A New Wireless Charging System for Buses

By Aaryaa Padhyegurjar

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Researchers from Taiwan’s National Chiayi University have created a revolutionary UAV-based charging method for city-wide sensor networks that use public transportation. 

Bus routes in Chiayi city. (Credit: Lin, T-L et al., Electronics)

Wireless rechargeable sensor networks (WRSNs) are an important technology for the future development of smart cities. These devices use renewable energy or self-charging capabilities to power a variety of cutting-edge traffic and environmental monitoring applications. The usage of wireless charging with UAVs is an innovative way for providing electricity for these networks. This solution allows for the ongoing deployment of WRSNs to previously inaccessible sites while also lowering the maintenance requirements for continuous wireless charging networks.

Current technology, however, limit this method. Because UAVs have a limited battery life, they must return to their base stations to recharge on a regular basis. Furthermore, these devices’ battery capacity constraints limit their ability to charge sensor networks across a vast area. The car has to be driven to the point of interest after being picked and periodically recharged on public transportation.

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Bus timetables, point of interest locations, arrival times, and energy threshold information can all be used to create a continuous and dependable system for monitoring places of interest with UAVs. Other research has suggested employing drone scheduling to keep UAVs charged at all times. The bus system, UAV, and WRSN must all transmit energy efficiently in order for this system to work.

A novel bus system-assisted UAV charging system for the efficient and reliable monitoring of WRSNs was proposed in a study published in the journal Electronics. The bus provides the energy required for effective UAV operation, while flight energy consumption is kept to a minimum. This method creates a comprehensive network, which includes sensor flight segments, flight between sensors in the city-wide WRSN network, and bus route segments.

The paper contributes to the advancement of this technology in various ways. First, a method for coordinating bus schedules and UAVs has been established. Second, charging algorithms based on real-world bus data and metropolitan maps have been developed to increase UAV recharging and sensor power supply. Finally, the authors analysed three different tactics for the suggested system (naive, shortest path, and maximum power). The simplest but the least efficient charging approach is naive charging.

Overall, the authors have developed a highly efficient and dependable unmanned aerial vehicle charging technology that has the potential to be used in future smart cities by finding the system’s ideal charging method and using real-world public transportation route information.


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