MUMBAI, India, May 29 -- Intellectual Property India has published a patent application (202641062095 A) filed by Sri Sai Harish Anumala, Vijayawada, Andhra Pradesh, on May 15, for 'edge & distributed computing: integrating edge and quantum computing for efficient data processing, and uav swarm computation offloading.'
Inventor(s) include Sri Sai Harish Anumala.
The application for the patent was published on May 29, under issue no. 22/2026.
According to the abstract released by the Intellectual Property India: "The global telecommunications, defense, logistics, emergency response, environmental monitoring, and smart infrastructure sectors face an unprecedented computational efficiency crisis driven by the fundamental inadequacy of conventional centralized cloud computing architectures, static bandwidth allocation protocols, and classical sequential data processing frameworks to meet the ultra-low latency, high-throughput, and energy-efficient computation demands of next-generation unmanned aerial vehicle swarm coordination, real-time industrial IoT sensor fusion, autonomous vehicle fleet management, distributed edge intelligence deployment, and time-critical mission-critical application environments. Defense establishments, telecommunications network operators, logistics enterprises, emergency response agencies, smart city infrastructure management authorities, and precision agriculture technology providers operating across surveillance reconnaissance, disaster relief coordination, last-mile delivery optimization, telecommunications relay infrastructure, environmental hazard monitoring, and infrastructure inspection automation domains generate massive volumes of distributed sensor telemetry streams, real-time video surveillance feeds, multi-agent coordination command signals, spectrum management intelligence datasets, and mission-critical situational awareness data records that conventional centralized cloud processing architectures are fundamentally incapable of transforming into actionable operational intelligence within the sub-millisecond to millisecond latency thresholds demanded by UAV swarm collision avoidance, autonomous navigation synchronization, and time-critical industrial control applications. [510] Existing edge computing technology platforms exhibit critical deficiencies in their capacity to dynamically offload heterogeneous computational workloads across hierarchical edge server networks while simultaneously optimizing task scheduling, resource allocation, energy consumption minimization, and quality-of-service assurance objectives, effectively coordinate distributed computation across geographically dispersed UAV swarm agents with dynamic topology reconfiguration, autonomously adapt quantum-classical hybrid computation workflows to available quantum processing unit resource availability, integrate quantum annealing-based combinatorial optimization into real-time task scheduling decision frameworks, and maintain reliable computation offloading performance continuity across intermittent wireless connectivity environments characteristic of mobile edge computing deployment contexts. [515] The integration of Artificial Intelligence capabilities including deep reinforcement learning multi-agent computation offloading optimization, federated learning distributed model training coordination, quantum-classical hybrid algorithm execution frameworks, transformer architecture network state prediction, graph neural network distributed resource allocation planning, and generative AI adaptive edge intelligence synthesis presents transformative opportunities for revolutionizing edge computing efficiency, UAV swarm computational capability, and quantum-enhanced distributed data processing performance across telecommunications infrastructure, defense operations, industrial automation, smart city management, environmental monitoring, and precision agriculture technological application domains. AI systems capable of learning optimal computation offloading policies, predicting wireless channel dynamics, coordinating distributed UAV swarm task execution, and exploiting quantum computational advantages for combinatorial optimization challenges from comprehensive operational telemetry datasets can autonomously orchestrate heterogeneous edge computing resources with energy efficiency, latency performance, and computational throughput exceeding conventional static scheduling and centralized cloud offloading methodologies. [520] The present invention describes a comprehensive AI-Integrated Edge and Distributed Computing System that integrates multi-tier edge computing resource orchestration modules, deep reinforcement learning computation offloading optimization engines, quantum-classical hybrid processing coordination frameworks, UAV swarm distributed computation management architectures, federated learning privacy-preserving model synchronization systems, and adaptive wireless channel-aware task scheduling controllers within a unified autonomous distributed computing platform. The system continuously analyzes network topology dynamics, computational workload characteristics, energy consumption profiles, wireless channel quality indicators, and UAV swarm operational state telemetry to orchestrate optimal computation offloading decisions, quantum resource allocation assignments, task partitioning configurations, and distributed intelligence coordination protocols with latency performance, energy efficiency, and computational throughput exceeding current manual configuration and conventional static allocation methodologies. [525] Validation studies conducted across multiple operational testing environments spanning telecommunications mobile edge computing infrastructure deployments, military UAV swarm coordination simulation platforms, smart city IoT sensor network testbeds, industrial automation edge server installations, and emergency response distributed computing field deployments demonstrated that the AI-Integrated Edge and Distributed Computing System achieved a 71.3 percent reduction in average task completion latency across heterogeneous workload profiles, 64.8 percent improvement in energy efficiency across UAV swarm computation offloading sessions, 58.7 percent improvement in quantum-enhanced optimization solution quality for NP-hard scheduling problems, 77.2 percent improvement in federated learning model convergence speed across distributed edge node networks, and 69.4 percent acceleration in multi-UAV swarm mission execution throughput compared to conventional centralized cloud offloading and static resource allocation methodologies. [530] The research findings confirm that the AI-Integrated Edge and Distributed Computing System constitutes a foundational technological advancement for distributed intelligent computing infrastructure, with deployment potential spanning telecommunications network operators managing 5G and beyond mobile edge computing resources, military and defense UAV swarm coordination command systems, smart city IoT infrastructure management authorities, industrial automation and Industry 4.0 manufacturing facilities, precision agriculture autonomous vehicle fleet management platforms, emergency response and disaster relief coordination agencies, environmental monitoring distributed sensor network operators, and autonomous transportation infrastructure management organizations requiring intelligent, adaptive, high-performance autonomous edge computing orchestration and quantum-enhanced distributed data processing capabilities aligned with accelerating global edge intelligence deployment demands."
Disclaimer: Curated by HT Syndication.
