27]. Birçok durumda, TDA içindeki bir düğümün konumunu öğrenmesi gerekmektedir.…”

Section: Gi̇ri̇ş (Introduction)unclassified

TinyOS Tabanlı Telsiz Duyarga Ağları için Bir Konumlandırma ve k-Bağlılık Denetleme Sistemi

Dağdeviren¹,

Akram²

2017

Bilişim Teknolojileri Dergisi

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Özet-Telsiz duyarga ağlarında (TDAlarında) düğümlerin konumlarının bulunması ve aralarındaki bağlantıların denetimi önemli konulardandır. Konumlandırma probleminde, amaç konumları belli olmayan mobil veya statik düğümlerin koordinatlarını diğer kök düğümlerin yardımıyla bulmaktır. k-Bağlılık denetleme probleminde amaç en az kaç adet düğüm bozulduğunda ağın bağlılığının bozulduğunu bulmaktır. Bu çalıĢmada TDA için bir konumlandırma ve k-bağlılık denetleme sistemi tasarlanmıĢtır ve uygulanmıĢtır. Bu sistemde trilaterasyon algoritması kullanılarak düğümlerin güncel pozisyonları diğer kök düğümlerden gelen mesajların üzerinden bulunmaktadır. Uygulanan sistemde düğümlerin arasındaki mesafeler gelen sinyallerin RSSI (Received Signal Strength Indicator) değerinden tahmin edilir. Konumu belli olmayan bir düğüm, en az 3 kök düğümden mesaj aldıktan sonra kendi koordinatını hesaplayabilir. Tüm gönderilen mesajlar ve bulunan koordinatlar, ağın çıkıĢ düğümü tarafından algılanıp, bir Java uygulamasına aktarılır ve arayüzler üzerinden kullanıcıya sunulur. Ağın son bağlılık durumunu göstermek için gelen mesajlar üzerinden oluĢturulan güncel topoloji üzerinde bir k-bağlılık denetleme algoritması çalıĢtırılır. Önerilen ve MEMSIC-IRIS düğümlerin üzerinde denetlenen sistem tüm TinyOS iĢletim sistemini destekleyen düğümlerin üzerinde çalıĢabilir. Önerilen sistem çeĢitli uygulamalarda bir altyapı olarak kullanılabilir.Anahtar Kelimeler-Telsiz Duyarga Ağları, Konumlandırma, Trilaterasyon, Bağlılık Denetleme, k-Bağlılık A Localization and k-Connectivity Detection System for TinyOS based Wireless Sensor NetworksAbstract-Localization and connectivity detection are two important topics in Wireless Sensor Networks (WSNs). In localization problem the aim is finding the position of mobile or static nodes with unknown coordinates. The purpose of the k-connectivity problem is to find the number of nodes whose removal destroys the connectivity of the network. In this paper, we have designed and implemented a localization and k-connectivity detection system. The proposed system uses trilateration approach to find the up to date locations of nodes where the trilateration method uses the messages from anchor nodes. In our system, the RSSI (Received Signal Strength Indicator) values of transmitted messages are used to estimate the distance between nodes. Using the proposed system, nodes can estimate their locations after receiving messages from at least 3 anchor nodes. The exchanged message and detected positions are snooped by the sink node, transferred to a Java program and presented to the user on GUIs. To find the latest connectivity statuses of a network, a k-connectivity testing algorithm periodically is run on the current detected topology from incoming messages. The proposed system has been tested with MEMSIC-IRIS motes and supports all WSNs which use TinyOS based motes. This system can be used as an infrastructure for various applications in WSNs.

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Robot-Assisted Localization Techniques for Wireless Image Sensor Networks

Cited by 15 publications

References 15 publications

Deployment of a mobile wireless sensor network with k-coverage constraint: a cellular learning automata approach

Deployment of a mobile wireless sensor network with k-coverage constraint: a cellular learning automata approach

A survey of agent technologies for wireless sensor networks

TinyOS Tabanlı Telsiz Duyarga Ağları için Bir Konumlandırma ve k-Bağlılık Denetleme Sistemi

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