Energy Harvesting for Autonomously-Powered Sensor Networks
1. Los Alamos National Laboratory
Energy Harvesting for Autonomously-
Powered Sensor Networks
Scott Ouellette, Ph.D.
R&D Engineer
Advanced Engineering Analysis Group
Los Alamos National Laboratory
Los Alamos, New Mexico
A systems-level paradigm for energy harvesting to
power the connected world
LA-UR-16-28210
Operated by Los Alamos National Security, LLC for the U.S. Department of Energy's NNSA
2. Los Alamos National Laboratory
Motivation – Internet of Things (IoT) for the Connected
World
• Many definitions for IoT depending on perspective: applications,
technological, benefits, etc.
• In general, the Internet of Things is the process by which environmental or
operational data is networked and processed to become actionable
information.
• Examples of IoT are:
• Sensors for microbial awareness in cities
• Connected automobiles / autonomous driving
• Smart buildings: adaptive lighting and air conditioning
• Structural Health Monitoring (SHM)
Ambient Energy System Data Information
Energy Harvesting
IoT
3. Los Alamos National Laboratory
Uses of IoT in Business/Industry
Information and Analysis Automation and Control
1. Tracking Behavior
• Inventory and supply-chain
management
2. Enhanced Situational Awareness
• Damage detection in composite
structures using Acoustic
Wavenumber Spectroscopy
3. Sensor-Driven Decision Analytics
• Condition-based aircraft
maintenance vs. time-based
maintenance
1. Process Optimization
• Continuous, precise adjustments
in manufacturing processes
2. Optimized Resource Consumption
• Intelligent energy grid to match
consumption demand / prevent
power black-outs
3. Complex Autonomous Systems
• Adaptive automobile cruise
control and collision avoidance
systems
M. Chui, M. Löffler, and R. Roberts, “The Internet of Things | McKinsey & Company.”
[Online]. Available: http://www.mckinsey.com/industries/high-tech/our-insights/the-internet-
of-things#0. [Accessed: 21-Oct-2016].
4. Los Alamos National Laboratory
Example of IoT Project Dataflow
“IoT streaming analytics, data production and workflow services added to Azure,” The Fire
Hose, 29-Oct-2014.
Event Producers Collection Ingestor Transformation
Long-term
Storage
Presentation
and Action
5. Los Alamos National Laboratory
Projected Growth of Deployed Sensors
Cerasis_IT, “The IOT Supply Chain Benefits Coming Clearer,” Transportation Management
Company | Cerasis, 14-Jul-2015. [Online]. Available: http://cerasis.com/2015/07/14/iot-
supply-chain/. [Accessed: 24-Oct-2016].
50 billion
6. Los Alamos National Laboratory
Motivation for Energy Harvesting Approach
• Advances in semiconductor manufacturing technology have drastically outpaced
battery storage capacity
• Power consumption of CMOS integrated circuits are also continuing to decrease
• As such, energy harvesting as a means of powering microprocessors continues to
become more viable
G. Park, T. Rosing, M. Todd, C. Farrar, and W. Hodgkiss, “Energy Harvesting for Structural
Health Monitoring Sensor Networks,” J. Infrastruct. Syst., vol. 14, no. 1, pp. 64–79, 2008.
7. Los Alamos National Laboratory
Purpose of Energy Harvesting Paradigm
• Desire to reduce / eliminate costs associated with conventional battery replacement
and chemical waste
• Enabling technology for IoT and SHM sensor networks
• Ultimate goal is to provide autonomous power to sensor network for time scales on
the order of the lifetime of the host structure
H. Boukabache, C. Escriba, and J.-Y. Fourniols, “Toward Smart Aerospace Structures:
Design of a Piezoelectric Sensor and Its Analog Interface for Flaw Detection,” Sensors, vol.
14, no. 11, pp. 20543–20561, Oct. 2014.
