All living things, including plants, animals, and humans, need water in order to live. Even though the world has a lot of water, only about 1% of it is fresh and usable. As the population has grown and water has been used more, fresh water has become a more valuable and important resource. Agriculture uses more than 70% of the world’s fresh water. People who work in agriculture are not only the world’s biggest water users by volume, but also the least valuable, least efficient, and most subsidized water users. Technology like smart irrigation systems must be used to make agricultural irrigation more efficient so that more water is used. A system like this can be very precise, but it needs information about the soil and the weather in the area where it is going to be used. This paper analyzes a smart irrigation system that is based on the Internet of Things and a cloud-based architecture. This system is designed to measure soil moisture and humidity and then process this data in the cloud using a variety of machine learning techniques. Farmers are given the correct information about water content rules. Farming can use less water if they use smart irrigation.

In recent years, the applicability of these DG units to electrical Microgrids (MGs) has grown rapidly, enabling them to contribute a large percentage of the installed generating capacity. However, the fluctuating and intermittent nature of renewable generation can adversely affect electric grid stability and operations. Conventionally, to overcome these problems, batteries are employed. Nevertheless, the quick charging and discharging cycle reduces the battery life span, resulting in an economic burden and environmental damage. To resolve these problems, short-term Distributed Energy Storage (DES) systems based on advanced technologies, such as Superconducting Magnetic Energy Storage (SMES) and Supercapacitor Energy Storage (SCES), are emerging as potential alternatives. A supplementary regulator which includes storing energy as well as a flexible AC transmission system is designed to boost the minute signal reliability. The precise optimization of multiple energy device characteristics is needed for efficient performance. The artificial intelligence methods and Particle Swarm Optimization (PSO) are used to obtain the optimum parameters in the micro-hydro system. This research examines the many Energy Storage Systems (ESSs) in power systems, particularly microgrids, and demonstrates their critical role in improving the quality of electrical systems. As a result, the ESSs were divided into several technologies based on the energy storage form and the most important technological features. In this review paper, the most common classifications are introduced/presented, summarized, and compared according to their characteristics.

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In wireless sensor networks, due to the restricted battery capabilities of sensor nodes, the energy issue plays a critical role in network efficiency and lifespan. In our work, an upgraded long short-term memory is executed by the base station to frequently predict the forecast positions of the node with the help of load-adaptive beaconing scheduling algorithm. In recent years, new technologies for wireless charging have offered a feasible technique in overcoming the WSN energy dilemma. Researchers are deploying rechargeable wireless sensor networks that introduce high-capacity smartphone chargers for sensor nodes for charging. Nearly all R-WSN research has focused on charging static nodes with relativistic routes or mobile nodes. In this work, it is analysed how to charge nondeterministic mobility nodes in this work. In this scenario, a new mechanism is recommended, called predicting-based scheduling algorithm, to implement charging activities. In the suggested technique, it directs them to pursue the mobile charger and recharge the sensor, which is unique for the present work. The mobile charger will then choose a suitable node, utilizing a scheduling algorithm, as the charging object. A tracking algorithm based on the Kalman filter is preferred during energy transfer to determine the distance needed for charging between the destination node & mobile charger. Here, the collecting & processing of data are performed through the big data collection in WSNs. The R-WSN charging operations of nondeterministic mobility nodes will be accomplished using the proposed charging strategy.

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