Localization With Mobile Anchor Points in Wireless Sensor Networks

Ssu, Kuo‐Feng; Ou, Chia-Ho; Jiau, Hewijin Christine

doi:10.1109/tvt.2005.844642

Cited by 402 publications

(214 citation statements)

References 33 publications

Supporting

Mentioning

206

Contrasting

Order By: Relevance

“…Xiao et al proposes another scheme using the arrival and departure overlap area, which is a possible area delimited by two circles with the same radius at different centers [20]. Ssu et al introduces scheme [18] that the location of a sensor node is estimated using the intersection points of two perpendicular bisectors of the chords obtained by three beacon points. To improve the localization accuracy of Ssu's scheme, an efficient scheme is suggested to estimate sensor locations from possible areas by using geometric constraints [12].…”

Section: Related Workmentioning

confidence: 99%

Localization Using a Mobile Beacon with Directional Antenna for Wireless Sensor Networks

Chen

2011

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

SUMMARYWireless sensor networks are comprised of several sensor nodes that communicate via wireless technology. Locating the sensor nodes is a fundamental problem in developing applications for wireless sensor networks. In this paper, we introduce a distributed localization scheme, called the Rectangle Overlapping Approach (ROA), using a mobile beacon with GPS and a directional antenna. The node locations are computed by performing simple operations that rely on the rotation angle and position of the mobile beacon. Simulation results show that the proposed scheme is very efficient and that the node positions can be determined accurately when the beacon follows a random waypoint movement model.

show abstract

Section: Related Workmentioning

confidence: 99%

Localization Using a Mobile Beacon with Directional Antenna for Wireless Sensor Networks

Chen

2011

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

show abstract

“…Upon receiving all the beacon packets, the sensor node's location can be restricted in an intersected area of several bounding boxes as shown in Figure 9.12. Unlike the above GPS-based localization using mobile beacon, it was proposed in [47] to localize the sensor node using radio range of sensor node instead of that of mobile beacon. The method is based on a geometry conjecture, named Perpendicular Bisector of a Chord, which states that that a perpendicular bisector of a chord passes through the center of the circle (see Figure 9.13 for an illustration).…”

Section: Mobility-assisted Localizationmentioning

confidence: 99%

“…The analysis on the communication/computation costs was not extensively presented in the empirical studies due to the difficulty in measuring the cost in real implementations. [11] 10%R N/A 4 anchors at the corners DV-hop [12] 30%R for isotropic; 90%R for anisotropic 7000 messages exchanged dg=7.6 with 30% to be anchors DV-distance [12] 15%R for isotropic; 80%R for anisotropic 7200 messages exchanged dg=7.6 with 30% to be anchors Euclidean [12] 10%R for isotropic; 15%R for anisotropic 8000 messages exchanged dg=7.6 with 30% to be anchors MDS-MAP [13] 50%R computation complexity of O(n 3 ) dg 12.2 with 3 anchors at random positions Ecolocation [66] 30%D N/A 15 anchors randomly placed DV-coordinate [27] 1m for isotropic; 1.25m for anisotropic N/A dg average =9 DV-bearing [26] 1 hop distance N/A dg average =10.5 DV-radial [26] 0.8 hop distance N/A dg average =10.5 Bisector [47] 5%R 1597 packets 319 randomly deployed nodes Kernel-based Learning [49] 0.47 worst case computational time O(n 3 ) 25 anchors, 400 non-anchors EKF [67] 20%R N/A randomly deployed nodes, 1 mobile robot MCL [53] 20%R 50 samples dg=10, anchor density is 4 RSS Model [17] 5%R O(n 2 log 2 n) node density is 0.5/meter 2…”

Section: Summary Of Performancesmentioning

confidence: 99%

Algorithmic Aspects of Sensor Localization

Das

Wang

Ghosh

et al. 2010

Monographs in Theoretical Computer Science. An EATCS Series

View full text Add to dashboard Cite

Identifying locations of nodes in wireless sensor networks (WSNs) is critical to both network operations and most application level tasks. Sensor nodes equipped with geographical positioning system (GPS) devices are aware of their locations at a precision level of few meters. However, installing GPS devices on a large number of sensor nodes is not only expensive but affects the form factor of these nodes. Moreover, GPS-based localization is not applicable in the indoor environments such as buildings. There exists an extensive body of research literature that aims at obtaining absolute locations as well as relative spatial locations of nodes in a WSN without requiring specialized hardware at large scale. The typical approach consists of employing only a limited number of anchor nodes that are aware of their own locations, and then trying to infer locations of non-anchor nodes using graph theoretic, geometric, statistical, optimization and machine learning techniques. Thus, the literature represents a very rich ensemble of algorithmic techniques applicable to low power, highly distributed nodes with resource-optimal computations. In this chapter we take a close look at the algorithmic aspects of various important localization techniques for WSNs.

show abstract

“…This method can offer real-time localization of staff and equipment and has various functions, such as for track record, work attendance monitoring, behavioral analysis, and intelligent patrol [4][5]. However, the demand for outdoor remote localization increases gradually with the development of technologies and the growth of outdoor wireless localization demands.…”

Section: Introductionmentioning

confidence: 99%

TDOA Localization Algorithm based on Weight Fusion of Target Position Coordinates

QingYi¹,

Zhang²,

Sun³

2017

JESTR

View full text Add to dashboard Cite

The use time of difference of arrival (TDOA) or angle of arrival (AOA) in locating the target requires at least four base stations. The measurement accuracy of different base stations varies due to different relative positions of the base station and the target. Therefore, the measurement results of different base stations cannot be discussed simultaneously. A new TDOA localization algorithm based on weighting fusion of position coordinates of target source was proposed to address this problem. First, maximum likelihood estimation was used to acquire the position coordinates of multiple groups of target sources on the 2D plane with time of arrival (TOA) and AOA of signals from base stations to the target source. Second, the coordinates were applied with weighted fusion according to the relative angle between the signals emitted from the base stations and target source to obtain relatively accurate 3D position coordinates. Finally, the proposed algorithm was compared with TDOA localization algorithm and TDOA-AOA combined localization algorithm. Results reveal that the proposed algorithm increases accuracy by 68.33% and 48.27% compared with TDOA localization algorithm and TDOA-AOA combined localization algorithm, respectively. Weighting data fusion can increase the calculation accuracy more effectively than the averaging method. This study increases the accuracy of 3D target localization in complicated environment. The proposed method shows prospect to optimize the wireless positioning technology in military and civilian fields.

show abstract

Localization With Mobile Anchor Points in Wireless Sensor Networks

Cited by 402 publications

References 33 publications

Localization Using a Mobile Beacon with Directional Antenna for Wireless Sensor Networks

Localization Using a Mobile Beacon with Directional Antenna for Wireless Sensor Networks

Algorithmic Aspects of Sensor Localization

TDOA Localization Algorithm based on Weight Fusion of Target Position Coordinates

Contact Info

Product

Resources

About