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Energy Harvesting and Storage: The Catalyst to the Power Constraint for Leveraging Internet of Things (IoT) on Trains


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.


One of the biggest constraints in Wireless Sensor Network Technology is energy consumption. Several wireless sensors utilize different approaches to conserve energy, but the energy stored is eventually exhausted and a new power source (battery) is needed. Majority of the batteries have less than a year lifespan in moderate use. Some advances have been made in the battery technology. One such advancement includes using batteries made with Lithium Thionyl Chloride, which is advertised to have a ten year shelf life in certain circumstances. However, it is inconvenient to replace and dispose of dead batteries, which can be toxic to the environment. For many WSN applications, energy harvesting is more attractive.


Figure 3–1: Ambient Energy Power Source before conversion

Figure 3–1: Ambient Energy Power Source before conversion

As expected, radiant sources perform better outdoor than indoor. The ambient energy must be selected based on the sensor’s environment. Radiated sources in the rail environment pose a problem due to assuring the surface of the harvester will be clear of opaque objects.

Figure 3–3: Energy Harvesting System Block Diagram

Figure 3–3: Energy Harvesting System Block Diagram

A hybrid option (combining two or more of the ambient energy sources) would ultimately be more efficient. The circuit in Appendix D supports multiple energy harvesting sources. A concise energy harvesting block diagram is shown in Figure 3–3.


Figure 4–3: Zoomed into 5/27

Figure 4–3: Zoomed into 5/27

Surprisingly, the node using the most power was in location 33. Figure 4–3 and Figure 4– 4 show a closer look of the column graph for 5/27 and 5/28 respectively. The bars appear in the order of where the node was placed. In other words, the first bar corresponds to position 1 (P1) and the second to position 2 (P2)…and so on.

Figure 4–22: Time Domain

Figure 4–22: Time Domain

The amplitude of acceleration observed for the loaded train exceed the maximum capacity of the data logger which is +-16g. The data was gathered at a sample rate of 3200.00Hz. The combined duration of the recording was 872.32 seconds. Figure 4–22 is a graphical representation of the total 2791421 data points per axis.

Figure 4–31: Y Channel Frequency

Figure 4–31: Y Channel Frequency

After the maximum displacement was determine, the next step was to determine the resonance frequency of the cars, which was used to tune the natural frequency of the harvester to maximize power generated. The frequency was determined by performing a Fourier transform of the Time domain data.


This thesis examined the study of existing and emerging Energy Harvesting technologies for use in freight car sensors. Specifically, the pros and cons of energy harvesting technologies such as photovoltaic, electrodynamics, thermoelectric or piezoelectric were presented. Energy harvesting is no longer a myth but a feasible method of powering devices using energy harvested from the environment. Vibration energy sources are recommended for the freight car environment. The first step was to determine the power requirement from the data collected from 100 sensor nodes arranged in a linear topology. The physical layer of the sensors conforms to IEEE 802.15.4 standard. Maximum current draw observed was 0.96mA, equating to a power of 3.168mW.

Source: University of Nebraska-Lincoln
Author: Kelechi Nwogu

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