Smart Systems for Information Processing in Sensor Networks

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Systems".

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 3809

Special Issue Editor


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Guest Editor
School of Engineering, Edith Cowan University, Joondalup, WA 6027, Australia
Interests: signal processing; wireless sensor networks; digital communications; image processing; electronic engineering; applied mathematics
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Special Issue Information

Dear Colleagues,

Signal processing has been the prime factor behind modern revolutionary progress in various fields including digital communications, biomedical engineering, control, security, and sensors, among many other disciplines. It deserves the title of the “missing link between disciplines”. For the last three decades, digital signal processing (DSP) has led the revolution of digital communications, which has connected all parts of the world. Now sensor signal processing is leading a new revolution with a significant impact on various applications in conjunction with the latest developments in the Internet of Things (IoT). Most existing applications utilize complex, multi-bit digital systems for information processing. In some applications like sensor networks, however, systems may be confronted with limited storage, power, or computation capabilities, hence ordinary complex techniques of signal processing would not be applicable. This Special Issue of Information will focus on smart signal processing systems, like short word-length (SWL) or other innovative techniques, that enable efficient processing in sensor networks with minimal use of energy, computation, and memory.


Topics to be covered include, but are not limited to the following:

  • Adaptive single-bit systems
  • Optimization in SWL systems
  • SWL filters
  • SWL communications
  • SWL for wireless sensor networks (WSNs)
  • SWL in biomedical applications
  • Energy-efficient techniques for WSNs
  • Energy-efficient routing and transmission methods for WSNs
  • Energy-efficient and low-cost sensor design

Prof. Dr. Zahir M. Hussain
Guest Editor

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Keywords

  • signal processing
  • efficient signal processing
  • information processing in WSNs
  • efficient computation techniques for WSNs
  • low-energy DSP
  • low-energy digital systems
  • energy-efficient wireless-sensor networks (WSNs)
  • low-energy sensors
  • efficient sensor communications
  • efficient routing for WSNs
  • energy-efficient protocols for WSNs
  • low-cost sensors
  • efficient multi-bit systems
  • sigma-delta modulation
  • short word-length (SWL)
  • ternary systems
  • single-bit systems
  • efficient sampling
  • adaptive filters

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Published Papers (1 paper)

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Research

20 pages, 4576 KiB  
Article
Short Word-Length Entering Compressive Sensing Domain: Improved Energy Efficiency in Wireless Sensor Networks
by Nuha A. S. Alwan and Zahir M. Hussain
Information 2021, 12(10), 415; https://doi.org/10.3390/info12100415 - 11 Oct 2021
Viewed by 1853
Abstract
This work combines compressive sensing and short word-length techniques to achieve localization and target tracking in wireless sensor networks with energy-efficient communication between the network anchors and the fusion center. Gradient descent localization is performed using time-of-arrival (TOA) data which are indicative of [...] Read more.
This work combines compressive sensing and short word-length techniques to achieve localization and target tracking in wireless sensor networks with energy-efficient communication between the network anchors and the fusion center. Gradient descent localization is performed using time-of-arrival (TOA) data which are indicative of the distance between anchors and the target thereby achieving range-based localization. The short word-length techniques considered are delta modulation and sigma-delta modulation. The energy efficiency is due to the reduction of the data volume transmitted from anchors to the fusion center by employing any of the two delta modulation variants with compressive sensing techniques. Delta modulation allows the transmission of one bit per TOA sample. The communication energy efficiency is increased by RⱮ, R ≥ 1, where R is the sample reduction ratio of compressive sensing, and Ɱ is the number of bits originally present in a TOA-sample word. It is found that the localization system involving sigma-delta modulation has a superior performance to that using delta-modulation or pure compressive sampling alone, in terms of both energy efficiency and localization error in the presence of TOA measurement noise and transmission noise, owing to the noise shaping property of sigma-delta modulation. Full article
(This article belongs to the Special Issue Smart Systems for Information Processing in Sensor Networks)
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