EOC: Energy Optimization Coding for Wireless Nanosensor Networks in the Terahertz Band | Zendy

Long-Jun Huang | Zendy; Xin-Wei Yao | Zendy; Wan-Liang Wang | Zendy; Shi-Genshen | Zendy

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EOC: Energy Optimization Coding for Wireless Nanosensor Networks in the Terahertz Band

Author(s) -

Long-Jun Huang,

Xin-Wei Yao,

Wan-Liang Wang,

Shi-Genshen

Publication year - 2017

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2017.2665487

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

Wireless nanosensor networks (WNSNs), which consist of numerous nanosensors, offer a number of unprecedented and promising applications in the biomedical, environmental, industrial, and military fields. However, a single nanosensor in WNSNs has very limited capability as a result of nanoscale components, especially the extremely small nanobatteries. Therefore, energy efficiency has become an essential issue for WNSNs. In this paper, by considering the scenarios of transmitting binary source symbols in WNSNs, an energy optimization coding (EOC) for communication in WNSNs is proposed, and the energy model by jointly accounting for the energy consumption of both a transmitter and a receiver is presented. Based on the optimal source-word length and the optimal code-word length by solving an energy optimization problem, an energy-efficient coding scheme and the corresponding coding algorithm are presented. Simulation results show that EOC performs better energy efficiency than the existing nanonetwork minimum energy coding while requiring a smaller source-word length. Moreover, the proposed coding algorithm is more suitable for the scenarios of transmitting long binary source symbols.

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