In recent years, the information system has laid a profound foundation in agriculture with greenhouse development, leading to accelerated growth. The green infrastructure thus built is easily accessible remotely using the intelligent system of Internet of Things (IoT). In this proposed work, an IoT-based environment is designed, developed, and implemented with sensors which are connected to the laptop/computer or a mobile phone with Internet. Further to save electricity, a separate control unit is built which provides the devices an energy efficient way of functioning. Thus, information regarding growth of the plants, moisture content in the soil, energy consumed by each smart appliances in the farm, etc., is collected using data acquisition. The data thus gathered is then segregated depending on the applications and sent to the Firebase cloud. To monitor the environmental parameters within the greenhouse, we have used a cloud-based data collection mechanism. Interfacing the dashboard with the cloud platform, it is possible to analyze the power consumed by the system using the data present. When a discontinuity occurs with data missing for about an hour, the missing data is filled with the help of previous data automatically. The maximum temperature within the greenhouse is set as 28°C, and the soil moisture content threshold is set between 50% and 80%. An artificial environment is thus created to improve the crop yield per square meter on continuous monitoring of climatic parameters resulting in an optimal environment.
To ensure self-sustainable and long-term requirement of Internet of Things (IoT) operation, energy harvesting (EH) is a promising technique. In this work, battery prediction problems and joint access control are studied in a IoT cell system with a base station (BS) and EH user equipment (UE) with limited uplink access channels. Every UE present holds a limited capacity rechargeable battery. In order to address this issue, we take into consideration the uplink wireless system with “N” EH user equipment (UE) along with the base station. To build the UE uplink access control, the first step involves the application of hybrid Q network (HQN) with long short-term memory (LSTM) to tackle the access control issue. This technique maximizes the uplink transmission sum rate. The LSTM DQN network designed has two layers with battery prediction and joint access control solution. The first layer is developed to predict battery levels, while the next layer generates the access control information based on channel information along with the predicted values. This network is trained to decrease the discounted prediction loss and increase long-term discounted sum rate, at the same time by training the two layers jointly. In recent years, IoT sensors have played a major role in collecting and monitoring data gathered from real-world applications such as environmental monitoring, transportation, urban security, smart energy management, agriculture, and health care. Several of these appliances function with batteries. Hence, the primary task would be to predict the batter’s lifespan and provide a timeframe to replace or recharge the battery. The battery life used in an IoT network is predicted with the help of a hybrid random forest-principal component analysis (PCA) regression algorithm with machine learning.
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