8. Los Alamos National Laboratory
An Analogy of the Energy Harvesting Approach
• The human body is a mixed, wired and wireless, network of sensor performing
continuous measurements (sensing) which are transmitted to the brain
(communication) and converted to diagnostic information (local computing)
• The body is nourished with food, which is then converted (transduced) to metabolic
energy
• Digestive process (conditioning) requires a small amount of energy, but is overall
highly efficient
• Excess energy is converted to fat (storage) which could be used when access to
nourishment becomes sparse (management)
9. Los Alamos National Laboratory
Internet of Things
Conventional Powering Approach for IoT Networks
Power Source
Battery or Mains
Power
Central
Computing Server
and Storage
Database
Sensor Node
Sensor Node Sensor Node
Sensor Node
Battery or Mains
Power
Battery or Mains
Power
End Users
10. Los Alamos National Laboratory
Systematic Energy Harvesting Paradigm for
Autonomously-Powered Sensor Networks
Solar
Vibration
Electrochemical
Thermal
Radio Frequency
AC
DC
EnergyDensity
AC-DC
Converter
Sufficient
Power?
No
Yes
Energy
Buffer
DC-DC
Converter
Voltage
Regulator
Power
Management
Super Capacitor
OR
Rechargeable
Battery
Energy Source Power Conditioning & Management
S. A. Ouellette, “Energy Harvesting Paradigms for Autonomously-Powered Sensor
Networks,” UNIVERSITY OF CALIFORNIA, SAN DIEGO, 2015.
11. Los Alamos National Laboratory
Energy Harvesting at LANL
• Development of a multi-source
energy harvesting system for
structural health monitoring of wind
turbine blades
• Transduction schemes studied:
• Solar / Photovoltaic
• Vibration
• Thermal-Electric Generation
• A multi-source energy combination
circuit was prototyped
C. P. Carlson, A. D. Schlichting, S. Ouellette, K. Farinholt, and G. Park, “Energy Harvesting
to Power Sensing Hardware Onboard Wind Turbine Blade,” in Structural Dynamics and
Renewable Energy, Volume 1, T. Proulx, Ed. Springer New York, 2011, pp. 291–304.
12. Los Alamos National Laboratory
Energy Harvesting at LANL
S. G. Taylor et al., “A mobile-agent-based wireless sensing network for structural monitoring
applications,” Meas. Sci. Technol., vol. 20, no. 4, p. 045201, 2009.
• Multi-source energy combination circuit was tested as a power supply on
prototype mobile wireless interrogation device (WID 2.0)
• Custom electronic devices have been developed (WID 3.0 / WiDAQ) for
application-specific health monitoring of wind turbine blades
14. Los Alamos National Laboratory
Problems that need solutions for successful
deployment of IoT systems
• Network security and data privacy
• Computers are bad at keeping
secrets
• Interoperability of hardware /
devices
• Too many communication
protocols / standards, no
unification
• Complexity of hardware and
networking
• Energy / Powering devices
• Need replacement for batteries
15. Los Alamos National Laboratory
Problems that need solutions for successful
deployment of IoT systems
• Network security and data privacy
• Computers are bad at keeping
secrets
• Interoperability of hardware /
devices
• Too many communication
protocols / standards, no
unification
• Complexity of hardware and
networking
• Energy / Powering devices
• Need replacement for batteries
16. Los Alamos National Laboratory
Problems that need solutions for successful
deployment of EH-enabled IoT systems
• Energy storage
• Improvements to rechargeable battery energy density
• Improvements to number of super-capacitor recharge cycles and thermal
resilience
• Reduce Transmission Power Consumption
• New protocols for low-power data transmission
• Improvements to network design protocols
• Power Management Circuit Design and Efficiency
• Reduce consumption overhead of circuitry used for combining and
managing power storage and usage within sensor nodes
17. Los Alamos National Laboratory
LANL collaborators on Energy Harvesting
Technologies
• Prof. Gyuhae Park – Chonnam National University
• Dr. Kevin Farinholt – Luna Innovations Incorporated
• Prof. Steve Anton – Tennessee Technological University
• Dr. Scott Ouellette – Los Alamos National Laboratory
Kevin FarinholtSteve Anton Gyuhae ParkScott Ouellette
18. Los Alamos National Laboratory
Acknowledgements
• National Science Foundation
• Korea Global Research and Development Centers (GRDC)
• Los Alamos National Laboratory Engineering Institute
• University of California, San Diego
• Professor Gyuhae Park, Professor Reon Kang, Professor Jung-Ryul
Lee
