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Underwater IoT Network by Blind MIMO OFDM Transceiver Based on Probabilistic Stones Blind Source Separation

Underwater IoT Network by Blind MIMO OFDM Transceiver Based on Probabilistic Stones Blind Source... Telecommunications systems with Multi-Input Multi-Output (MIMO) structure using Orthogonal Frequency Division Modulation (OFDM) have great potential for efficient application to a network of Internet of Things (IoT) at a high data rate. When the IoT network is among the underwater sensory devices known as the Internet of Underwater Things (IoUT), the electromagnetic wave cannot play the role of baseband signal due to rapid fall-off inside the water. Thus, acoustic OFDM is a reliable replacement for conventional OFDM inside the water. A blind structure for MIMO acoustic OFDM using Independent Component Analysis (ICA) brings even further advantages in data rate and energy consumption by avoiding the required pilot and preamble data. This research work presents a blind MIMO Acoustic OFDM blind transceiver for IoUT based on Probabilistic Stone’s Blind Source Separation (PS-BSS). The proposed technique has multiple times lower complexity compared to the ICA-based technique while maintaining a comparable efficiency. As observed in the results carried out with 100 Monte Carlo runs of transmission of random data bits over a highly sparse channel that is the common case of an underwater environment, the proposed PS-BSS-based technique dominates the ICA-based one, and as the sparseness of the channel decreases, its efficiency is comparable to the ICA-based technique. Thus, in the case of a highly sparse channel, the proposed technique is superior in both aspects of efficiency and complexity, while over lower sparseness, due to its comparative efficiency, it can be hired as an optimum technique fulfilling a fair tradeoff between efficiency and complexity. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Sensor Networks (TOSN) Association for Computing Machinery

Underwater IoT Network by Blind MIMO OFDM Transceiver Based on Probabilistic Stones Blind Source Separation

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2022 Association for Computing Machinery.
ISSN
1550-4859
eISSN
1550-4867
DOI
10.1145/3462674
Publisher site
See Article on Publisher Site

Abstract

Telecommunications systems with Multi-Input Multi-Output (MIMO) structure using Orthogonal Frequency Division Modulation (OFDM) have great potential for efficient application to a network of Internet of Things (IoT) at a high data rate. When the IoT network is among the underwater sensory devices known as the Internet of Underwater Things (IoUT), the electromagnetic wave cannot play the role of baseband signal due to rapid fall-off inside the water. Thus, acoustic OFDM is a reliable replacement for conventional OFDM inside the water. A blind structure for MIMO acoustic OFDM using Independent Component Analysis (ICA) brings even further advantages in data rate and energy consumption by avoiding the required pilot and preamble data. This research work presents a blind MIMO Acoustic OFDM blind transceiver for IoUT based on Probabilistic Stone’s Blind Source Separation (PS-BSS). The proposed technique has multiple times lower complexity compared to the ICA-based technique while maintaining a comparable efficiency. As observed in the results carried out with 100 Monte Carlo runs of transmission of random data bits over a highly sparse channel that is the common case of an underwater environment, the proposed PS-BSS-based technique dominates the ICA-based one, and as the sparseness of the channel decreases, its efficiency is comparable to the ICA-based technique. Thus, in the case of a highly sparse channel, the proposed technique is superior in both aspects of efficiency and complexity, while over lower sparseness, due to its comparative efficiency, it can be hired as an optimum technique fulfilling a fair tradeoff between efficiency and complexity.

