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Enhanced interpolation with semi-supervised algorithm and greedy forwarding technique for forest fire prediction over wireless sensor and actuator networks


Automatically increases the danger of starting fires due to global warming, and the number of forest fires is growing. Natural disasters are a regular occurrence in which both living and nonliving things in the environment are affected. Humans, on the other hand, can anticipate disaster and take significant measures to avoid it. There are a variety of technologies that can predict abnormalities or changes in exposed open areas. As autonomous sensors, Wireless sensors and Actuator Networks are used to monitor physical and environmental conditions. Though WSANs applications are being implemented with various location prediction techniques, there are still issues predicting more accurate data. The work mainly aims at identifying the abnormalities in certain areas, given location and distance information to wireless sensor networks. The proposed system includes the phases such as network construction, cluster head (CH) node selection, forest fire prediction, and data transmission. In the network construction, sensor nodes and actuator nodes are associated with sending and receiving the packets.The energy model and mobility model are built to provide efficient packet transmission over WSAN. Then the improved firefly algorithm (IFFA) is designed to select the best CH node depending on better energy utilization, delay, and lifetime between sensor nodes. Enhanced nearest neighbor interpolation with semi-supervised algorithm (ENNISSA) ENNISSA is proposed for a better forest fire prediction model using nearest neighbor interpolation (NNI) method and support vector machine (SVM) & K-means clustering (KMC) algorithm. To predict the oddity, the temperature, positions of heat sources, and pressure around that location are considered for retrieving optimized data using interpolation techniques.The semi-supervised classifier [high active (HA), medium active (MA), and low active (LA)] is presented to resolve this issue by separating the forest area into various zones. The significance of the work is to predict more accurate information of anomalies in uncovered regions. As a result, the suggested ENNISSA method is superior to existing techniques for energy consumption, throughput, end-to-end delay, network lifespan.

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Ananthi, J., Sengottaiyan, N. & Anbukaruppusamy, S. Enhanced interpolation with semi-supervised algorithm and greedy forwarding technique for forest fire prediction over wireless sensor and actuator networks. Wireless Netw (2022).

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  • Enhanced interpolation technique
  • Wireless sensor and actuator networks (WSAN)
  • Forest fire prediction
  • CH node selection
  • Data transmission