Source Localization in Wireless Sensor Networks From Signal Time-of-Arrival Measurements

Xu, Enyang; Ding, Zhi; Dasgupta, Soura

doi:10.1109/tsp.2011.2116012

Cited by 229 publications

(145 citation statements)

References 21 publications

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“…We will derive the D-optimal configurations for the unconstrained and the constrained cases that are described in Section 4.1. From (30) and (B.18) we have the FIM for the emitter location vector η ′ = [x T y T ] as…”

Section: Optimal Sensor Configurationmentioning

confidence: 99%

“…They use a two-step weighted least squares (WLS) algorithm. Additionally, when the signal waveform is known, localization may be performed from the times of arrival (TOAs) instead of the TDOAs [29,30]. In such TOA based techniques the unknown transmission time occurs as a nuisance parameter which will have to be estimated.…”

Section: Introductionmentioning

confidence: 99%

“…Here, it is common to work with the TOAs instead of the TDOAs [29,30,44]. The TOA at a sensor "fixes" the emitter position on a sphere (circle in two dimensions) centered at the sensor location.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Wideband Signal De-Interleaving (WSD)

Kay¹,

Vankayalapati²

2012

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Section: Optimal Sensor Configurationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Wideband Signal De-Interleaving (WSD)

Kay¹,

Vankayalapati²

2012

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“…These measurements include the time of arrival (TOA) [4,5], time difference of arrival (TDOA) [6] and received signal strength (RSS) [7,8]. There is a tradeoff in the techniques, which rely on these parameters, in terms of implementation complexity and localization accuracy.…”

Section: Introductionmentioning

confidence: 99%

Convex Optimization Algorithms for Multiple Source Localization Based on Received Signal Strength Measurements

Deng¹

2016

IJFGCN

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show abstract

“…This usually requires antenna arrays or some other complicated hardware using directional antennas. Sensors use the location information of the reference nodes and the angle, or direction, at which they received the beacon signal to solve triangulation equations and determine their locations [59][60][61][62]. [61][62] [63] [64].…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Wireless Sensor Network Deployment

Qu¹

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Two centralized optimization algorithms are developed, one based on Genetic Algorithms (GAs) and one based on Particle Swarm Optimization (PSO). Both optimization algorithms use powerful central nodes to calculate and obtain the global optimum outcomes. The GA is used to determine the optimal tradeoff between network coverage and overall distance travelled by fixed range sensors. The PSO algorithm is used to ensure 100% network coverage and minimize the energy consumed by mobile and range-adjustable sensors. Up to 30% -90% energy savings can be provided in vii different scenarios by using the developed optimization algorithms thereby extending the lifetime of the sensor by 1.4 to 10 times.Three distributed optimization algorithms are also developed to relocate the sensors and optimize the coverage of networks with more stringent design and cost constraints. Each algorithm is cooperatively executed by all sensors to achieve better coverage. Two of our algorithms use the relative positions between sensors to optimize the coverage and energy savings. They provide 20% to 25% more energy savings than existing solutions. Our third algorithm is developed for networks without self-localization capabilities and supports the optimal deployment of such networks without requiring the use of expensive geolocation hardware or energy consuming localization algorithms. This is important for indoor monitoring applications since current localization algorithms cannot provide good accuracy for sensor relocation algorithms in such indoor environments. Also, no sensor redeployment algorithms, which can operate without selflocalization systems, developed before our work.viii

show abstract

Source Localization in Wireless Sensor Networks From Signal Time-of-Arrival Measurements

Cited by 229 publications

References 21 publications

Wideband Signal De-Interleaving (WSD)

Wideband Signal De-Interleaving (WSD)

Convex Optimization Algorithms for Multiple Source Localization Based on Received Signal Strength Measurements

Wireless Sensor Network Deployment

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