Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Reinforcement Learning-Based MAC Protocol for Underwater Multimedia Sensor Networks

Reinforcement Learning-Based MAC Protocol for Underwater Multimedia Sensor Networks High propagation delay, high error probability, floating node mobility, and low data rates are the key challenges for Underwater Wireless Multimedia Sensor Networks (UMWSNs). In this article, we propose RL-MAC, a Reinforcement Learning (RL)–based Medium Access Control (MAC) protocol for multimedia sensing in an Underwater Acoustic Network (UAN) environment. The proposed scheme uses Transmission Opportunity (TXOP) for relay nodes in a multi-hop network for improved efficiency concerning the mobility of the relays and sensor nodes. The access point (AP) and relay nodes calculate traffic demands from the initial contention of the sensor nodes. Our solution uses Q-learning to enhance the contention mechanism at the initial phase of multimedia transmission. Based on the traffic demands, RL-MAC allocates TXOP duration for the uplink multimedia reception. Further, the Structural Similarity Index Measure (SSIM) and compression techniques are used for calculating the image quality at the receiver end and reducing the image at the destination, respectively. We implement a prototype of the proposed scheme over an off-the-shelf, low-cost hardware setup. Moreover, extensive simulation over NS-3 shows a significant packet delivery ratio and throughput compared with the existing state-of-the-art. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Sensor Networks (TOSN) Association for Computing Machinery

Reinforcement Learning-Based MAC Protocol for Underwater Multimedia Sensor Networks

Download PDF
25 pages

Loading next page...
 
/lp/association-for-computing-machinery/reinforcement-learning-based-mac-protocol-for-underwater-multimedia-F7UT2SjVXH
Publisher
Association for Computing Machinery
Copyright
Copyright © 2022 Association for Computing Machinery.
ISSN
1550-4859
eISSN
1550-4867
DOI
10.1145/3484201
Publisher site
See Article on Publisher Site

Abstract

High propagation delay, high error probability, floating node mobility, and low data rates are the key challenges for Underwater Wireless Multimedia Sensor Networks (UMWSNs). In this article, we propose RL-MAC, a Reinforcement Learning (RL)–based Medium Access Control (MAC) protocol for multimedia sensing in an Underwater Acoustic Network (UAN) environment. The proposed scheme uses Transmission Opportunity (TXOP) for relay nodes in a multi-hop network for improved efficiency concerning the mobility of the relays and sensor nodes. The access point (AP) and relay nodes calculate traffic demands from the initial contention of the sensor nodes. Our solution uses Q-learning to enhance the contention mechanism at the initial phase of multimedia transmission. Based on the traffic demands, RL-MAC allocates TXOP duration for the uplink multimedia reception. Further, the Structural Similarity Index Measure (SSIM) and compression techniques are used for calculating the image quality at the receiver end and reducing the image at the destination, respectively. We implement a prototype of the proposed scheme over an off-the-shelf, low-cost hardware setup. Moreover, extensive simulation over NS-3 shows a significant packet delivery ratio and throughput compared with the existing state-of-the-art.

Journal

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

Published: Sep 19, 2022

Keywords: Underwater Wireless Multimedia Sensor Networks (UMWSNs)

References

  • Quality aware image transmission over underwater multimedia sensor networks
    [,
  • Performance comparison of sender-based and receiver-based scheduling MAC protocols for underwater sensor networks
    [,
  • Efficient depth-based scheduling MAC protocols for underwater sensor networks
    [,
  • A comparative performance evaluation of distributed collision-free MAC protocols for underwater sensor networks
    [,
  • Exploiting energy efficient routing protocols for void hole alleviation in IoT enabled underwater WSN
    [,
  • Comparison of contention-based MAC protocols for underwater sensor networks
    [,
  • Collision avoidance energy efficient multi-channel MAC protocol for underwater acoustic sensor networks
    [,
  • Deep learning based BER prediction model in underwater IoT networks
    [,
  • Cooperative multihop communication for underwater acoustic networks
    [,
  • Medium access control under space-time coupling in underwater acoustic networks
    [,
  • DCO-MAC: A hybrid MAC protocol for data collection in underwater acoustic sensor networks
    [,
  • Reinforcement-learning based dynamic transmission range adjustment in medium access control for underwater wireless sensor networks
    [,
  • A hybrid reservation-based MAC protocol for underwater acoustic sensor networks
    [,
  • A multi-hop energy-efficient sleeping MAC protocol based on TDMA scheduling for wireless mesh sensor networks
    [,
  • UnRest: Underwater reliable acoustic communication for multimedia streaming
    [,
  • MAC protocol for underwater acoustic networks based on deep reinforcement learning
    [,
  • The high frequency underwater acoustic channel
    [,
  • Optical-acoustic hybrid network toward real-time video streaming for mobile underwater sensors
    [,
  • A review and classification of energy efficient MAC protocols for underwater wireless sensor network
    [,
  • RCAR: A reinforcement-learning-based routing protocol for congestion-avoided underwater acoustic sensor networks
    [,
  • Underwater network management system in Internet of Underwater Things: Open challenges, benefits, and feasible solution
    [,
  • Localization in terrestrial and underwater sensor-based M2M communication networks: Architecture, classification and challenges
    [,
  • A survey on IEEE 802
    [,
  • UCMAC: A cooperative MAC protocol for underwater wireless sensor networks
    [,
  • Structural similarity index SSIMplified: Is there really a simpler concept at the heart of image quality measurement?
    [,
  • An improved R-MAC based MAC protocol for underwater acoustic networks
    [,
  • Aqua-sim next generation: A NS-3 based simulator for underwater sensor networks
    [,
  • An ultrasonic sensor based low-power acoustic modem for underwater communication in underwater wireless sensor networks
    [,
  • Performance of the ALOHA-Q MAC protocol for underwater acoustic networks
    [,
  • Reinforcement learning based MAC protocol (UW-ALOHA-Q) for underwater acoustic sensor networks
    [,
  • Reinforcement learning based MAC protocol (UW-ALOHA-QM) for mobile underwater acoustic sensor networks
    [,
  • ahoi: Inexpensive, low-power communication and localization for underwater sensor networks and \( \mu \) AUVs
    [,
  • A synchronous duty-cycled reservation based MAC protocol for underwater wireless sensor networks
    [,
  • On the relationship between capacity and distance in an underwater acoustic communication channel
    [,
  • Analytic description of the low-frequency attenuation coefficient
    [,
  • A robust energy efficient MAC protocol design for Underwater sensor network
    [,
  • Design and implementation of state-based MAC protocol for UANs in real testbed
    [,
  • Image quality assessment: From error visibility to structural similarity
    [,
  • Reinforcement-learning-based relay mobility and power allocation for underwater sensor networks against jamming
    [,
  • Deep reinforcement learning based MAC protocol for underwater acoustic networks
    [,
  • Deep reinforcement learning based MAC protocol for underwater acoustic networks
    [,
  • A load-based hybrid MAC protocol for underwater wireless sensor networks
    [,
  • AUV-aided energy-efficient data collection in underwater acoustic sensor networks
    [,