Disasters often take place in the vicinity of human livelihood. Most of the time, it is either natural (e.g., landslide, earthquake, tsunami, flood, forest fire, and lightning) or manmade (e.g., industrial explosion, leakage in an oil pipeline, leakage in gas production, and terrorist attacks). Regardless the cause of incident, disaster leads to huge destruction in terms of economic and human lives.
We classify the disaster management systems based on the application point of view as follows (i) service-oriented, (ii) natural, (iii) man-made, and (iv) post-disaster management systems:
A. Service-oriented Disaster Management:
- R3 IoT-based Early Warning System (R3 IoTEWS) to cater the environment disaster risk and effect management in an efficient way.
- The R3 IoT-based Safe Community Awareness and Alerting Network (R3 SCALE) is a similar platform with cost effective sensors, actuators, and microcontroller. The main purpose of R3 SCALE is to provide an alarm when it detects such prospective act of nature. This work proposes the R3 Data in Motion Exchange (DIME) platform that is designed to allow heterogeneous integration of devices (and services) to publish/subscribe to any other data feed.
B. VOLCANIC DISASTER MANAGEMENT
Volcanic eruptions took millions of lives in last century. Industrial approaches are indeed necessary in this regard. R3 IoT-based Volcanic digital early warning system (R3 IoTVDEWS) uses the latest IOT application such as to predict the volcanic eruptions by using IoT-enabled sensors placed inside the crater and remotely located cloud services altogether which gives system the capability to monitor the volcanic activity and predict the eruption of dead time. The sensors of the systems are designed to monitor (i) several Volatile Organic Compound (VOC) emissions, (ii) topography changes in geolocations, and (iii) vibrations in the surrounding air which are caused by the spewing rocks and ashes from the volcanoes.
Machine learning algorithms and analytics are used to discover the suspicious patterns in the volcanic activity. This is truly the first IoT-based volcano monitoring system being deployed in the world.
C. FLOOD DISASTER MANAGEMENT
Flood is one of the most common disastrous events that take place in different countries every year around the globe. IoT has been able to be applied to save the livelihood among flood affected areas in recent times. A R3IoT-based water level monitoring system (R3 IOTWLMS) is recently designed to measure the water level in a river, pond, lake, and lagoon. The developed system uses water level sensor to estimate the depth of water bodies by incorporating IoT as an essential tool where the information about a level of water is sent to a local machine through the local WiFi or Lorawan network. The received information on the local machine can be obtained by any smart phone and other digital devices.
D. FOREST FIRE DISASTER MANAGEMENT
A forest fire is one of the most ancient mishaps taking place on the earth. A R3 IoT based early warning and detection of forest fire (R3 IoT EWADFF) is recently designed for environmental monitoring infrastructure, in particular for a forest fire scenario. The project utilized `Libelium’ platforms along with several temperatures, humidity and gas sensors to automate the process of notification and visualization
on a web portal.
E. LANDSLIDE DISASTER MANAGEMENT
Landslide typically occurs after either rapid deforestation or earth quake followed by heavy rain in short duration of time. A R3 IoT based Landslide early warning System and detection (R3 IoT LSEWD) is recently designed for the landslide disaster management system. The system incorporates a tilt sensor, pressure sensor, moisture sensor, geophone, and strain gauge along with microcontroller distributed in landslide hazard zone areas. The moisture level and real-time soil tilting information are sent and received by the Lorawan/ZigBee-based transceivers to automate the process of notification and visualization
on a web portal.
Sustainable Optimization of energy consumption patterns as well as predict peaks to enable local trading of energy in a smart grid.