Spatial Field Reconstruction and Sensor Selection in Heterogeneous Sensor Networks With Stochastic Energy Harvesting - 2018


We tend to address the two fundamental issues of spatial field reconstruction and sensor selection in heterogeneous sensor networks. We have a tendency to consider the case where two varieties of sensors are deployed: the first consists of high-priced, prime quality sensors; and therefore the second, of cheap low quality sensors, which are activated solely if the intensity of the spatial field exceeds a pre-outlined activation threshold (e.g., wind sensors). Additionally, these sensors are powered by suggests that of energy harvesting and their time varying energy status impacts on the accuracy of the measurement that will be obtained. We have a tendency to then address the subsequent two vital issues: (i) a way to efficiently perform spatial field reconstruction based on measurements obtained simultaneously from each networks; and (ii) a way to perform query based mostly sensor set selection with predictive MSE performance guarantee. To overcome this problem, we tend to solve the first problem by developing a low complexity algorithm based mostly on the spatial best linear unbiased estimator (S-BLUE). Next, building on the S-BLUE, we have a tendency to address the second downside, and develop an economical algorithm for query primarily based sensor set selection with performance guarantee. Our algorithm is based on the Cross Entropy methodology which solves the combinatorial optimization downside in an efficient manner. We tend to gift a comprehensive study of the performance gain which will be obtained by augmenting the high-quality sensors with low-quality sensors using each artificial and real insurance storm surge database known as the Extreme Wind Storms Catalogue.

Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE : Modeling Spatial Trajectories with Attribute Representation Learning ABSTRACT: The widespread use of positioning devices has resulted in the generation of a large number of trajectories, each of which possesses
PROJECT TITLE : Exploring Spatial Significance via Hybrid Pyramidal Graph Network for Vehicle Re-Identification ABSTRACT: Spatial pooling operations are commonly used in existing methods for vehicle re-identification. These operations
PROJECT TITLE : Realization of Spatial Sparseness by Deep ReLU Nets With Massive Data ABSTRACT: The tremendous success of deep learning presents significant challenges that must be addressed immediately if we are to understand
PROJECT TITLE : A Novel Dynamic Model Capturing Spatial and Temporal Patterns for Facial Expression Analysis ABSTRACT: Incorporating spatial and temporal patterns present in facial behavior should substantially improve facial
PROJECT TITLE : Adaptive Pulse Wave Imaging Automated Spatial Vessel Wall Inhomogeneity Detection in Phantoms and in-Vivo ABSTRACT: Imaging the mechanical characteristics of the artery wall may aid in the diagnosis of vascular

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry