Efficient data interpretation and artificial intelligence enabled IoT based smart sensing system

Underwater wireless communications (UWC), based on acoustic waves, radio frequency waves, and optical waves, are currently deployed using underwater communications networks. Wireless sensor communications are among the most common UWC technologies because they offer connectivity over long distances. However, the UWC complex problems include restricted bandwidth, multitrack loss, limited battery power, and latency in propagation. Hence in this paper, Artificial Intelligence based Effective Data Interpretation Approach (AI-EDIA) has been proposed to improve the underwater wireless sensor network communication and less computational Time in IoT platform. The proposed AI-EIDA utilizes the discrete cosine transform (DCT) with frequency modulation multiplexing (FMM) for underwater acoustic communication. Underwater acoustic channels are categorized as double Time and frequency distribution channels. Therefore, the reverse DCT structure provides the orthogonal characteristic of the traditional FMM with the additional advantages of reduced execution and improved speed when the actual calculations are needed. Thus the experimental results show that AI-EDIA decreases energy usage and less delay rate to 0.45 s.

Automated summary

In this paper, an Artificial Intelligence based Effective Data Interpretation Approach (AI-EDIA) is proposed to improve underwater wireless sensor network communication and reduce computational time in IoT platform. The AI-EDIA utilizes discrete cosine transform (DCT) with frequency modulation multiplexing (FMM) for underwater acoustic communication. Experimental results show that AI-EDIA decreases energy usage and delay rate to 0.45 s.