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.

The condition monitoring of rotating machines for critical applications plays an important role in reducing downtime. With Industry 4.0, the role of IoT in online condition monitoring of electrical machines has gained considerable significance. The main aim of the paper is the use of IoT for online monitoring of motor parameters like current, temperature, vibration, and humidity and observing its online trending using a web server. Data can be accessed in form of graphs and widgets by visiting the web page. The advantage of this project is the real-time monitoring of the motor from any remote area and in case of any abnormality operating personnel can take necessary steps for preventing complete breakdown. The proposed work can help industry people in online monitoring of motors and in the future work can be extended for fault prediction and classification.

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.

Renewable energy sources, particularly solar photovoltaic generation, now dominate generation options. Solar generation advancements have resulted in floating solar photovoltaics, also known as FSPV systems. FSPV systems are one of the fastest growing technologies today, providing a viable replacement for ground-mounted PV systems due to their flexibility and low land-space requirement. This paper presents a systematic approach for implementing a proposed FSPV–grid integrated system in Bhilai Steel Plant’s (BSP) subsections. BSP is a steel manufacturing plant located in Bhilai, Chhattisgarh, and the FSPV system has the potential to generate sufficient energy by accessing two of its reservoirs. The system was simulated in HOMER Pro software, which provided the FSPV system power estimations, area requirements, net present cost (NPC), levelized cost of energy (LCOE), production summary, grid purchasing/selling, IRR, ROI, paybacks and pollutant emissions. A sensitivity analysis for a hike in PV prices globally due to a shortage in poly silicone in international markets during the fiscal year 2021–2022 was undertaken for the proposed FSPV–grid system. Here, the authors considered hikes in the PV price of 1%, 9%and 18% respectively, since the maximum percentage increase in PV prices globally is 18%. The authors also compared the proposed FSPV–grid system to the existing grid-only system for two sections of the BSP and the results obtained showed that the NPC and LCOE would be much lower in the case of the FSPV–grid system than the grid-only system. However, with changes in the percentage hike in PV prices, the NPC and LCOE were found to increase due to changes in the proportion of FSPV–grid systems in production. The pollutant emissions were the minimum in the case of the FSPV–grid system, whereas they were the highest in the case of the existing grid-only system. Furthermore, the payback analysis indicated that the minimum ROI for the above-defined construction would be fully covered in 15.81 years with the nominal 1% pricing for FSPV–grid generation. Therefore, the overall results suggest that the FSPV–grid system has the potential to be a perfect alternative solar energy source that can meet the current electrical energy requirements of the steel manufacturing industry with nominal pricing better than the existing grid-only system, as well as addressing economic constraints and conferring environmental benefits.

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.

GPS Receiver Position Estimation and DOP Analysis Using a New Form of the Observation Matrix Approximations: A location sensor is a feature that communicates with a Global Positioning System (GPS) receiver to learn about the status of the current location. This work presents the GPS receiver position estimation and Dilution of Precision (DOP) analysis using a new approximate form of observation matrix which can be used in place of the classic observation matrix that was derived from the Taylors series. It has been realized that, the approximate observation matrix is numerically stable and provides greater precision in calculating DOP values and estimating the position of a GPS receiver. The experimental results show that the proposed observation matrix provides better precision in DOP analysis and GPS receiver position estimation with a fast convergence rate and improved algorithm stability. Therefore, it can be concluded that the proposed new observation matrix plays a significant role to estimate accurately the location of the GPS receiver position and to enhance all parameters of the DOP.