Experimental Evaluation of Lightning and Weather Alert Methods in Rural India using LoRa and IoT Technology with Nanosensors

Every year, ∼2,000 people are killed by lightning in India, with rural areas accounting for 94% of all lightning deaths. In rural locations, a weather and lightning alert system is crucial for alerting and raising awareness about severe weather and lightning. Only a few states in India have expensive lightning alert systems that send out alerts via SMS and mobile apps. Due to a lack of information about weather alert apps, poor network facility, low literacy, and less usage of smartphones, existing alert mechanisms is not reaching all rural populations. As a result, there is a need to build a low-cost lightning alert system in rural areas that alerts and creates awareness in advance about lightning to rural residents using a variety of alerting methods, such as announcements, sirens, WhatsApp, voice call alerts, and email, in addition to existing alert methods to overcome limitations of existing systems. In order to achieve this, we have developed an IoT- and long range (LoRa)-based weather stations, gateways, and announcement systems using ESP32, nanosensors (DHT11, BMP180, rain sensor, and LDR), lightning detector (AS3935), lightning emulator, Arduino Nano, SD card module, and speakers. This prototype is tested in real time by creating lightning radiation using an emulator shield and by monitoring environmental parameters. It sends an alert to authorized persons/village government officials/rural communities through WhatsApp, email, and voice call about abnormal weather situations with the help of cloud platforms. It also alerts and creates safety awareness to rural people in advance about lightning/critical weather condition through announcement system using loudspeakers. In the future, the proposed system will be trained using artificial intelligence to predict the lightning and other hazardous weather situations in advance and alert rural residents about critical weather conditions in early. © 2023 Ome Nerella et al.