Hybrid Micro-Energy Harvesting Model using WSN for Self-Sustainable Wireless Mobile Charging Application

The self-sustainable Wireless Sensor Networks (WSNs) face a major challenge in terms of energy efficiency as they have to operate without replacement of batteries. The benefits of renewable and green energy are taken into consideration for sensing and charging the battery in recent literatures using Energy Harvesting (EH) techniques. The sensors are provided with a reliable energy source through Wireless Charging (WC) techniques. Several challenges in WSN are addressed by combining these technologies. However, it is essential to consider the deployment cost in these systems. This paper presents a self-sustainable energy efficient WSN based model for Mobile Charger (MC) and Energy Harvesting Base Station (EHBS) while considering the cost of deployment. This system can also be used for low-cost microelectronic devices and low-cost Micro-Energy Harvesting (MEH) system-based applications. While considering the deployment cost, the network lifetime is maximized and an extensive comparison of simulation with various existing models is presented to emphasize the validity of the proposed model.

“Ferrites”: Synthesis, Structure, Properties and Applications

Ferrites synthesis method and characterization techniques are attracting huge attentions of researchers because of their wide scope of uses in numerous areas. The ferrites include high resistivity, saturation magnetization, permeability, coercivity and low power losses. The above-mentioned useful ferrites characteristics make them appropriate for use in different applications. These ferrites are used in biomedical field for cancer cure and MRI. Electronic applications are transformers, transducers, and inductors which are also made using ferrites and also used in making magnetic fluids, sensors, and biosensors. Ferrite is a profoundly helpful material for many electrical and electronic applications. It has applications in pretty much every domestic device like LED bulb, mobile charger, TV, microwave, fridge, PC, printer, etc. This review mainly focus on the synthesis method, characterization techniques, and implementation of FNPs. This Chapter presents various methods used for ferrites preparation with distinctive examples, their advantages as well as limitations in detail. Ferrites properties like structural, optical, electrical and magnetic with their characterization techniques and various applications in the areas of biomedical, electronics, and environment are also discussed.

Energy-Efficient Enhancement for the Prediction-Based Scheduling Algorithm for the Improvement of Network Lifetime in WSNs

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.

Coin-Based Mobile Charger using Solar Tracker

Background/Purpose: Mobile phone industry is a booming industry in the market in the present times. This industry is growing tremendously not only in urban areas but also in villages. For communication and other various purposes people use Mobile phones. Hence the usage of mobile is much more that before ten years. In urban areas there are many resources available for charging but in rural areas most of the time charging facilities are unavailable and load shedding causes a lot of problems. Sometimes the battery becomes low during conversations or even an important transaction, and an urgent charging becomes necessary. Objective: The major objective of this work is by inserting a coin in public places, one can charge the mobile phone. It will be helpful for the people who are suing mobile phones outside their homes/offices, need to use the coin based mobile phone charger to charge the mobile. To transmit and receive the IR (Infrared) signal, an IR transmitter and IR receiver will be used at the receiving end. A coin needs to be inserted between IR transmitter and receiver, which will change the polarity of the pulses in Signal Conditioning Unit (SCU) input. To activate the 230V charger, the relay will be ON and then the charger is used to charge the mobile phone. Design/Methodology/Approach: Coin-based Mobile charger using solar tracker designed to provide a completely different service to people staying in rural areas. For controlling applications, 8051 microcontroller-based circuits used for programming. Power grid is the used for charging and the energy obtained from the sun is used when grid power is not available. MATLAB is utilized for coding to find the edge of the coin comparing it to the image clicked by the camera and to avoid coin duplication Findings/Results: A simple and effective mobile charger has been implemented, which allow users to charge their phones in an emergency in public areas. This device uses very few components with a microcontroller 8051-based circuit. Usage of stepper motors allows accurate sun tracking. After reviewing the data, it was discovered that it is possible to capture good volume of sun energy when compared with fixed panel system. This method is more efficient, less costly and easier to use. Conclusion: This work uses solar power as solar energy and generates more energy for free of cost. This system also uses external grid power in the absence of solar energy which will be helpful in the rural areas since villages face load shedding most of the time. Paper Type: Research paper on the success story and contributing factors of coin based mobile charger using solar system.

RLC: A Reinforcement Learning-Based Charging Algorithm for Mobile Devices

Wireless charging has been demonstrated as a promising technology for prolonging device operational lifetimes in Wireless Rechargeable Networks ( WRNs ). To schedule a mobile charger to move along a predesigned trajectory to charge devices, most existing studies assume that the precise location information of devices is already known. Unfortunately, this assumption does not always hold in real mobile application, because the activities of the vast majority of mobile devices carried by mobile agents appear dynamic and random. To the best of our knowledge, this is the first work to study how to wirelessly charge mobile devices with non-deterministic mobility. We aim to provide effective charging service to them, subject to the energy capacity of the mobile charger. We formalize the effective charging problem as a charging reward maximization problem ( CRMP ), where the amount of reward obtained by charging a device is inversely proportional to the residual lifetime of the device. Then, we prove that CRMP is NP-hard. To derive an effective charging heuristic, an algorithm based on Reinforcement Learning ( RL ) is proposed. The evaluation results show that the RL-based charging algorithm achieves excellent charging effectiveness. We further interpret the learned heuristic to gain deep and valuable insights into the design options.