MUMBAI, India, Sept. 22 -- Intellectual Property India has published a patent application (202641108996 A) filed by Madhankumar C; Ms Narmatha S - Knowledge Institute Of Technology; Dr. Manikandan S - Rathinam Global Deemed To Be University; Dr. N. Alamelu Sathyabama - Srm Institute Of Science And Technology; Mr P. T. Prithivirajan - Jai Shriram Engineering College; Ms Priyanka K - Kings Engineering College; and Dr. Amitava Biswas - Vivekananda College on September 11, 2026, for Edge-Ai Enabled Multi-Sensor Surveillance Network For Real-Time Critical Infrastructure And Border Security.
Inventors include Ms Narmatha S - Knowledge Institute Of Technology; Dr. Manikandan S - Rathinam Global Deemed To Be University; Dr. N. Alamelu Sathyabama - Srm Institute Of Science And Technology; Mr P. T. Prithivirajan - Jai Shriram Engineering College; Ms Priyanka K - Kings Engineering College; and Dr. Amitava Biswas - Vivekananda College.
The application for the patent was published on September 18, 2026, under issue no. 38/2026.
Abstract: Edge-AI Enabled Multi-Sensor Surveillance Network for Real-Time Critical Infrastructure and Border Security ABSTRACT Edge-AI Enabled Multi-Sensor Surveillance Network for Real-Time Critical Infrastructure and Border Security The present invention relates to an Edge Artificial Intelligence (Edge-AI) enabled multi sensor surveillance network for real-time monitoring, anomaly detection, situational awareness, and security management of critical infrastructure and authorized border-security environments. The proposed system integrates distributed Internet of Things (IoT) sensor nodes, electro-optical and thermal imaging devices, radar and motion sensors, acoustic sensors, vibration sensors, environmental sensors, edge-computing units, secure wireless communication, multi-sensor fusion, artificial intelligence analytics, geospatial intelligence, explainable artificial intelligence, and a centralized or distributed monitoring interface within a unified surveillance architecture. The system is configured to acquire heterogeneous real time information from geographically distributed surveillance locations and perform localized processing at edge nodes to reduce communication latency, network bandwidth requirements, and dependence on continuous centralized processing. The multi-sensor layer acquires visual, thermal, motion, acoustic, vibration, environmental, structural, and other authorized sensing information associated with critical infrastructure assets and monitored geographical regions. The edge-AI processing module performs data filtering, noise reduction, temporal synchronization, feature extraction, sensor-health assessment, object or event detection, preliminary classification, and anomaly identification close to the point of data generation. The processed information is subsequently combined through a multi-sensor fusion engine to correlate observations received from different sensing modalities and improve the reliability and contextual understanding of detected events. The AI analytics module is configured to perform real-time object and event detection, movement analysis, abnormal-pattern identification, infrastructure-condition monitoring, environmental-event recognition, and risk classification based on fused sensor information. The system may generate a security or infrastructure-status score based on detected conditions, sensor confidence, geographical location, historical patterns, environmental conditions, and predefined operational thresholds. A geospatial processing module associates detected events with geographical coordinates and monitored zones to provide location-aware situational information. The surveillance network further incorporates an adaptive communication mechanism capable of selecting appropriate communication paths according to network availability, signal quality, congestion, latency, reliability, and operational priority. Edge nodes may temporarily store and forward surveillance information during intermittent connectivity, thereby supporting continuity of monitoring. Distributed processing and redundant communication paths enable the system to remain operational when selected network components experience degradation or temporary failure. An explainable artificial intelligence module generates human-readable explanations associated with detected events and analytical outputs by identifying contributing sensor observations, detected patterns, environmental conditions, relevant infrastructure information, confidence indicators, and analytical factors. A secure monitoring interface provides authorized personnel with real-time dashboards, geospatial visualization, sensor status, infrastructure health information, event alerts, risk indicators, historical trends, and evidence-linked analytical information. The security layer incorporates device authentication, user authorization, encrypted communication, access control, audit logging, data-integrity mechanisms, and secure system configuration. Accordingly, the invention provides a scalable, low-latency, resilient, privacy-aware, and intelligent surveillance architecture capable of supporting continuous monitoring of critical infrastructure and authorized security zones, improving early anomaly identification, reducing unnecessary transmission of raw sensor data, enhancing multi-sensor situational awareness, and providing explainable decision-support information to authorized security and infrastructure-management personnel.
Disclaimer: Curated by HT Syndication.
