Mathematical Theories of Distributed Sensor Networks

Iyengar, S. Sitharama; Boroojeni, Kianoosh G.; Balakrishnan, N.

doi:10.1007/978-1-4419-8420-3

Cited by 13 publications

(4 citation statements)

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“…The boundaries of each region are called Voronoi edges and the intersections of Voronoi edges are called Voronoi vertices. For more details see [2,16,13].…”

Section: Voronoi Diagramsmentioning

confidence: 99%

Modified refinement algorithm to construct Lyapunov functions using meshless collocation

Mohammed

Giesl

2023

JCD

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<p style='text-indent:20px;'>Lyapunov functions are functions with negative derivative along solutions of a given ordinary differential equation. Moreover, sublevel sets of a Lyapunov function are subsets of the domain of attraction of the equilibrium. One of the numerical construction methods for Lyapunov functions uses meshless collocation with radial basis functions.</p><p style='text-indent:20px;'>Recently, this method was combined with a grid refinement algorithm (GRA) to reduce the number of collocation points needed to construct Lyapunov functions. However, depending on the choice of the initial set of collocation point, the algorithm can terminate, failing to compute a Lyapunov function. In this paper, we propose a modified grid refinement algorithm (MGRA), which overcomes these shortcomings by adding appropriate collocation points using a clustering algorithm. The modified algorithm is applied to two- and three-dimensional examples.</p>

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“…The boundaries of each region are called Voronoi edges and the intersections of Voronoi edges are called Voronoi vertices. For more details see [2,16,13].…”

Section: Voronoi Diagramsmentioning

confidence: 99%

Modified refinement algorithm to construct Lyapunov functions using meshless collocation

Mohammed

Giesl

2023

JCD

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“…For a broader treatment on this, there are some other researches on this topic that considered electric energy dispatch in presence of DRRs [14,15]. For a broader treatment on this, please see [16,17,18,19,20,21,22,23,24,25,26,27].…”

Section: Related Workmentioning

confidence: 99%

Optimal Two-Tier Forecasting Power Generation Model in Smart Grids

Boroojeni¹,

Mokhtari²,

Amini³

et al. 2015

Preprint

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There has been an increasing trend in the electric power system from a centralized generation-driven grid to a more reliable, environmental friendly, and customer-driven grid. One of the most important issues which the designers of smart grids need to deal with is to forecast the fluctuations of power demand and generation in order to make the power system facilities more flexible to the variable nature of renewable power resources and demand-side. This paper proposes a novel two-tier scheme for forecasting the power demand and generation in a general residential electrical gird which uses the distributed renewable resources as the primary energy resource. The proposed forecasting scheme has two tiers: long-term demand/generation forecaster which is based on Maximum-Likelihood Estimator (MLE) and real-time demand/generation forecaster which is based on Auto-Regressive Integrated Moving-Average (ARIMA) model. The paper also shows that how bulk generation improves the adequacy of proposed residential system by canceling-out the forecasters estimation errors which are in the form of Gaussian White noises.

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“…The management of such complex systems is part of the present Big Data paradigm. Details on sampling techniques, distributed smart monitoring, and mathematical theories of distributed sensor networks can be found in [ 1 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Monitoring Soil and Ambient Parameters in the IoT Precision Agriculture Scenario: An Original Modeling Approach Dedicated to Low-Cost Soil Water Content Sensors

Placidi

Morbidelli

Fortunati

et al. 2021

Sensors

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A low power wireless sensor network based on LoRaWAN protocol was designed with a focus on the IoT low-cost Precision Agriculture applications, such as greenhouse sensing and actuation. All subsystems used in this research are designed by using commercial components and free or open-source software libraries. The whole system was implemented to demonstrate the feasibility of a modular system built with cheap off-the-shelf components, including sensors. The experimental outputs were collected and stored in a database managed by a virtual machine running in a cloud service. The collected data can be visualized in real time by the user with a graphical interface. The reliability of the whole system was proven during a continued experiment with two natural soils, Loamy Sand and Silty Loam. Regarding soil parameters, the system performance has been compared with that of a reference sensor from Sentek. Measurements highlighted a good agreement for the temperature within the supposed accuracy of the adopted sensors and a non-constant sensitivity for the low-cost volumetric water contents (VWC) sensor. Finally, for the low-cost VWC sensor we implemented a novel procedure to optimize the parameters of the non-linear fitting equation correlating its analog voltage output with the reference VWC.

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Mathematical Theories of Distributed Sensor Networks

Cited by 13 publications

References 0 publications

Modified refinement algorithm to construct Lyapunov functions using meshless collocation

Modified refinement algorithm to construct Lyapunov functions using meshless collocation

Optimal Two-Tier Forecasting Power Generation Model in Smart Grids

Monitoring Soil and Ambient Parameters in the IoT Precision Agriculture Scenario: An Original Modeling Approach Dedicated to Low-Cost Soil Water Content Sensors

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