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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.

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Pattern recognition is one of the prime concepts in current technologies in both private and public sectors. The analysis and recognition of two or more patterns is a complex task due to several factors. The consideration of two or more patterns requires huge space for keeping the storage media as well as computational aspect. Vector logic gives very good strategy for recognition of patterns. This paper proposes pattern recognition in multimodal authentication system with the use of vector logic and makes the computation model hard and less error rate. Using PCA two to three biometric patterns will be fusion and then various key sizes will be extracted using LU factorization approach. The selected keys will be combined using vector logic, which introduces a memory model often called Context Dependent Memory Model (CDMM) as computational model in multimodal authentication system that gives very accurate and very effective outcome for authentication as well as verification. In the verification step, Mean Square Error (MSE) and Normalized Correlation (NC) as metrics to minimize the error rate for the proposed model and the performance analysis will be presented.