Abstract
Abstract Wireless sensor networks (WSNs) play a critical role in applications such as wildlife monitoring, disaster recovery, and precision agriculture, where continuous coverage and longevity are paramount amidst dynamic environmental challenges. To address these demands, the cellular adaptive energy forecasting and coverage optimization (CAEFCO) framework integrates localized neuro-symbolic energy forecasting (LNS-EF), a novel concept that combines symbolic reasoning with neural network learning directly on sensor nodes. LNS-EF enables nodes to not only predict energy depletion based on past consumption patterns and environmental factors but also incorporate rule-based contextual reasoning for enhanced decision-making. Alongside this, CAEFCO employs an anomaly detection module that identifies disruptions, such as sensor damage or environmental interference, allowing real-time task redistribution. This dual approach ensures seamless task reallocation while extending network lifetime. CAEFCO’s proactive methodology demonstrates a 97% reduction in data loss and an 85% improvement in network longevity, offering a breakthrough in the resilience and sustainability of WSNs in mission-critical and harsh environments.
Accepted Version
Published Version
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