{'fraction_non_alphanumeric': 0.02619515389652914, 'fraction_numerical': 0.0, 'mean_word_length': 5.55793991416309, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Energy-Efficient IoT-Fog-Cloud Architectural Paradigm for Real-Time Wildfire Prediction and Forecasting

Wildfires are catastrophic disasters. They pose a fatal threat not only to the forest resources but also to the entire regime of flora and fauna, gravely disturbing the bio-diversity and ecology of the region. The frequency and severity of wildfires are expected to grow, owing to global warming. Therefore, it is essential to adopt a comprehensive, multifaceted approach that enables the real-time monitoring of forest terrains and prompt responsiveness. The Internet of Things (IoT) technology has grown exponentially in recent years, with IoT sensors being deployed to monitor and collect time critical data. This research proposes an integrated IoT-Fog-Cloud energy-efficient framework for wildfire prediction and forecasting. Initially, analysis of variance and Tukey’s post hoc test-based energy conserving mechanism ensures the enhanced lifetime of resource-constrained sensors by adapting the sampling rate of wildfire influent parameters (WIPs) at fog layer. Principal component analysis (PCA) is employed for WIPs’ reduction. Wildfire vulnerability level of a forest terrain is predicted and forecasted using Naïve Bayes (NB) classifier and seasonal auto regressive integrated moving average model, respectively, at cloud layer. Burnt forest area is also predicted using support vector regression. The implementation results of the proposed framework prove its efficiency in predicting and forecasting wildfires.