MUMBAI, India, Aug. 12 -- Intellectual Property India has published a patent application (202641093548 A) filed by Dr. M. Kanmani on August 01, 2026, for Multi-Tiered Cognitive Tracking Platform For Real-Time Monitoring Of Meta-Memory Self-Planning Self-Monitoring Self-Evaluation And Self-Regulation Dimensions During Scientific Pedagogy.
Inventor includes Dr. M. Kanmani.
The application for the patent was published on August 07, 2026, under issue no. 32/2026.
Abstract: This disclosure introduces an integrated, multi-tiered cognitive framework engineered to maximize network longevity, resource management, and sensor-fusion fidelity within a wireless sensor network tailored for early-stage wildfire detection. The underlying platform coordinates data collection from thermo-optical sensor arrays and acoustic anomaly detection hardware, managing data dissemination across a multi-hop LoRa communication mesh. Operationally, the system functions via a closed- loop, self-referential architecture distributed across distinct cognitive dimensions. A persistent meta-memory repository acts as a localized ledger, cataloging baseline micro-climate fluctuations, background acoustic profiles, and network performance signatures to ground the system in historical context. Capitalizing on these baselines, an anticipatory self-planning engine dynamically models upcoming environmental risks to orchestrate duty cycles, routing paths, and power allocations ahead of unfolding field hazards. As these schedules execute, a continuous self-monitoring tier logs real-time telemetry, mapping live infrared variations, frequency anomalies, and signal attenuation metrics. These runtime streams are continuously scrutinized by a self-evaluation layer, which calculates operational error metrics, quantifies power budget deviations, and validates the statistical integrity of active fire alerts. Finally, an automated self-regulation mechanism ingests these evaluative scores to execute immediate corrective counter-measures, adjusting hardware amplification, updating mesh topologies, and shifting computational workloads to safeguard early warning accuracy under volatile environmental conditions.
Disclaimer: Curated by HT Syndication.
