Enhanced Energy Allocating Time Slots Mechanism (Eeatsm) in Smart Water Management for Iot and Wsn

Abstract

The utilisation of Internet of Things (IoT) and Wireless Sensor Network (WSN) technologies in the implementation of intelligent water management systems has become an essential approach in tackling the growing need for sustainable water management. One of the primary obstacles in the creation of such systems pertains to the need to optimise energy efficiency, given the restricted power supply available in austere and remote settings. The Enhanced Energy Allocating Time Slots Mechanism (EEATSM) has been proposed as a potential solution to tackle the challenge of optimising energy consumption in IoT and WSN systems.

The present study puts forth a proposition for the application of EEATSM in the context of intelligent water management systems, utilising the Internet of Things (IoT) and Wireless Sensor Network (WSN) technologies. The methodology employed in this study involves the implementation of a dynamic time slot allocation mechanism aimed at enhancing the efficiency of energy utilisation within the system. A systematic methodology for the implementation of EEATSM is presented, comprising of pertinent equations, models, and tables. The study showcases the outcomes of our experiments that exhibit the efficacy of EEATSM in curtailing energy consumption while simultaneously guaranteeing dependable data transmission in smart water management systems.

The proposed implementation of Energy-Efficient Adaptive Time Synchronisation Method (EEATSM) has the potential to enhance the development of sustainable and efficient water management practises. This can facilitate the extensive deployment of Internet of Things (IoT) and Wireless Sensor Network (WSN) systems in environments that are remote and have limited resources. The present study has the potential to yield societal benefits through the enhancement of safe drinking water accessibility, reduction of operational expenses, and mitigation of environmental repercussions associated with water management practises. In general, this manuscript can offer perspectives on the advancement of energy-efficient strategies for intelligent water management systems utilising Internet of Things (IoT) and Wireless Sensor Network (WSN) technologies.