In order to solve the problems of poor data processing ability of underwater hardware equipment and low accuracy of classification algorithms in the existing marine target recognition and detection methods based on sensors and transducers, by combining perception technology, underwater Internet of Things technology, and artificial intelligence, multiple devices could communicate with each other to achieve automatic and intelligent high-precision marine target recognition. Compared with existing methods, not only the accuracy rate is improved but also the hardware requirements are lower, and it is easier to deploy in engineering.

We proposed a novel approach to evolve LSTM networks utilizing intelligent optimization algorithms and address time-series classification problems in EdgeIoT. Meanwhile, a new optimizer called cultural society and civilization (CSC) algorithm is proposed to reduce the probability of stagnated in the local optima and increase the convergence speed. The suggested method could relieve the problem that the traditional data mining and pattern extraction methods cannot guarantee high accuracy and are hard to deploy on terminal devices. The proposed CSC algorithm and CSC-optimized LSTM model is examined on benchmark problems and demonstrates remarkable superiority over traditional methods and can be applied to support EdgeIoT for learning and processing.

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