A Lightweight Localization Scheme in Wireless Sensor Networks

Liao, Wen-Hwa; Lee, Yu-Chee

doi:10.1109/icwmc.2006.5

Cited by 9 publications

(4 citation statements)

References 11 publications

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“…In the third stage, the range estimations from all nodes are combined and used to solve Equation (4) The mobile node's location is estimated using the trilateration technique [ 38 ]. A median filter [ 39 ] is used to apply the continuity constraint in Equation (4) .…”

Section: Rssi Based Tracking Of a Body Segmentmentioning

confidence: 99%

Human Body Parts Tracking and Kinematic Features Assessment Based on RSSI and Inertial Sensor Measurements

Blumrosen

Luttwak

2013

Sensors

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Acquisition of patient kinematics in different environments plays an important role in the detection of risk situations such as fall detection in elderly patients, in rehabilitation of patients with injuries, and in the design of treatment plans for patients with neurological diseases. Received Signal Strength Indicator (RSSI) measurements in a Body Area Network (BAN), capture the signal power on a radio link. The main aim of this paper is to demonstrate the potential of utilizing RSSI measurements in assessment of human kinematic features, and to give methods to determine these features. RSSI measurements can be used for tracking different body parts' displacements on scales of a few centimeters, for classifying motion and gait patterns instead of inertial sensors, and to serve as an additional reference to other sensors, in particular inertial sensors. Criteria and analytical methods for body part tracking, kinematic motion feature extraction, and a Kalman filter model for aggregation of RSSI and inertial sensor were derived. The methods were verified by a set of experiments performed in an indoor environment. In the future, the use of RSSI measurements can help in continuous assessment of various kinematic features of patients during their daily life activities and enhance medical diagnosis accuracy with lower costs.

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Section: Rssi Based Tracking Of a Body Segmentmentioning

confidence: 99%

Human Body Parts Tracking and Kinematic Features Assessment Based on RSSI and Inertial Sensor Measurements

Blumrosen

Luttwak

2013

Sensors

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“…Lastly Section 4 concludes this work. [9] Ssu et aL [10] Liao et al [6] Yu et aL [12][13] Our scheme Accuracy low medium medium high higher Pre-calibration" yes no no no no Anchor trajectory flexible rectilinear flexible rectilinear flexible Assumed radio range spherical irregular spherical irregular irregular Beacon packets many 3 3 4 many *Pre-calibration is to find a relation of signal strength measurements to the distances from a reference site, shaping a probability distribution function.…”

Section: Introductionmentioning

confidence: 99%

On complexity in wireless network localization

Chi

Hsu

Jiang

et al. 2012

2012 Complexity in Engineering (COMPENG). Proceedings

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Considerable literature on wireless network localization has assumed that each wireless station has a spherical radio range. This assumption, however, is generally untenable because wireless signals are subject to physical re8ection, diffraction, refraction, and scattering of electromagnetic waves. In consequence, radio signals become irregular, making position assessment complex and susceptible to nontrivial errors. As a remedy, this study presents a convex hull-based localization scheme allowing for radio coverage irregularity. We exploit a mobile anchor node to assist in locating target stations in a non-idealized space containing obstacles. Our scheme operates without reliance upon any ranging measurements about angle, distance, and received signal strength indication between radio transceivers. Qualitative and quantitative comparisons indicate that our approach outperforms counterpart schemes in terms of localization accuracy, yet at the expense of insignificant overhead. Overall performance evaluation concludes that our scheme can get fielded promisingly in practice.Our approach is primarily based on computational geometry and triangle geometry, as shall be clarified shortly.

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“…The estimated location is defined by the center of the region formed by the intersection of the circles. Another approach [24] utilizes only N = p anchor nodes and estimates the location by one of the intersection points. It records several intersection points in consecutive times and estimates the intersection location by the closest distance.…”

Section: Location Estimationmentioning

confidence: 99%

“…We choose a variant of [24] to estimate the mobile node's location using the Maximum A Posteriori (MAP) criterion. Assuming that the range estimations have the same statistical distribution and the mobile node location has Gaussian distribution, the MAP criterion coincides with the MMSE criterion, [25].…”

Section: Location Estimationmentioning

confidence: 99%

Continuous Close-Proximity RSSI-Based Tracking in Wireless Sensor Networks

Blumrosen

Hod

Anker

et al. 2010

2010 International Conference on Body Sensor Networks

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Abstract-In this paper we develop a continuous high-precision tracking system based on Received Signal Strength Indicator (RSSI) measurements for small ranges. The proposed system uses minimal number of sensor nodes with RSSI capabilities to track a moving object in close-proximity and high transmission rate. The close-proximity enables conversion of RSSI measurements to range estimates and the high transmission rate enables continuous tracking of the moving object. The RSSI-based tracking system includes calibration, range estimation, location estimation and refinement. We use advanced statistical and signal processing methods to mitigate channel distortion and packet loss. The system is evaluated in indoor settings and achieves tracking resolution of few centimeters. Therefore, it becomes the motion trackers of notice in many applications.

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A Lightweight Localization Scheme in Wireless Sensor Networks

Cited by 9 publications

References 11 publications

Human Body Parts Tracking and Kinematic Features Assessment Based on RSSI and Inertial Sensor Measurements

Human Body Parts Tracking and Kinematic Features Assessment Based on RSSI and Inertial Sensor Measurements

On complexity in wireless network localization

Continuous Close-Proximity RSSI-Based Tracking in Wireless Sensor Networks

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