MPP DBMS for Wireless Sensor Network Data Prediction
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Solution Overview
Problem
Current wireless sensor networks face challenges in managing sensor data quality, optimizing resource use, and extending operational lifetime due to environmental noise, battery limitations, and the need for self-management and adaptation in harsh conditions.
Innovation Solution
The implementation of a data-centric architecture integrating Massively Parallel Processing Database Management Systems (MPP DBMS) and data prediction models for autonomic computing, enabling context-aware adaptation, self-configuration, and self-healing within sensor networks to manage sensor data efficiently and optimize resource use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor nodes continuously transmit data to ensure high data quality, then data quality is improved, but energy consumption increases and operational lifetime decreases
Solution Approach 1:
The system performs preliminary actions by pre-calculating prediction models during periods of low activity and storing them in the database. When queries arrive, the pre-computed models can be quickly applied without requiring intensive real-time computation, thus reducing energy consumption while maintaining data quality.
Solution Approach 2:
The sensor network implements self-service through autonomous prediction models that automatically generate and update predictions without requiring continuous human intervention or complex real-time processing. The system serves itself by maintaining prediction models in the database that can autonomously answer queries about sensor data trends and patterns.
2Measurement precision
If complex data processing is performed in real-time to ensure accurate sensor readings, then measurement precision is improved, but processing time increases and productivity decreases
Solution Approach 1:
Complex data processing is performed in advance by pre-computing prediction models and storing them in the database. When queries are received, the system quickly retrieves and applies pre-computed models rather than performing complex real-time analysis, thus maintaining accuracy while dramatically improving response speed and productivity.
Solution Approach 2:
The patent introduces an intermediary layer (prediction models stored in database) between raw sensor data and query responses. This intermediary pre-processes and structures data into predictive models that can be quickly queried, acting as a buffer that maintains data quality while enabling fast response times.
3Reliability
If manual configuration and maintenance are performed to ensure system reliability, then system reliability is improved, but operational complexity and time requirements increase
Solution Approach 1:
The system implements self-service by autonomously maintaining prediction models in the database without requiring manual configuration or intervention. The system automatically manages the complexity of data processing and model maintenance, reducing operational burden while ensuring system reliability through consistent, automated processes.
Solution Approach 2:
The system incorporates feedback mechanisms where prediction models are continuously validated and updated based on actual sensor data performance. This automated feedback loop ensures system reliability by detecting and correcting issues without manual intervention, while the database structure manages complexity by organizing feedback information in structured formats.
4Measurement precision
If more sensor nodes are deployed to improve data quality and coverage, then measurement precision and reliability are improved, but network complexity and energy consumption increase
Solution Approach 1:
The patent merges data from multiple sensor nodes into unified prediction models stored in the database. Instead of managing complex individual node operations, the system combines inputs from multiple sensors and processes them through centralized prediction models, thus improving data quality through aggregation while reducing network complexity by centralizing intelligence in the database layer.
Data Source
AI summary
An architecture, methods and apparatus are provided for managing sensor data. Sensor networks comprised of a plurality of sensors are managed by obtaining measurement data and context data from the plurality of sensors; storing the obtained measurement data and context data using a Massively Parallel Processing Database Management System (MPP DBMS); and managing the sensor network from outside of the sensor network using the MPP DBMS. Context-aware adaptation of sensors is based on context regarding a state of the sensor network and context regarding a state of one or more applications. The sensor nodes are optionally clustered based on semantic similarities among sensor readings from different sensor nodes and a distance among the sensor nodes. A subset of the sensor nodes is optionally selected to be active based on a residual energy of the sensor nodes and a relevance of the sensor nodes to an application. Data prediction models are generated and employed for data sensing and analytics.