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The world has experienced a digital transformation of epic proportions in the past few years. The prospects of the Internet of Things for improving efficiency, reducing cost, achieving data-oriented predictive maintenance, and enhancing quality have now been proven well by various studies. Since everything is going online, a lot of data is available at the disposal of organizations. Useful insights can be gleaned from these large data sets, which comprise real-time data from various devices with intelligent sensors, and seamless integration. Here is precisely where Smart Devices come into the picture. IoT and smart devices are critical enablers for higher customer satisfaction, energy efficiency, and personalization. IoT-enabled smart devices like intelligent wearables (fit-bit, iWatch), voice assistants (Google Home, Alexa), smart refrigerators, smart washing machines, etc., are becoming widespread, commercially available. People using these develop routines and become dependent on them. This entire ecosystem consisting of IoT, smart devices, technologies, and the internet is highly consumer-centric and eventually leads to satisfied and happy customers. Quantitative research involving the collection and analysis of data will be carried out. Primary data will be collected by floating a questionnaire. Data from Indians (age group 18-60) will be collected using a questionnaire. Using SPSS, the descriptive analysis would be carried out on the data collected to find the mean, standard deviation, correlation, etc. of the data. Technology Acceptance Model (TAM) is incorporated in the research to comprehend how receptive or averse are people when it comes to IoT-enabled smart devices. The model used comprises of a factor, namely Social Context Factor which includes the Social Influence construct, and technology factors which include the following constructs, Trust, Perceived Usefulness (PU), and Perceived Ease of Use (PEOU). TAM will help one understand a user's Behavioural Intention to use IoT-enabled smart devices. The paper would help one derive the impact of technical factors and social context factors on the acceptance of IoT-enabled smart devices. The paper tries to explore the various driving factors when it comes to an individual's willingness to use IoT-enabled smart devices from different prerogatives i.e. technology, and social context. The research paper successfully extends TAM to such acceptance. [ABSTRACT FROM AUTHOR]

The world has experienced a digital transformation of epic proportions in the past few years. The prospects of the Internet of Things for improving efficiency, reducing cost, achieving data-oriented predictive maintenance, and enhancing quality have now been proven well by various studies. Since everything is going online, a lot of data is available at the disposal of organizations. Useful insights can be gleaned from these large data sets, which comprise real-time data from various devices with intelligent sensors, and seamless integration. Here is precisely where Smart Devices come into the picture. IoT and smart devices are critical enablers for higher customer satisfaction, energy efficiency, and personalization. IoT-enabled smart devices like intelligent wearables (fit-bit, iWatch), voice assistants (Google Home, Alexa), smart refrigerators, smart washing machines, etc., are becoming widespread, commercially available. People using these develop routines and become dependent on them. This entire ecosystem consisting of IoT, smart devices, technologies, and the internet is highly consumer-centric and eventually leads to satisfied and happy customers. Quantitative research involving the collection and analysis of data will be carried out. Primary data will be collected by floating a questionnaire. Data from Indians (age group 18-60) will be collected using a questionnaire. Using SPSS, the descriptive analysis would be carried out on the data collected to find the mean, standard deviation, correlation, etc. of the data. Technology Acceptance Model (TAM) is incorporated in the research to comprehend how receptive or averse are people when it comes to IoT-enabled smart devices. The model used comprises of a factor, namely Social Context Factor which includes the Social Influence construct, and technology factors which include the following constructs, Trust, Perceived Usefulness (PU), and Perceived Ease of Use (PEOU). TAM will help one understand a user's Behavioural Intention to use IoT-enabled smart devices. The paper would help one derive the impact of technical factors and social context factors on the acceptance of IoT-enabled smart devices. The paper tries to explore the various driving factors when it comes to an individual's willingness to use IoT-enabled smart devices from different prerogatives i.e. technology, and social context. The research paper successfully extends TAM to such acceptance. [ABSTRACT FROM AUTHOR]

In agriculture, intelligent sensor approaches have gained a lot of attention in the past several years. Planning many operations and missions with limited resources and little human involvement is used in agriculture. New agricultural approaches are currently highly popular among producers like Machine learning.As a result of the technique,Machine learning helps keep the irrigation system appropriate. It optimizes water use, supplies the field with needed water and fertility, increases output, decreases handling, and decreases crop disease.Plants are grown in a growth chamber under completely controlled circumstances by misting them with a nutritional solution instead of dirt.Backpropagation networks(BPN) train BPN to discover the appropriate correlating proportion between the characteristics by providing reference crop growth properties and their capacity to supply nutrients from their reserves in both instances and external crop production applications. For all-weather circumstances, the system is updated immediately, and no water dispersion for crops occurs. By analyzing the cloud and monitoring the temperature, this set-up can rapidly feel the weather and be used as a water conservation device. Forecasting may be improved by gathering real-time weather, soil and air quality, and crop maturity data in order to make better judgments in the future. A data collecting system that generates and gathers information autonomously in isolated or remote areas is described in this study.A line-following robot regularly gathers soil moisture data from the sensor module and air temperature data from the sensor module. It uploads the data to a computer through a cloud connection for further analysis. [ABSTRACT FROM AUTHOR]