PAFF: predictive analytics on forest fire using compressed sensing based localized Ad Hoc wireless sensor networks

Raj Vikram, Ditipriya Sinha, Debashis De, Ayan Kumar Das

    Research output: Contribution to journalArticlepeer-review

    5 Citations (Scopus)

    Abstract

    Early detection of a forest fire can save our flora and fauna. Ad Hoc Wireless Sensor Networks (WSN) plays an important role in detecting forest fire. This article proposes a model for early detection of forest fire through predictive analytics. In this approach, the forest area is divided into different zones. Status of a zone, i.e., High Active (HA), Medium Active (MA), and Low Active (LA), is predicted by applying the semi-supervised classification technique. Each zone has static sensors, mobile sensors, and an Initiator node. Initiator nodes of LA and MA zone transfer their mobile nodes (MN) to the nearer HA zone for the quick prediction of forest fire by using the Random trajectory generation (RTG) technique. This technique generates the intermediate points between LA/MA to HA zone to create the movement path of MN. Compressed sensing based Gradient descent (GD) localization technique is used to track the movement of MN by the anchor nodes. This technique reduces the energy consumption of MN that causes an increase in network lifetime. The analysis of the localization error of MN during its traveling towards the HA zone increases the accuracy of its path detection. Thus the increase of sensor nodes in the HA zone results in transferring a huge amount of data from HA zone to base station for quick prediction of a forest fire.

    Original languageEnglish
    Pages (from-to)1647-1665
    Number of pages19
    JournalJournal of Ambient Intelligence and Humanized Computing
    Volume12
    Issue number2
    Early online date27 Jun 2020
    DOIs
    Publication statusPublished - Feb 2021

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