BSEIFFS: Blockchain-secured edge-intelligent forest fire surveillance

This work aims to address two major issues in forest fire management: accurate real-time forecast and end-to-end secured data delivery between validated entities. To do so, we introduce BSEIFFS, a secured and intelligent forest fire prediction architecture realized through the convergence of Blockchain technology, mist computing, edge computing and machine learning paradigms. The proposed architecture renders Blockchain-backed security and Support Vector Regression based wildfire prediction at the edge by offloading the execution overheads to containerized cloud applications. Two algorithms namely Uncorrelated Data Transmission (UDT) algorithm and Blockchain based Audit Trail validation (BAT) algorithm are proposed to reduce number of repetitive packet transmissions and rendering a lightweight Blockchain based identity validation scheme at the edge respectively. The prediction model is trained using real-world meteorological data and tested on a steel manufacturing facility where controlled combustion and quenching processes closely resemble fire and rain scenarios. Apart from the Google Cloud Platform, tools such as IBM NodeRed, Siemens IBA PDA softwares are used to implement the BSEIFFS architecture. Extensive experimentation and comparative evaluation are performed with relevant baseline approaches. BSEIFFS achieves a prediction accuracy of 98.61%, ROC AUC score of 0.9668 with 0.1127 as false positive rate. The BSEIFFS SVR predictor with rbf kernel (MAE = 1.840, RMSE = 5.728) performs better than similar baseline approaches. The proposed UDT algorithm causes 82.02% reduction in packet transmissions through the network. BSEIFFS also achieves atleast 44.84% lesser energy consumption, 11.91% higher throughput and 71.91% lesser roundtrip time in comparison with baseline approaches. BSEIFFS can massively extend the benefits of the imminent B5G/6G technologies to perform fine-grained and faster computations.