Sensor Management System for Community Security Analytics
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Solution Overview
Problem
Current technologies fail to effectively monitor and manage data from multiple sensors for enhanced community security, particularly in detecting threats like radiation, as most data collected is used for marketing rather than security purposes.
Innovation Solution
A system and method for monitoring and managing a plurality of sensors, including configuration of parameters for sensors and sensor analytics processes, storage of metadata with analyzed data, and load balancing across multiple computing systems to ensure proper operation and efficient data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensor data is collected and analyzed for security purposes, then community security detection capability is improved, but system complexity and infrastructure requirements increase
Solution Approach 1:
The system segments sensor management and analytics into modular components distributed across multiple computing systems. Each computing system handles specific sensor analytics processes independently, allowing the security detection capability to be built incrementally without requiring a monolithic complex infrastructure.
Solution Approach 2:
The computing systems are designed to perform multiple functions - they can run different sensor analytics processes, handle various sensor types, and provide both security detection and potential marketing applications. This multi-functionality reduces overall system complexity by using standardized platforms rather than specialized dedicated systems.
2Reliability
If sensor analytics processes are distributed across multiple computing systems, then system reliability and scalability are improved, but coordination and management complexity increase
Solution Approach 1:
The system implements monitoring mechanisms that track the health and performance of sensor analytics processes across distributed computing systems. This feedback enables automatic detection of failures and triggers reconfiguration or redistribution of processes, maintaining reliability without requiring complex manual coordination.
Solution Approach 2:
The distributed computing systems are designed to autonomously manage their own sensor analytics processes, including self-monitoring, self-diagnosis, and automatic recovery from failures. This self-service capability reduces the need for external coordination complexity while maintaining high availability.
3Measurement precision
If comprehensive sensor metadata is stored with analytics data, then data analysis accuracy and security detection quality are improved, but data storage and processing requirements increase
Solution Approach 1:
The system stores comprehensive metadata and analytics data with the precision needed for security detection at the local level (with each computing system and its associated sensors), rather than centralizing all data. This allows high measurement precision where needed while distributing storage requirements.
Solution Approach 2:
Data storage is segmented across multiple computing systems based on sensor type, location, and analytics process. Each computing system stores metadata and data relevant to its specific sensors and processes, reducing the storage burden on any single system while maintaining comprehensive data availability for security detection.
Data Source
AI summary
Systems, apparatuses, and methods described herein are configured for monitoring and managing a plurality of sensors. The plurality of sensors may be fixed, mobile, or a combination thereof. In some embodiments, the monitoring and management of the sensors is facilitated via a graphical user interface.