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Compressive Sensing-Based IoT Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (10 December 2022) | Viewed by 2553

Special Issue Editor


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Guest Editor
Department of Applied Informatics, University of Macedonia, 54124 Thessaloniki, Greece
Interests: Internet of Things (IoT); artificial intelligence (AI); haptic communications; QoS control; big data analysis; cloud computing; digital twins
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) is a promising technology for 5G communications systems, providing a fully interactive and interconnected environment with billions of devices connected in an integrated heterogeneous network, thus supporting multiple users, services, and applications. Compressive sensing (CS) is a rapidly emerging collection of optimization tools, initially applied to reduced complexity data acquisition, but also directly applicable to technologies such as IoT. Compressive sensing offers reduced effective parameter dimensionality and most importantly reduction in computational and implementation complexity. The integration of CS and IoT technology will surely provide more effective and low-complexity/cost data management and information extraction. We invite researchers to publish original papers connecting CS theory and IoT network technology together in terms of optimized performance and reduced complexity.

Potential topics include but are not limited to the following:

  • Compressive sensing theory and IoT network performance optimization
  • Compressive sensing reconstruction algorithms for IoT networks
  • Compressive sensing and information theory for IoT technology
  • IoT services and applications based on compressive sensing
  • Compressive sensing-based IoT data management
  • Compressive sensing applicability on IoT emerging solutions
  • IoT decentralized network design with sparse operational parameters
  • Compressive sensing and IoT network heterogeneity
  • Compressive sensing-based energy efficiency in IoT networks
  • Compressive sensing and security over IoT networks
  • Compressive sensing mobility in IoT networks
  • Industrial and healthcare IoT applications based on compressive sensing
  • Compressive sensing sparsity-based IoT network complexity
  • Compressive sensing-based IoT network scalability and reliability optimization
  • Compressive sensing-based NOMA schemes for IoT networks
  • Compressive sensing-based functionality of resource-constrained IoT devices
  • Compressive sensing-based data processing and information extraction in wireless sensor IoT networks
  • Compressive sensing-based wideband spectrum sensing for Cognitive IoT networks
  • Compressive sensing sparsity-based IoT wireless channel modeling and estimation

Dr. Psannis Kostas
Guest Editor

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Published Papers (1 paper)

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Research

17 pages, 595 KiB  
Article
An Efficient Compressive Sensing Event-Detection Scheme for Internet of Things System Based on Sparse-Graph Codes
by Jun Cai, Xin Xu, Hongpeng Zhu and Jian Cheng
Sensors 2023, 23(10), 4620; https://doi.org/10.3390/s23104620 - 10 May 2023
Viewed by 1506
Abstract
This work studied the event-detection problem in an Internet of Things (IoT) system, where a group of sensor nodes are placed in the region of interest to capture sparse active event sources. Using compressive sensing (CS), the event-detection problem is modeled as recovering [...] Read more.
This work studied the event-detection problem in an Internet of Things (IoT) system, where a group of sensor nodes are placed in the region of interest to capture sparse active event sources. Using compressive sensing (CS), the event-detection problem is modeled as recovering the high-dimensional integer-valued sparse signal from incomplete linear measurements. We show that the sensing process in IoT system produces an equivalent integer CS using sparse graph codes at the sink node, for which one can devise a simple deterministic construction of a sparse measurement matrix and an efficient integer-valued signal recovery algorithm. We validated the determined measurement matrix, uniquely determined the signal coefficients, and performed an asymptotic analysis to examine the performance of the proposed approach, namely event detection with integer sum peeling (ISP), with the density evolution method. Simulation results show that the proposed ISP approach achieves a significantly higher performance compared to existing literature at various simulation scenario and match that of the theoretical results. Full article
(This article belongs to the Special Issue Compressive Sensing-Based IoT Applications)
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