Wireless Sensor Networks—Node Localization for Various Industry Problems

Derr, Kurt; Manic, Milos

doi:10.1109/tii.2015.2396007

Cited by 59 publications

(31 citation statements)

References 27 publications

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“…All control system structures were tested on the nonlinear model, accepting the main values of the parameters given in [24]. This paper has considered a low-cost implementation of the controllers but this must be viewed in connection with the complexity of the control algorithms, and several approaches can be used [10], [12], [37], [13], [15], [31], [46].…”

Section: Discussionmentioning

confidence: 99%

Proportional-Integral-Derivative Gain-Scheduling Control of a Magnetic Levitation System

Bojan-Dragoş

Precup

Tomescu

et al. 2017

INT J COMPUT COMMUN

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The paper presents a gain-scheduling control design procedure for classical Proportional-Integral-Derivative controllers (PID-GS-C) for positioning system. The method is applied to a Magnetic Levitation System with Two Electromagnets (MLS2EM) laboratory equipment, which allows several experimental verifications of the proposed solution. The nonlinear model of MLS2EM is linearized at seven operating points. A state feedback control structure is first designed to stabilize the process. PID control and PID-GS-C structures are next designed to ensure zero steady-state control error and bumpless switching between PID controllers for the linearized models. Real-time experimental results are presented for validation.

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Section: Discussionmentioning

confidence: 99%

Proportional-Integral-Derivative Gain-Scheduling Control of a Magnetic Levitation System

Bojan-Dragoş

Precup

Tomescu

et al. 2017

INT J COMPUT COMMUN

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“…In [16] Delaunay triangulation and smoothing algorithm is introduced for determining the node location , where the sensor nodes has to be deployed for air pollutant monitoring and warning systems. About 13.7 % of recent scientific publications involves wireless sensor networks based localization aspects and target tracking [17].…”

Section: Related Workmentioning

confidence: 99%

Hybrid Firefly Variants Algorithm for Localization Optimization in WSN

Srideviponmalar¹,

Kumar²

2017

IJCIS

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Localization is one of the key issues in wireless sensor networks. Several algorithms and techniques have been introduced for localization. Localization is a procedural technique of estimating the sensor node location. In this paper, a novel three hybrid algorithms based on firefly is proposed for localization problem. Hybrid Genetic Algorithm-Firefly Localization Algorithm (GA-FFLA), Hybrid Differential Evolution-Firefly Localization Algorithm (DE-FFLA) and Hybrid Particle Swarm Optimization -Firefly Localization Algorithm (PSO-FFLA) are analyzed, designed and implemented to optimize the localization error. The localization algorithms are compared based on accuracy of estimation of location, time complexity and iterations required to achieve the accuracy. All the algorithms have hundred percent estimation accuracy but with variations in the number of fireflies' requirements, variation in time complexity and number of iteration requirements.

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“…As a result, all of these beacon signals have the same fixed transmission radius. In ripple localization algorithm, a beacon node transmits beacon signals at different power levels corresponding to different transmission radii so that these radii fall into certain pre-determined quantized intervals 1 . The beacon nodes are tested and calibrated so that transmission radii corresponding to different power levels are recorded for embedding in beacon messages.…”

Section: B Algorithm For Beacon Nodesmentioning

confidence: 99%

“…I N many applications of industrial wireless sensor networks, sensor nodes need to determine their own geographic positions. Examples of such industrial applications include gas leakage detection, industrial IoT, industrial fire detection, underground pipeline inspection, target tracking, habitat monitoring and area surveillance [1]- [3]. In such cases, unknown sensor nodes need to employ an intelligent localization algorithm to estimate their geographic position coordinates usually with the assistance of a few beacon nodes.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Energy Efficient Localization Using Variable Range Beacons in Industrial Wireless Sensor Networks

Farooq-i-Azam

Ansari

2016

IEEE Trans. Ind. Inf.

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In many applications of industrial wireless sensor networks, sensor nodes need to determine their own geographic position coordinates so that the collected data can be ascribed to the location from where it was gathered. We propose a novel intelligent localization algorithm which uses variable range beacon signals generated by varying the transmission power of beacon nodes. The algorithm does not use any additional hardware resources for ranging and estimates position using only radio connectivity by passively listening to the beacon signals. The algorithm is distributed, so each sensor node determines its own position and communication overhead is avoided. As the beacon nodes do not always transmit at maximum power and no transmission power is used by unknown sensor nodes for localization, the proposed algorithm is energy efficient. It also provides control over localization granularity. Simulation results show that the algorithm provides good accuracy under varying radio conditions.

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Wireless Sensor Networks—Node Localization for Various Industry Problems

Cited by 59 publications

References 27 publications

Proportional-Integral-Derivative Gain-Scheduling Control of a Magnetic Levitation System

Proportional-Integral-Derivative Gain-Scheduling Control of a Magnetic Levitation System

Hybrid Firefly Variants Algorithm for Localization Optimization in WSN

Intelligent Energy Efficient Localization Using Variable Range Beacons in Industrial Wireless Sensor Networks

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