Emotional Feeling Evaluation Model in Underwater Environment Based on Wearable Sensor

Abstract

Underwater sensor network technologies, as well as devices, are developing rapidly, and underwater IoT devices have been widely used in energy surveys, environmental indicator detection, military surveillance, and disaster event monitoring. The transmission of massive amounts of underwater data to the cloud for processing and analysis has become the dominant processing paradigm, and cloud computing has become a dominant computing paradigm. The preparation strategy of elastomer-coated hydrogel optical fibers for stable optical sensing proposed in this work opens up a new method and approach for developing low-cost and highly sensitive water flow sensors while analyzing the design of wearable smart devices to assess underwater environmental emotion perception evaluation schemes. In this paper, we propose a sensory data acquisition technique for event coverage detection of underwater environmental emotions, observing that an event may correspond to deviations from the normal sensory range of sensory data from multiple adjacent sensor nodes. Distributed edge computing is introduced to assume part of the cloud computing pressure, and an edge prediction-based data acquisition and sensing scheme for underwater sensor networks is proposed to realize the conversion of the acoustic communication transmission part of underwater data into data prediction transmission, thus reducing the energy consumption caused by acoustic communication. The model established in this paper effectively reduces sensor energy consumption while ensuring accurate data transmission and can respond to the underlying demand promptly, which is significantly better than the already existing schemes.