| Summary: | It is becoming clear that the maritime industry is expected to see considerable growth over the coming years, and the Internet of Underwater Things (IoUT) could have an essential role to play in its technological development. In this regard, because batteries are the main power source in underwater environments, there is a clear need to minimize the consumption of energy. In addition, underwater links that make use of acoustic soundwaves can cause relatively long propagation delays. In our proposed scheme, we focus on the initial connection procedure between sensor nodes and underwater base stations to tackle these environmental problems, in which the former estimates the signal strength of the latter. From local information measured during the initial network entry phase, underwater sensor nodes determine locational priority of candidate targets, but do not scan or measure the signal strength of other neighbouring underwater base stations as a means of keeping the power consumption to an absolute minimum. This can be considered an appropriate scenario for use in severely battery-limited environments such as IoUT. Based on analysis using machine learning, we obtain meaningful clues regarding the procedure for handover prediction without channel measurement. By removing the overhead from the channel measurement, the power consumption of the underwater things can be minimized. Two different methods of deciding on handover priority are suggested and analysed mathematically. The performance of each method is evaluated through intensive system-level simulations and compared to that of a more conventional scheme.
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