ADAL: A distributed range-free localization algorithm based on a mobile beacon for wireless sensor networks

Guerrero, Esteban; Huagang, Xiong; Gao, Qiang; Cova, Gabriel; Ricardo, R.; Estevez, J.

doi:10.1109/icumt.2009.5345556

Cited by 20 publications

(11 citation statements)

References 19 publications

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“…A better way to show how often a variable is over a given level is calculating the complementary cumulative distribution function in each case, some of our results in (7) are graphically shown in Fig.5 using the corresponding complementary cumulative distribution functions.…”

Section: Simulation Results and Analysismentioning

confidence: 98%

“…In [6] and [7] location algorithm are based on several and one mobile beacon respectively, each beacon transmit needed information to the sensors allowing them to estimate their own position running an algorithm. Without loss of generality all of these algorithms require that sensors receive at least three information packages from Anchor nodes or beacons in order to estimate their own position using trilateration or triangulation techniques.…”

Section: Introductionmentioning

confidence: 99%

“…Many range-free localization algorithms have been proposed, some of them are in literature [5], [6], [7].…”

Section: Introductionmentioning

confidence: 99%

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Monte Carlo Analysis of nodes deployment for large-scale Wireless Sensor Network using range-free location methods

Ricardo

Huagang

Gao

et al. 2010

2010 IEEE 2nd Symposium on Web Society

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In this paper a Monte Carlo Analysis of nodes deployment for large-scale and non-homogeneousWireless Sensor Networks, has been done. We ran simulations of random deployments of nodes over a square area using different densities, assuming that our network is composed by Anchor nodes (special sensors with known position) and simple Sensor nodes, the latter are supposed to estimate their own position after being placed within the coverage area with the minimum Anchor nodes needed to 'feed' them with the necessary information. The goal is then to assist decision-makers in selecting among different alternatives to deploy the networks, according to resources features and availability, hence this method provides an estimate value of how many Anchor nodes should be deployed in a given area to trigger the location algorithm in the greatest possible number of Sensor nodes in the network.

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Section: Simulation Results and Analysismentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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Monte Carlo Analysis of nodes deployment for large-scale Wireless Sensor Network using range-free location methods

Ricardo

Huagang

Gao

et al. 2010

2010 IEEE 2nd Symposium on Web Society

Self Cite

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“…The scheme described in [26] is also another area based range-free Localization Algorithm which uses a mobile beacon instead of static beacon nodes called the Azimuthally Defined Area Localization (ADAL) method. This scheme uses a mobile beacon with a rotary directional antenna to send message in a determined azimuth periodically, and an unknown node uses the centroid of the intersection area of several beacon messages as its position [10].…”

Section: A Anchor Based Approachesmentioning

confidence: 99%

Wireless Sensor Networks Localization Algorithms: A Comprehensive Survey

Mesmoudi¹,

Feham²,

Labraoui³

2013

IJCNC

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“…[6] proposed a method based on geometric constraints utilizing three reference points. ADAL (Azimuthally Defined Area Localization) algorithm [7] uses a mobile beacon node equipped with a rotary directional antenna to determine the area.…”

Section: A Mobile Beacon Assisted Localization Methodsmentioning

confidence: 99%

Getting Obstacle Avoidance Trajectory of Mobile Beacon for Localization

Cui¹,

Wang²,

Guo³

et al. 2010

IJCNIS

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Abstract-Localization is one of the most important technologies in wireless sensor network, and mobile beacon assisted localization is a promising localization method. The mobile beacon trajectory planning is a basic and important problem in these methods. There are many obstacles in the real world, which obstruct the moving of mobile beacon. This paper focuses on the obstacle avoidance trajectory planning scheme. After partitioning the deployment area with fixed cell decomposition, the beacon trajectory are divided into global and local trajectory. The approximate shortest global trajectory is obtained by depth-first search, greedy strategy method and ant colony algorithm, while local trajectory is any existing trajectories. Simulation results show that this method can avoid obstacles in the network deployment area, and the smaller cell size leads to longer beacon trajectory and more localizable sensor nodes.

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ADAL: A distributed range-free localization algorithm based on a mobile beacon for wireless sensor networks

Cited by 20 publications

References 19 publications

Monte Carlo Analysis of nodes deployment for large-scale Wireless Sensor Network using range-free location methods

Monte Carlo Analysis of nodes deployment for large-scale Wireless Sensor Network using range-free location methods

Wireless Sensor Networks Localization Algorithms: A Comprehensive Survey

Getting Obstacle Avoidance Trajectory of Mobile Beacon for Localization

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