Even in muddy, dark underwater habitats, killer whales have learned to recognise a variety of different marine life. Recent research has shown that killer whales can locate and identify different species of marine life even in waters with poor visibility. This is due to the fact that killer whales send out sound waves to other aquatic life and listen for their echoes. Right now, the standard is what we are focusing on in order to recognise the Internet of Things-based issue. We all coexist underwater, and there is a core criterion for producing sound waves and detecting echoes that is used to investigate different living groups. Thus, it is essential to play a central function and a reference role among numerous sensors for scenario detection in order to raise the amount of condition information by utilising a broad range of sensors in an ideal IoT system. In order to establish such standards, this study suggests looking at additional sensor data streams based on one data stream among several other sensor data streams that are streaming into a network. With this, it is able to deduce the correlation between distinct sensor data and to see how numerous sensor types carry out sensing tasks as one circumstance develops, which may significantly improve situational awareness. In this study, different sensors were used to choose one of the many sensor data streams. A different sensor data stream was then searched around the reference sensor data stream to choose the data section required for analysis, and the data stream was used to demonstrate better situation recognition.