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Summary Minimum Exposure Problem (MEP) has become one of the most important issues in WSN as it affects the coverage quality measurement to a greater extent. Recently, MEP issues have been dealt by Physarum Optimization Algorithm (POA). Nevertheless, this method ends with the less scope in precision faults. Along with these issues, the necessity for computation time and memory requirements gets increases statistically. The major aim of the work is to propose a new optimization algorithm to solve the MEP issue. Thus, Hybrid Genetic Particle Swarm Optimization (H‐GPSO) is formulated to give a desirable solution for MEP issue. In the proposed work, energy usage of the sensor node for MEP identification is measured using Hidden Markov Model (HMM) with the intention of prolonging the lifetime of the sensor node. After accomplishing the energy efficiency, MEP is developed and converted to an optimization issue. H‐GPSO is presented to resolve the optimization problem; hence, it gives a desirable solution to MEP issue. Therefore, the pretended answer proves the proposed H‐GPSO related MEP model is desirable for detecting the minimal exposure problem with high energy ratio. The results of the proposed and existing methods are measured in terms of Energy, Throughput, Delay, and Exposure via NS2.

Fault occurrence and machine downtime in work area is one of the major concerns in many industries which lead to severe economic losses and causalities. The main causes behind these problems is nothing but in-avoidance of regular checking and periodical inspection of working environment. Here is one of the similar cases, where failures in compressor system lead to several losses in industrial aspect due to its enormous application. So monitoring and diagnosis of faults in compressor systemic proposed in this study to avoid regular breakdown and idle time of machineries in industrial and domestic applications. Out of several faults in compressor, five major and common faults were taken in this study and vibration parameters for each condition is measured using accelerometer sensor. Further signals were extracted and classified through machine learning approach for the efficient diagnosis and detection of faults in compressor system.

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