Journal

ACM Transactions on Sensor Networks (TOSN) – Association for Computing Machinery

Published: Apr 18, 2022

Keywords: Underwater IoT

References

  • Sparse channel estimation for multicarrier underwater acoustic communication: From subspace methods to compressed sensing
    [,
  • Design and implementation of smart environment monitoring and analytics in real-time system framework based on internet of underwater things and big data
    [,
  • Deep learning based BER prediction model in underwater IoT networks
    [,
  • Sparse underwater acoustic channel parameter estimation using a wideband receiver array
    [,
  • Independent component analysis, a new concept?Signal Processing 36, 3 (1994), 287–314
    [,
  • An IoT based predictive connected car maintenance
    [,
  • An overview of the internet of underwater things
    [,
  • Underwater acoustic networks testbed for ecological monitoring of Qinghai lake
    [,
  • An efficient hierarchical clustering protocol for multihop internet of vehicles communication
    [,
  • Modeling architecture for collaborative virtual objects based on services
    [,
  • Economic IoT strategy: The future technology for health monitoring and diagnostic of agriculture vehicles
    [,
  • Economic data analytic AI technique on IoT edge devices for health monitoring of agriculture machines
    [,
  • Hydraulic system onboard monitoring and fault diagnostic in agricultural machine
    [,
  • Driving behavior evaluation model base on big data from internet of vehicles
    [,
  • Ocean networks Canada: From geohazards research laboratories to smart ocean systems
    [,
  • Underwater sensor networks: Applications, advances and challenges
    [,
  • Comparison of sparse recovery algorithms for channel estimation in underwater acoustic OFDM with data-driven sparsity learning
    [,
  • Emerging smart logistics and transportation using IoT and blockchain
    [,
  • Independent components analysis versus principal components analysis
    [,
  • Blind Source Separation and Its Application to Speech, Image and MIMO-OFDM Communication Systems
    [,
  • A robust and precise solution to permutation indeterminacy and complex scaling ambiguity in BSS-based blind MIMO-OFDM receiver
    [,
  • An optimum pre-filter for ICA based mulit-input multi-output OFDM system
    [,
  • An efficient ICA based approach to multiuser detection in MIMO OFDM systems
    [,
  • A PDF-matched modification to Stone’s measure of predictability for blind source separation
    [,
  • A theoretical discussion on the foundation of Stone’s blind source separation
    [,
  • Compressive Sensing in Healthcare
    [,
  • Smart green ocean underwater IoT network by ICA-based acoustic blind MIMO OFDM transceiver with analysis of acoustic channel sparsity and blind estimation efficiency in data rate and energy consumption
    [,
  • Probabilistic Stone’s blind source separation with application to channel estimation and multi-node identification in MIMO IoT green communication and multimedia systems
    [,
  • Energy efficient, resource-aware, prediction based VM provisioning approach for cloud environment
    [,
  • Comparative study on ant colony optimization (ACO) and k-means clustering approaches for jobs scheduling and energy optimization model in Internet of Things (IoT)
    [,
  • An RFID aquarium sensing system
    [,
  • Prospects and problems of wireless communication for underwater sensor networks
    [,
  • Estimation of rapidly time-varying sparse channels
    [,
  • Enhanced channel estimation and symbol detection for high speed multi-input multi-output underwater acoustic communications
    [,
  • IoT considerations, requirements, and architectures for smart buildings: Energy optimization and next-generation building management systems
    [,
  • DROPS: Dynamic radio protocol selection for energy-constrained wearable IoT healthcare
    [,
  • Taxonomies for reasoning about cyber-physical attacks in IoT-based manufacturing systems
    [,
  • Infrastructure for mobile sensor network in the singapore coastal zone
    [,
  • A multimedia cross-layer protocol for underwater acoustic sensor networks
    [,
  • Performance analysis of video transmission in vertical-UWOC link in mid-sea oil rig IoT systems
    [,
  • SIGMM: A novel machine learning algorithm for spammer identification in industrial mobile cloud computing
    [,
  • Underwater Internet of Things in smart ocean: System architecture and open issues
    [,
  • LoRaquatica: Studying range and location estimation using LoRa and IoT in aquatic sensing
    [,
  • Autonomous underwater monitoring system for detecting life on the seabed by means of computer vision cloud services
    [,
  • Configuring a trusted cloud service model for smart city exploration using hybrid intelligence
    [,
  • Selection of effective machine learning algorithm and Bot-IoT attacks traffic identification for Internet of Things in smart city
    [,
  • Beacon-based cluster framework for internet of people, things, and services (IoPTS)
    [,
  • Evaluation of a gastric radio tag insertion technique for anadromous river herring
    [,
  • Conceptual model for smart cities: Irrigation and highway lamps using IoT
    [,
  • Underwater acoustic communication channels: Propagation models and statistical characterization
    [,
  • Blind source separation using temporal predictability
    [,
  • Performance comparison of Doppler scale estimation methods for underwater acoustic OFDM
    [,
  • Dynamic magnetic induction wireless communications for autonomous-underwater-vehicle-assisted underwater IoT
    [,
  • A review on intelligent process for smart home applications based on IoT: Coherent taxonomy, motivation, open challenges, and recommendations
    [,
  • Natural gradient works efficiently in learning
    [,
  • Detecting OSDM signals in sparse channels and snapping shrimp noise
    [,