The problem of hazard detection and the robotic exploration of the hazardous environment is the need of the of the hour due to the continuous increase of the hazardous gases owing to the industry proliferation and modernization of the infrastructure. It includes radiological materials and toxic gases with long term harmful effects. The definition of a hazardous environment and extracting the parameters for the same is itself a complicated task. The chapter proposes the alarming solution to warn about the level of hazardous effects for a particular environment area. The need of the hour is to build complete systems that can autosense the hazardous environment even in low visibility environment and raise an alarm. The combination of IoT and machine learning can be best used for getting the real-time data and using the real-time data for analyzing the accurate current hazardous level as well as prediction of future hazards by reading the parameters for detection and also selecting the useful parameters from them.
TopIntroduction
Internet of Things (IoT) can be explained as a network in which the connection of various types of devices which have electronics, sensors, and software, controllers embedded in them and connected for exchanging data. The connectivity in IoT is not limited to desktops, laptops, smart phones and tablets but covers any kind of daily usage objects like home appliances. The digital world can be directly converted to physical world for our convenience and controlling the world around us much more efficiently and effortlessly.
As studied by Gartner there is a tremendous increase of the use of IoT devices from 31% in one year, from approximately 8.4 billion in the year 2017 to the estimation of approximately 30 billion devices by 2020 (Gartner). Technically a fully fledged unit of IoT system consists of majorly four components consisting of sensors/devices with an interface for user and connection between the various devices and sensors for processing and transferring data. The technology roadmap for IoT is as shown below in Figure 1.
Figure 1.
Technology roadmap of IoT
As the road map indicates IoT has come a long way. It started with demand for expatiating logistics with these of RFID tags for facilitating routing, inventory management, tracking of goods, monitoring of logistics and tasks which can be done automatically when data is read from RFID tag by the readers. After that came many applications like Surveillance, security, healthcare, food safety, document management and disaster management. Application areas for IoT started increasing though in the chapter major areas are covered. Use of IoT became prevalent after 2010 and further the future beyond this is ubiquitous computing where locating people and everyday objects is going to become easy. It can help in many aspects like tracking lost objects, stolen objects, tracking missing people but still intruding the privacy is a big issue which needs to be resolved. The future of IoT is physical web world connections with the ability to control and monitor distant objects. Use of software agents and advance sensor fusion is going to make humans rock with almost every work done remotely, ubiquitously and automatically with IoT. The sensors produce large amount of data which is collected through cloud platform and this data can be used in disaster management. Due to the high temporal frequency of the sensor data it is considered as the Big Data. There are many challenges associated with it like capturing and storing the sensor data and providing means for searching, utilizing and analyzing this data. So, in this cyber-physical era, the Big Data act as a bridge between the IoT and the internet. The different Big Data technologies like Hadoop, NoSql, and MapReduce etc can be used for IoT generated data. The Big Data generated provides an ample amount of opportunities for disaster Management. This Big Data consist of Geo-spatial sensor data, data generated by social networking sites etc. The main activities in Disaster Management are Preparedness, Response and Recovery and these phases are inter-related with each other as the success of response and recovery phases depends on the data collection during preparedness and prevention phases. For instance, the data generated is further processed and analyzed for various actions like raising an alarm or alerting the users or calibrating the system automatically without any manual intervention. This chapter proposes an IoT based system that can be used for disaster management in industries. The chapter is organized in the following sections:
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IoT and Big Data
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IoT in Disaster Management
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Big Data in Disaster Management
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Security of Big Data in Disaster Management
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Existing Scenarios
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Proposed Solution
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Conclusion