Distributed Camera System Using Named Queues for Robust Sensor Management
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Solution Overview
Problem
Current systems cannot effectively combine the robustness of Internet-scale cloud computing with the rich sensor data and location information from handheld devices in home or office environments, lacking a unified solution that integrates both for efficient management and security.
Innovation Solution
A room cloud system that applies Internet-scale robustness at the hardware level, utilizing bus interconnects and failure-over systems, public key encryption, fault tolerance, and re-configurability, enabling reliable communication and computation of real-time sensor data through a network of sensor nodes and mobile devices, allowing for distributed processing and redundancy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If distributed camera systems use traditional centralized processing, then system complexity is reduced, but system robustness and reliability deteriorate due to single points of failure
Solution Approach 1:
The patent divides the centralized processing system into distributed camera nodes, each with independent processing capabilities. Each camera node segments the overall system functionality, allowing independent operation and failure isolation. This segmentation enables the system to maintain robustness through distribution while managing complexity at the node level through standardized interfaces.
Solution Approach 2:
The system implements dynamic task allocation and data routing where camera nodes can adaptively join or leave the network based on operational needs. The distributed architecture allows dynamic reconfiguration of processing paths and data flows, enabling the system to maintain reliability through flexible resource allocation while managing complexity through adaptive rather than static design.
2Reliability
If distributed camera systems implement failure-over systems with redundancy, then system reliability improves, but device complexity and hardware requirements worsen
Solution Approach 1:
The patent implements virtual copying of system state and data through distributed buffers and message queues rather than physical hardware redundancy. Each camera node maintains virtual copies of relevant data in memory buffers, allowing failover through data replication in software rather than requiring duplicate hardware components. This approach improves reliability through redundancy while minimizing hardware complexity.
Solution Approach 2:
The system replaces mechanical/hardware failover mechanisms with software-based solutions including distributed message queues, virtual memory buffers, and software-controlled data routing. This substitution of software for hardware in the failover mechanism improves reliability through flexible error handling while reducing hardware complexity and cost.
3Productivity
If real-time sensor data is processed centrally, then data correlation is simplified, but processing speed and system responsiveness worsen due to bottlenecks
Solution Approach 1:
The patent segments the data processing function across multiple camera nodes rather than centralizing it. Each node processes and buffers its local sensor data independently, then contributes to correlated outputs through distributed message queues. This segmentation enables parallel processing that improves speed while managing data management complexity through standardized data exchange protocols.
Solution Approach 2:
The system introduces distributed message queues as intermediary components between camera nodes and processing targets. These queues act as buffers and mediators that decouple data production from data consumption, allowing nodes to process data at optimal speeds without creating bottlenecks. The intermediary queues manage data flow complexity while enabling high-speed parallel processing.
4Reliability
If distributed camera nodes communicate via traditional networking, then system scalability improves, but communication reliability and data consistency worsen due to network uncertainties
Solution Approach 1:
The patent implements beforehand cushioning through distributed message queues with built-in buffering and acknowledgment mechanisms. Data is cushioned in queues during transmission, providing protection against network losses and inconsistencies. This prior cushioning ensures communication reliability while maintaining scalability through standardized queue interfaces that work across different network configurations.
Solution Approach 2:
The system implements feedback mechanisms where camera nodes receive acknowledgments and status information from message queue consumers and other nodes. This feedback loop ensures data consistency by allowing nodes to verify successful data transmission and retry failed communications. The feedback mechanism maintains reliability while supporting scalability through standardized feedback protocols.
Data Source
AI summary
A system includes sensor nodes each including camera and a processor, the nodes interconnected via a wide area network. A master controller establishes named queues each associated with a proximate physical object in an area including one or more of the sensor nodes and detectable by the sensor node camera. Each sensor node identifies a viewed physical object in a feed from the sensor node camera, and associates the viewed physical object with one of the named queues.