The necessity to address the growing pace of industrialization and resource constraints has underscored the importance of developing innovative Information and Communication Technology solutions. The prevailing “smart industry” paradigm is distinguished by its widespread cyber infrastructure for monitoring and controlling critical industrial assets. In case sudden failure of any equipment shot down the industry and apart from financial loss, it take ample amount of time to recover. To effectively manage the multitude of continuous services while maintaining their quality of service requirements, it is crucial to concurrently utilize sensor-based real-time assessment for predicting employed equipment status and lifespan become a challenging. This research explores the feasibility of enhancing the lifespan of sensors at the fog layer by implementing intelligent sleep and wake-up duty cycles. We propose a clustered-based duty-cycling approach that optimizes service continuity with a minimal number of active sensor nodes using a genetic algorithm. The abovementioned framework simulated in iFogSim simulator and obtained results demonstrate the reduced the energy consumption by approximately 16% to 20% over the energy efficient aware and around 38% to 42% over the K-mean clustering approach with 90% duty cycle.