Field Systems Project

People: Hoda Ayatollahi, Nadir Adam, Cristiano Tapparello and Wendi Heinzelman
Kai Shen (PI), Gaurav Sharma and Tolga Soyata

Project overview:
Intelligent field systems like highway traffic management or area surveillance require substantial data collection in the field. Processing of this data in the field requires processing power that is not attainable via low-powered embedded devices. Recent advances in GPU technology afford embedded devices powered by advanced processors such as TEGRA3 to process the acquired data in the field. Intelligent field systems are traditionally powered by self-sustainable power sources such solar panels and the excess energy generated by the power source is buffered in rechargeable batteries. As an alternative to rechargeable batteries, our research focuses on the application of a different buffering mechanism using super-capacitors. Super-capacitors have near-infinite lifetime and are manufactured using significantly more environmentally-friendly materials as compared to rechargeable batteries. Also, their remaining energy can be estimated with better than 1% accuracy. This feature permits a new set of energy-aware applications by using super-capacitors. Despite these advantages, super-capacitors only have a tenth of the energy density that of rechargeables. Our research is centered around building field systems that can be powered via solar panels by using super-capacitors as the storage medium. Our research goal is to create a new class of environmentally-friendly field systems that amass a significant compute-capability, which are virtually maintenance-free and can be deployed in areas where maintenance is of significant premium.


  • - Simulation of communication systems and network protocols over realistic device operations is seen as a necessary task before implementation, because it allows for a flexible and fast, but still accurate, testing of the system evolution.
  • - The problem of designing and simulating optimal communication protocols for energy harvesting wireless networks requires an accurate modeling of the energy harvesting process and a consequent redesign of the simulation framework.

Network Simulator 3 (ns-3) includes an energy framework that allows users to simulate the energy consumption at a node as well as to determine the overall network lifetime under specific conditions. This framework adds sufficient support to ns-3 to devise simulations that include the energy consumption of the communication network.

Our Contributions:

Figure 1: ns-3 Energy Harvesting Framework. Our contributions are shown by the dashed lines.

In [1,2] we proposed and implemented an extension of the ns-3 energy framework in order to explicitly introduce the concept of an energy harvester. Starting from the definition of a general energy harvester, we provide the implementation of two simple models for the energy harvester. In addition, we introduced the concept of an energy predictor, that gathers information from the energy source and harvester and uses this information to predict the amount of energy that will be available in the future. Finally, we extended the current energy framework to include a model for a supercapacitor energy source and a device energy model for the energy consumption of a sensor.

Code Included in ns-3:
  • - Energy Harvester interface and Basic Energy Harvester [Since ns-3.21, download]

Code Under Review for Inclusion in ns-3:
  • - New models for energy sources, converters, predictors, and sensor models
  • - LithiumIonEnergySource helper
  • (Additional informations can be found on the ns-3.23 release planning webpage)


Power and energy consumption are the most important factors in extending the lifetime of Wireless Sensor Networks (WSN). Many energy efficiency techniques, that consider both the transmission and circuit power consumption have been proposed for both the case of Single-Input Single-Output (SISO) and Multiple-Input Multiple-Output (MIMO) WSNs. However, the power consumption of the receiver should also be considered in order to maximize the network lifetime.

Multiple-Input Multiple-Output (MIMO) radio antenna technology is a promising approach to improve the energy efficiency of wireless communications. This technology is employed in WSNs due to its potential to dramatically improve the data throughput and radio energy efficiency without increasing the total transmission power. Using multiple antennas at both the transmitter and the receiver, MIMO systems spread the transmission power among different antennas in order to achieve a power gain that increases the bandwidth efficiency for the same Bit-Error-Rate (BER) requirement.
Several energy efficient MIMO protocols have been proposed. While these protocols consider both the circuit and the transmission power required to exchange data packets, in order to optimize the energy usage, most of them focus on minimizing the transmission power for a certain transmission distance or communication delay.

Our Contributions:
In [3] we introduced a novel communication protocol for Multiple-Input Multiple-Output (MIMO) WSNs. In this protocol, the number of antennas to be used at both the transmitter and receiver are selected according to the energy consumption of the scheme, the remaining energy at the nodes, the distance between the nodes, and the target bit error rate. Starting from a policy that selects the optimal number of antennas, we then proposed 3 low complexity heuristics with different information requirements. Numerical results show that our proposed communication protocols dramatically outperform the performance of a traditional fixed MIMO system in terms of energy consumption and system lifetime.

Related Publications

  1. C. Tapparello, H. Ayatollahi and W. Heinzelman, "Energy Harvesting Framework for Network Simulator 3 (ns-3)," in Proceedings of 2nd International Workshop on Energy Neutral Sensing Systems (ENSsys), Memphis, TN, USA. November 6, 2014. [PDF]
  2. C. Tapparello, H. Ayatollahi and W. Heinzelman, "Extending the Energy Framework for Network Simulator 3 (ns-3)," in Workshop on ns-3 (WNS3) - Poster Session, Atlanta, GA, USA. May 7, 2014. [PDF]
  3. H. Ayatollahi, C. Tapparello and W. Heinzelman, "Transmitter-Receiver Energy Efficiency: A Trade-off in MIMO Wireless Sensor Networks," in IEEE Wireless Communications and Networking Conference (WCNC), New Orleans, LA, USA. March 9-12, 2015. [PDF]
  4. T. Soyata, L. Copeland and W. Heinzelman, "RF Energy Harvesting for Embedded Systems: A Survey of Tradeoffs and Methodology," in IEEE Circuits and Systems Magazine, Vol. 16, Number 1, February 2016.
  5. M. Wijesundara, C. Tapparello, A. Gamage, Y. Gokulan, L. Gittelson, T. Howard and W. Heinzelman, "Design of a Kinetic Energy Harvester for Elephant Mounted Wireless Sensor Nodes of JumboNet," in IEEE GLOBECOM, Washington, D.C., USA. Dec. 4-8, 2016.
  6. K. Sarpong Adu-Manu, C. Tapparello, W. Heinzelman, F. Apietu Katsriku, J.-D. Abdulai, "Water Quality Monitoring Using Wireless Sensor Networks: Current Trends and Future Research Directions," accepted for publication in ACM Transactions on Sensor Networks, 2017.
  7. H. Ayatollahi, C. Tapparello and W. Heinzelman, "Reinforcement Learning in MIMO Wireless Networks with Energy Harvesting," Under Review.
  8. K. Sarpong Adu-Manu, N. Adam, C. Tapparello, H. Ayatollahi and W. Heinzelman "Energy-Harvesting Wireless Sensor Networks (EH-WSNs): A Review," Under Review.
  9. H. Ayatollahi, C. Tapparello and W. Heinzelman, "MAC-LEAP: Multi-Antenna, Cross Layer, Energy Adaptive Protocol," Under Review.