Electrical & Computer Engineering, Department of

 

Date of this Version

Summer 5-2015

Comments

A THESIS Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfilment of Requirements For the Degree of Master of Science, Major: Telecommunication Engineering, Under the Supervision of Professor Hamid Sharif. Lincoln, Nebraska: May, 2015

Copyright (c) 2015 Kelechi Obiajulu Nwogu

Abstract

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 ultra-low power sensors are configured to have very low duty cycle and are practically asleep most of the time. Short duty cycle might extend the battery life, but the energy will inevitably run out.

Energy harvesting has emerged as a viable solution to the energy loss issue by ensuring sensors never run out of energy. Though, there could be a significant upfront cost in employing energy harvesting; several studies have shown it takes 24 months or less to break even. Energy harvesters, unlike batteries, are not commonly a one size fits all; some customization is required based on the environment.

Mechanical harvesting sources are ideal for the rail environment because this environment has an abundant amount of vibration energy.

This thesis focuses on how Energy Harvesting and Storage can be used as the sole power source for the Wireless Sensor Networks that make up the Internet of Things in the railroad industry. It synthesizes the various works carried out in the energy harvesting techniques like solar and piezo, and storage technology like Lithium-ion batteries and Supercapacitors.

After introducing the general concept of Internet of Things, Energy Harvesting, and Storage, this document provides an in-depth analysis of the data gathered during this research. The data was used to determine sensor node power consumption when arranged in a linear topology like the train, available ambient energy on the train, and optimal energy harvesting sources for the railroad.

Adviser: Hamid Sharif