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<p>Acoustic communication has been traditional for Underwater Wireless Sensor Networks (UWSNs), particularly in ocean monitoring and resource exploration applications. It runs at the low end of speed with high-latency sensitivity to temperature, salinity, and pressure variations-conditions usually found in distortions of signals hence rendering reliability rather low. Electromagnetic (EM) waves can for example be an alternative means of fast and stable short-range underwater communications. There are higher data rates, a lower communication delay, and less influence by underwater noise or multipath interference than the acoustic case therefore suitable for today’s UWSN applications. This study develops a channel modeling framework using actual channel data from the NCEI oceanographic database for years 1955–2012 driven by a true world. Variations in temperature, salinity, and depth at 101 depth points between 5 and 5500&#xa0;m are applied by mathematical modeling which analyses the behavior of EM waves in underwater environments. This channel characterization is then put into a scheme that autonomously optimizes the movement of courier nodes for collecting information with minimized energy consumption together with enhanced reliability of the whole scheme of underwater communications. Simulation results of existing scheme cooperative and path-aware reliable routing (EPRR) compared with proposed scheme proved that shallow waters to be the best medium for underwater communication using electromagnetic waves with most nodes alive till 4000 rounds, delivering more than 154,000 packets maintaining lowest transmission loss (3.38&#xa0;dB) with minimum energy used at 0.6&#xa0;kJ. Networks in shallow water last 48% longer than networks in medium and deep environments thus proving it as the most efficient setting for EM-based UWSNs along with MMSE varies from 0 to 1.8 on average.</p>

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Predicting courier node trajectory using channel characterization to enhance lifetime in IoT-based underwater wireless sensor networks

  • 期刊论文

  • 发表日期：

    2026年4月26日

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  • Muhammad Ahmed Pervez,
  • Muhammad Tahir,
  • Zeshan Iqbal,
  • Moeenuddin Tariq,
  • Ghassen Ben Brahim,
  • Muhammad Attique Khan

摘要

Acoustic communication has been traditional for Underwater Wireless Sensor Networks (UWSNs), particularly in ocean monitoring and resource exploration applications. It runs at the low end of speed with high-latency sensitivity to temperature, salinity, and pressure variations-conditions usually found in distortions of signals hence rendering reliability rather low. Electromagnetic (EM) waves can for example be an alternative means of fast and stable short-range underwater communications. There are higher data rates, a lower communication delay, and less influence by underwater noise or multipath interference than the acoustic case therefore suitable for today’s UWSN applications. This study develops a channel modeling framework using actual channel data from the NCEI oceanographic database for years 1955–2012 driven by a true world. Variations in temperature, salinity, and depth at 101 depth points between 5 and 5500 m are applied by mathematical modeling which analyses the behavior of EM waves in underwater environments. This channel characterization is then put into a scheme that autonomously optimizes the movement of courier nodes for collecting information with minimized energy consumption together with enhanced reliability of the whole scheme of underwater communications. Simulation results of existing scheme cooperative and path-aware reliable routing (EPRR) compared with proposed scheme proved that shallow waters to be the best medium for underwater communication using electromagnetic waves with most nodes alive till 4000 rounds, delivering more than 154,000 packets maintaining lowest transmission loss (3.38 dB) with minimum energy used at 0.6 kJ. Networks in shallow water last 48% longer than networks in medium and deep environments thus proving it as the most efficient setting for EM-based UWSNs along with MMSE varies from 0 to 1.8 on average.