Abstract:
Introduction: The amalgamated bond between human-to-machine communications (H2M) with the Internet of Things (IoTs) technology has played a major role in easing the burden on conventional living methods in most developed communities. Wireless sensor networks (WSNs) integrated with the internet of things (IoTs) are paving the way to society 5.0 of a shared community relationship with the environment and economic growth. Objective: To proposed a neural language processing-deep learning model (scoring model) for data security and information encoding as part of the fight to ensure security for communications and data sharing. Problem: Recently, machine-machine (M2M) has dominated human-machine (H2M) coexistence. The global economy has survived and flourished in industry 4.0 without compromising on our community standards, environment, and economic growth. Little share approach in industry 4.0 is now manifesting in global climate change impacts. Method and material: The study uses a community mindset transformation appraisal (CMTA) concept and applied deep learning models knowledge and natural language processing data classification. Wireless sensor networks (WSNs) nods are used as edges of redistributed data and sensor nods. A formulated data flow is applied to deep learning models' summation of data flow (DLMSDF) concept. Formulated data is applied to the input, hidden layers, and output layers to demonstrate a possible method to monitor data flow. Results. Based on information for deep learning models summation of data flow (DLMSDF), a grade of 30 bits was recorded as input data equal to output and a score of 4.58 and 4.0909 for Class B and Class A respectively as behavioral influence of internet of things and wireless sensor networks on data security for community mindset. Conclusion: We concluded that the Integration of IoTs with WSNs can secure a perfect transformative community mindset free of security threats, corruption, racism, poverty, dominant attitudes, and lack of information
