The need for instantaneous processing for Internet of Things (IoT) has led to the notion of fog computing where computation is performed at the proximity of the data source. Though fog computing reduces the latency and bandwidth bottlenecks, the scarcity of fog nodes hampers its efficiency. Also, due to the heterogeneity and stochastic behavior of IoT, traditional resource allocation technique does not suffice the timesensitiveness of the applications. Therefore, adopting Artificial Intelligence (AI) based Reinforcement Learning approach that has the ability to self-learn and adapt to the dynamic environment is sought. The purpose of the work is to propose an Auto Centric Threshold (ACT) enabled Monte Carlo FogRA system that maximizes the utilization of Fog's limited resources with minimum termination time for time-critical IoT requests. FogRA is devised as a Reinforcement Learning (RL) problem, that obtains optimal solutions through continuous interaction with the uncertain environment. Experimental results show that the optimal value achieved by the proposed system is increased by 41% more than the baseline adaptive RA model. The efficiency of FogRA is evaluated under different performance metrics.
Agriculture and global warming are correlated with each other, particularly, it may affect nutrient cycles, microbial activities, and physiological activities of the crops. Agricultural development plays a crucial role in the growth of the economy of developing countries. The agriculture sector is a major source of employment in most of the developing countries. Over the year, there were changes and productivity loss due to the abiotic stresses and imbalance of nutrients of the plants. A continuous increase in temperature may affect the yields of crops up to 17%. Each plant has different characteristics in growth and some plants are susceptible to high temperature, some are quite the opposite. A Brassica Juncea L. belongs to a mustard family Brassicaceae or Cruciferae that are susceptible to high temperature. So, in this work, an attempt has been made for Brassica Juncea L. to grow and yield under temperature stress by controlling the temperature with the use of the Internet of Things (IoT). The experiment has been conducted where Brassica Juncea neither production nor consumption. IoT sensors are used to monitor the temperature and humidity in two different scenarios. This paper analyses the factors that affect the growth of Brassica Juncea and provide a solution to increase productivity.
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