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Energy Harvesting in Nanonetworks

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Modeling, Methodologies and Tools for Molecular and Nano-scale Communications

Part of the book series: Modeling and Optimization in Science and Technologies ((MOST,volume 9))

Abstract

The goal of this chapter is to review the process, issues, and challenges of energy harvesting in nanonetworks, composed of nanonodes that are nano to micro meters in size. A nanonode consisting of nan-memory, a nano-processor, nano-harvesters, ultra nano-capacitor, and a nano-transceiver harvests the energy required for its operations, such as processing and communication. The energy harvesting process in nanonetworks differs from traditional networks (e.g. wireless sensor networks, RFID) due to their unique characteristics such as nanoscale, communication model, and molecular operating environment. After reviewing the energy harvesting process and sources, we introduce the communication model, which is the main source of energy consumption for nanonodes. This is followed by a discussion on the models for joint energy harvesting and consumption processes. Finally, we describe approaches for optimizing the energy consumption process, which includes optimum data packet design, optimal energy utilization, energy consumption scheduling, and energy-harvesting-aware protocols.

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Notes

  1. 1.

    Lexicographic optimization is a form of multi-criteria (multi-objective) optimization in which the various objectives under consideration cannot be quantitatively traded off between each other, at least not in a meaningful and numerically tractable way. The lexicographic method assumes that the objectives can be ranked in the order of importance. It can be assumed, without loss of generality, that the k objective functions are in the order of importance so that \(f_1\) is the most important and \(f_k\) the least important to the decision maker. Then, the lexicographic method consists of solving a sequence of single objective optimization problems [47].

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Author information

Authors and Affiliations

  1. Department of Computer and Information Sciences, Temple University, Philadelphia, PA, USA

    Shahram Mohrehkesh

  2. Department of Computer Science, Old Dominion University, Norfolk, VA, USA

    Michele C. Weigle

  3. Department of Computer Science, Missouri University of Science and Technology, Rolla, MO, USA

    Sajal K. Das

Authors
  1. Shahram Mohrehkesh
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  2. Michele C. Weigle
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  3. Sajal K. Das
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Correspondence to Shahram Mohrehkesh .

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Editors and Affiliations

  1. Department of Computer Science, University of Massachusetts, Boston, Massachusetts, USA

    Junichi Suzuki

  2. Graduate School of Biological Sciences, Osaka University, Osaka, Japan

    Tadashi Nakano

  3. 57006D Applied Science Building, Pennsylvania State University, State College, Pennsylvania, USA

    Michael John Moore

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Mohrehkesh, S., Weigle, M.C., Das, S.K. (2017). Energy Harvesting in Nanonetworks. In: Suzuki, J., Nakano, T., Moore, M. (eds) Modeling, Methodologies and Tools for Molecular and Nano-scale Communications. Modeling and Optimization in Science and Technologies, vol 9. Springer, Cham. https://doi.org/10.1007/978-3-319-50688-3_14

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  • DOI: https://doi.org/10.1007/978-3-319-50688-3_14

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