An improved underwater wireless sensor network communication using Internet of Things and signal to noise ratio analysis

• Published in: Transactions on emerging telecommunications technologies Vol. 33; no. 9
• Main Authors: Nellore, Kapileswar, Polasi, Phani Kumar
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 01.09.2022
• ISSN: 2161-3915; 2161-3915
• DOI: 10.1002/ett.4560

Underwater acoustic (UWA) channels are widely regarded as one of the most challenging communication mediums. Low frequencies are best for acoustic propagation, and the bandwidth available for communication is extremely small. The worst channel situation often limits the efficiency of UWA communication systems due to the difficulty and time‐varying nature of the UWA channel. To solve the existing limitations related with underwater signal transmission and communication, an improved underwater communication system incorporated with Internet of Things (IoT) is proposed in this work. The proposed system utilizes the orthogonal signal division multiplexing modulation technique. Here the signal transmission process is achieved with the help of an IoT network system. An improved AdaBoost channel estimation algorithm is used to obtain the channel information. This work utilizes an improved stochastic gradient descent optimization method for selecting the suitable mode of signal transmission based on the estimated channel. Further, an adaptive recursive least square channel equalization algorithm is used for channel compensation. The simulation results are obtained with the help of the MATLAB platform. The performance is analyzed in terms of parameters such as signal to noise ratio, bit error rate, and mean square error. A comparison of the proposed method is also made with the existing methods. The evaluation results show that the proposed method performs better than the existing methods. ‐The proposed system utilizes the Orthogonal Signal Division Multiplexing (OSDM) modulation technique ‐An improved Adaboost channel estimation algorithm is used to obtain the channel information. ‐The system's performance is analyzed in terms of parameters such as SNR, MSE and BER. The SNR, BER and MSE results are taken for a different number of channels.