SHARP: A New Approach to Relative Localization in Wireless Sensor Networks

Ahmed, Ashfaq; Shi, Hongchi; Shang, Yi

doi:10.1109/icdcsw.2005.125

Cited by 65 publications

(37 citation statements)

References 3 publications

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“…-GDE (Global Distance Error): As discussed briefly at the start , it is important for the accuracy metric to reflect not only the positional error in terms of distance, but also in terms of the geometry of the network localization result. GDE in [6] takes the RMS error over the network of n nodes and normalises it using the constant R. In Ahmed et als context, R represents average radio range, meaning the localization results are represented as a percentage of the average distance nodes can communicate over.…”

Section: Resultsmentioning

confidence: 99%

“…If they are neighbors, X trace two circles (belongs to C N Λ ) centered in A of radius d AX ± and deduce that it is between these two circles. If they are not neighbors, X deduces that it is not inside the circles centered at A of radius r − and belongs to a circle of radius d AX + ,the definitions (4), (6), (9) and (11) become : si a i ∈ N Λ (u):…”

Section: Slsnj Propertiesmentioning

confidence: 99%

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A Distributed Method for Localization in Large-Scale Sensor Networks based on Jarvis

Sabri¹,

Kamoun²

2017

IJCA

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This paper addresses target localization problem in a cooperative 3-D wireless sensor network (WSN). We employ a hybrid system that fuses distance and angle measurements, extracted from the received signal strength (RSS) and angle-of-arrival (AoA) information, respectively. Based on range measurement model and simple geometry, we derive a novel convex estimator based on Jarv's scan . The network is said to be uniquely localizable if there is a unique set of locations consistent with the given data.This paper presents an improved localization algorithm with high accuracy in large-scale Sensor networks with a large number of sensor nodes based on the Jarvis' March ,called SLSNJ. the Jarvis' March adapted here for our approximation technique to determining the convex hull of a set of sensors used instead of the Grid-Scan method,to take into account the requirements in memory, to make it scalable and rapidly convergent with small location estimation error.We verify our algorithm in various scenarios and compare it with AT-Dist method. Our simulation results show that the new estimators have excellent performance in terms of the estimation accuracy and convergence, and they confirm the effectiveness of combining two radio measurements in large-scale.

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Section: Resultsmentioning

confidence: 99%

Section: Slsnj Propertiesmentioning

confidence: 99%

A Distributed Method for Localization in Large-Scale Sensor Networks based on Jarvis

Sabri¹,

Kamoun²

2017

IJCA

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show abstract

“…• GDE (Global Distance Error): As discussed briefly at the start , it is important for the accuracy metric to reflect not only the positional error in terms of distance, but also in terms of the geometry of the network localization result. GDE in www.ijacsa.thesai.org [46] takes the RMS error over the network of n nodes and normalises it using the constant R. In Ahmed et als context, R represents average radio range, meaning the localization results are represented as a percentage of the average distance nodes can communicate over. This section analyses the performances of our three methods related to the techniques SFR, DVM and MADRD.…”

Section: B Resultsmentioning

confidence: 99%

A Distributed Method to Localization for Mobile Sensor Networks based on the convex hull

Sabri¹,

Kamoun²

2012

IJACSA

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Abstract-There has been recently a trend of exploiting the heterogeneity in WSNs and the mobility of either the sensor nodes or the sink nodes to facilitate data dissemination in WSNs. Recently, there has been much focus on mobile sensor networks, and we have even seen the development of small-profile sensing devices that are able to control their own movement. Although it has been shown that mobility alleviates several issues relating to sensor network coverage and connectivity, many challenges remain. Among these, the need for position estimation is perhaps the most important. Not only is localization required to understand sensor data in a spatial context, but also for navigation, a key feature of mobile sensors. This paper concerns the localization problem in the case where all nodes in the network (anchors and others sensors) are mobile. We propose the technique following the capabilities of nodes. Thus, each node obtains either an exact position or an approximate position with the knowledge of the maximal error born. Also, we adapt the periods where nodes invoke their localization. Simulation results show the performances of our method in term of accuracy and determinate the technique the more adapted related to the network configurations.

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“…The GER metric does not reflect the rms error [109] and is addressed by defining an accuracy metric that better reflects the rms error called global distance error (GDE).…”

Section: Accuracymentioning

confidence: 99%

Localization in Wireless Sensor Networks: A Survey on Algorithms, Measurement Techniques, Applications and Challenges

Paul

Sato

2017

JSAN

183

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Abstract:Localization is an important aspect in the field of wireless sensor networks (WSNs) that has developed significant research interest among academia and research community. Wireless sensor network is formed by a large number of tiny, low energy, limited processing capability and low-cost sensors that communicate with each other in ad-hoc fashion. The task of determining physical coordinates of sensor nodes in WSNs is known as localization or positioning and is a key factor in today's communication systems to estimate the place of origin of events. As the requirement of the positioning accuracy for different applications varies, different localization methods are used in different applications and there are several challenges in some special scenarios such as forest fire detection. In this paper, we survey different measurement techniques and strategies for range based and range free localization with an emphasis on the latter. Further, we discuss different localization-based applications, where the estimation of the location information is crucial. Finally, a comprehensive discussion of the challenges such as accuracy, cost, complexity, and scalability are given.

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SHARP: A New Approach to Relative Localization in Wireless Sensor Networks

Cited by 65 publications

References 3 publications

A Distributed Method for Localization in Large-Scale Sensor Networks based on Jarvis

A Distributed Method for Localization in Large-Scale Sensor Networks based on Jarvis

A Distributed Method to Localization for Mobile Sensor Networks based on the convex hull

Localization in Wireless Sensor Networks: A Survey on Algorithms, Measurement Techniques, Applications and Challenges

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