An Enhancement of Data Aggregation Algorithms to Minimize the Consumption of Energy for Underwater Wireless Sensor Networks

Abstract

In recent years, the contribution of the sensor networks is very important in all fields especially underwater wireless sensor networks are playing a major responsibility in observing environmental prediction. Vital information at the appropriate time is not only essential but also a requisite the basic requirement for security, the safety of the society and indeed of the nation. Wireless Sensors will play a major role in every day and near the upcoming trend of the world. It is an ever-present part of living place, vehicles, machinery, health care, and all real-time environment monitoring applications. It helps in predicting and making a decision for the changes in an environment. A critical part of this growth is played by inexpensive and resource-efficient solutions in software and hardware development. newlineIn underwater, the sensor nodes are controlled by the power manageable to the battery-operated sensor nodes. Sometimes, the sensor gets failed due to an energy hole or hardware problem, and then the loss of data from the surrounding is attained. To address these issues, various deployment models, data aggregation algorithm mechanisms have been proposed in this study to enhance the life span of the sensors. newlineThe sensors clustered in the form of Date Palm Tree pattern used in the Semaphore Based Data Aggregation (SBDA). Sensors are deployed with the Minkowski distance model and maintain the three states for the Sensor Node, Data Aggregator Node and Cluster Head to reduce the conflict, packet loss, delay and minimize the consumption of energy. Energy Efficient Data Aggregation (EEDA) algorithms have collected the readings on the scheduled form to minimize the utilization of energy. newlineHierarchical classification of ANOVA Analysis (HCAA) and Time Series similarity Checking (TSSC) have examined the one-year real-time oceanographic information and found similar and dissimilar measurements and analyzed the error percentage. It proved to maximize the life span of the sensor by extending the sensing time interval.