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Adaptive modulation received significant attention for underwater acoustic (UA) communication systems with the aim of increasing the system efficiency. It is challenging to attain a high data rate in UA communication, as UA channels vary fast, along with the environmental factors. For a time-varying UA channel, a self-adaptive system is an attractive option, which can choose the best method according to the channel condition to guarantee the continuous connectivity and high performance constantly. A real-time orthogonal frequency-division multiplexing (OFDM)-based adaptive UA communication system is presented in this paper, employing the National Instruments (NI) LabVIEW software and NI CompactDAQ device. In this paper, the received SNR is considered as a performance metric to select the transmission parameters, which are sent back to the transmitter for data transmission. In this research, a UA OFDM communication system is developed, employing adaptive modulation schemes for a nonstationary UA environment which allows to select subcarriers, modulation size, and allocate power adaptively to enhance the reliability of communication, guarantee continuous connectivity, and boost data rate. The recent UA communication experiments carried out in the Canning River, Western Australia, verify the performance of the proposed adaptive UA OFDM system, and the experimental results confirm the superiority of the proposed adaptive scheme. [ABSTRACT FROM AUTHOR]

Recently, the Wormhole Attack (WA) affects the Wireless Sensor Networks (WSN). So far, there are '2' solutions to trounce WA, that is, utilizing specialized hardware or capturing a specific pattern extra overhead over the network. Some prevailing solutions for detectingWAneed special hardware or firmly synchronized clocks or longer processing time in addition to that some solutions can't even locate the WormHole (WH) and also have low security.This paper proposed an efficient WH detection as well as Optimal Path (OP) selection for safe Data Transmission (DT) in WSN. The proposed workencompasses a '2' phase: i) training, and ii) testing. Here, the WSN values are considered and chosen secure node utilizing the Fit Factor (FF). After selecting the secure nodes, the routes are discovered aimed at the optimal assortment of the optimal routes. Next, the gathered data is tested with a trained system in which feature reduction and classification are performed. Lastly, the MCSC securely transfers the non-attack data. The proposed method's outcomes are examined and weighed against the other prevailing techniques and the outcomesexhibited the proposed method's efficiency for attack detection along withdata security. [ABSTRACT FROM AUTHOR]