Natural Disaster Detection using IoT and LoRa

Description

Natural Disaster Detection using IoT and LoRa

Abstract

Every year, natural and human-induced disasters result in infrastructural damages, monetary costs, distress, injuries, and deaths. Unfortunately, climate change is strengthening the destructive power of natural disasters. In this context, Internet-of-Things (IoT)-based disaster detection and response systems have been proposed to cope with disasters and emergencies by improving disaster detection. Natural Disaster Detection using IoT and LoRa

Accordingly, IoT devices are used to collect data and help to identify different types of natural and manmade disasters. Here we design a general system with a number of sensors to detect abnormal situations. Disasters such as landslides, fire accidents, explosions, and earthquakes. The major difference between this system and existing systems is the decentralized and personalized alerting system. Here we get the location of the disaster-detected area and using this location identify all the people in that area based on their phone location and send them alerts regarding the disaster before the situation gets dire. This can be used as an early warning system in the most unexpected situations. Natural Disaster Detection using IoT and LoRa


Natural Disaster Detection using IoT and LoRa

Introduction

 Natural disasters are unexpected events that concern worldwide nations. Every year, extreme weather conditions, hurricanes, earthquakes, droughts, ?oods, and heatwaves cause considerable damages, monetary costs, mass evacuations, dis-tresses, injuries, and deaths. For instance, the tsunami that hit Japan in March 2011, destroyed more than 120,000 buildings, and occasioned an estimated? financial damage of about $199billion dollars, and caused 15,894 deaths. In Canada, the Fort McMurray wild? forced over 88,000 people to leave their town, caused an estimated C$3.6 billion in insurance costs, destroyed about 10% of all structures in the town, and provoked chaos with people leaving their homes with whatever they could take. Natural Disaster Detection using IoT and LoRa


Natural Disaster Detection using IoT and LoRa

Existing systems

  • Early warning systems are only for predictable natural disasters
  • Current detection systems are centralized and only send alerts to a command center
  • Does this cause a time delay
  • No monitoring from the remote location because existing systems uses internet or mobile network for communication

Natural Disaster Detection using IoT and LoRa

Proposed system

  • Personalized early disaster warning system
  • Based on geolocation
  • Arduino based implementation
  • Sensors are used for detecting abnormalities
  • Lora protocol is used for data transfer
  • Node mcu is used at the receiver end to identify the users at the location.
  • The receiver section sends an alert to each individual
  • MQTT is used for sending an alert

Block diagram

Transmitter

Indus1

Receiver

Indus2


Block diagram description

  • Divided into two sections transmitter and receiver?
  • The transmitter is used for detection and has all the sensors connected to it
  • Transmitter uses Arduino Uno and Lora is used for communication
  • Lora is connected using a serial UART interface
  • Depending on sensor type they use ADC or GPIO interface
  • At the receiver, the buzzer is used for a local alert
  • MQTT protocol is used for sending personalized alerts to each person

Hardware tools

  • Arduino UNO
  • Nodemcu
  • Lora transceiver
  • Vibration sensor
  • Moisture sensor
  • Piezoelectric?
  • Fire sensor
  • Buzzer

Software tools

  • Arduino IDE

Reference

  1. World Health Organization,?Disasters & emergencies definitions training package,? Geneva, Switzerland, 2002.
  2. United Nations Department of Economic and Social Affairs,?Population aging 2006,? New York, NY, USA. 2006. [Online]. Available: http://www.un.org/esa/population/publications/ageing/ageing2006.htm. Accessed on: Feb. 1, 2018.
  3. J. W. Brown and M. Lisa,?Older adults and disasters: How to be prepared and assist others.? Washington, DC, USA, American Psychological Association, 2018.
  4. P. Pandey and R. Litoriya, ?An activity vigilance system for the elderly based on fuzzy probability transformations,? J. Intell. Fuzzy Syst., vol. 36, no. 3, pp. 2481? 2494, 2019.
  5. P. Pandey and R. Litoriya, ?Legal/regulatory issues for MMBD in IoT BT,? Multimedia Big Data Computing for IoT applications: Concepts, Paradigms and Solutions, S. Tanwar, S. Tyagi, and N. Kumar, Eds., Singapore: Springer, 2020, pp. 367? 388.
  6. CRED,?Center for Research on Epidemiology of Disasters,? Brussels, Belgium, 2018.
  7. NIDM,?Disaster risk profile,? New Delhi, India, 2014.
  8. B. Wisner,?Vulnerability as a concept, model, metric, and tool,? in Oxford Research Encyclopedia of Natural Hazard Science, vol. 1, London, U.K.: Oxford Univ. Press, 2016.
  9. B. Wisner, J. Gaillard, and I. Kelman, ?Framing disaster,? in Handbook of Hazards and Disaster Risk Reduction. Evanston, IL, USA: Routledge, 2011.
  10. J. C. Gaillard,?Natural hazards and disasters,? in International Encyclopedia of Geography: People, the Earth, Environment, and Technology. Hoboken, NJ, USA: Wiley, 2017, pp. 1? 15.

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