Computer Engineering Commons^™

Energy Harvesting And Storage: The Catalyst To The Power Constraint For Leveraging Internet Of Things (Iot) On Trains, Kelechi Nwogu

Computer and Electronics Engineering: Dissertations, Theses, and Student Research

The success of Wireless Sensor Networks is heavily constrained by its reliance on storage technology like batteries, which are a finite resource. Whilst the number of transistors in an IC doubles every 18 months, the energy density of batteries is relatively flat during the same time period. This is a key challenge in leveraging the Internet of Things on trains.

The gravity of this problem is increased by an order of magnitude when the network is to be scaled up to hundreds or thousands of nodes. Comprehensive research and development efforts have been devoted to building ultra-low power sensors. These …

Optimal Joint Transmission And Harvested Energy Scheduling For Renewable Energy Harvesting Enabled Cellular Network Under Coordinated Multi-Point Transmission, Zejue Wang, Hongjia Li, Dan Hu, Song Ci

Department of Electrical and Computer Engineering: Faculty Publications

On-grid energy consumption of base stations (BSs) contributes up a significant fraction of the total carbon dioxide (CO₂) emissions of cellular networks, among which remote radio units (RRUs) absorb most of the energy consumption. To eliminate the on-grid energy consumption and the corresponding CO₂ emission, we propose a new transmission framework, in which all RRUs and associated power amplifiers (PAs) are powered by hybrid energy sources including on-grid energy source and off-grid renewable energy source. Based on the framework, we pursue a systematic study on the joint transmission and harvested energy scheduling algorithm for the hybrid energy …

A Statistical Analysis Of Wind Speed Data Using Burr, Generalized Gamma, And Weibull Distributions In Antakya, Turkey, İlker Mert, Cuma Karakuş

Turkish Journal of Electrical Engineering and Computer Sciences

The wind energy potential of the Antakya area was statistically analyzed based 8 years of wind data sets (2002--2009). The 4-parameter Burr, 3-parameter generalized gamma, and conventional Weibull distributions were regarded as suitable statistical models for describing wind speed profiles. The suitability of the models was tested by $R^{2}$, \textit{RMSE}, chi-squared, and Kolmogorov--Smirnov analysis. According to goodness-of-fit tests, the Burr distribution was found to be more suitable than the generalized gamma or Weibull distributions for representing the actual probability of wind speed data for Antakya. Based on the capacity factors estimated by the Burr model at a hub height, the …