Mobile Code for Distributed Data Fusion in Sensor Networks
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Solution Overview
Problem
Networked sensing systems face challenges in anticipating future sensing applications and sensor types, making it difficult to effectively utilize advanced sensing, processing, and communication capabilities.
Innovation Solution
The implementation of mobile code for distributed data fusion in networked sensing systems, where sensors store measurement models and generate likelihood functions, which are then aggregated by a data-fusing mechanism to compute phenomenon states, allowing for dynamic reconfiguration and adaptation to new sensors and applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the system pre-loads all possible measurement models for future sensing applications, then the system can handle future sensing needs, but the device complexity and memory requirements increase significantly
Solution Approach 1:
The system pre-loads only a subset of measurement models that are most likely to be needed based on historical data about sensing applications. This preliminary action prepares the system for common future scenarios without requiring all possible models to be loaded in advance, thus reducing device complexity while maintaining adaptability for future sensing needs.
Solution Approach 2:
The system employs a universal data-fusing mechanism that can process likelihood functions from various sensor types and measurement models. This multi-functional approach allows the same core infrastructure to handle diverse sensing applications without requiring separate specialized processing paths for each sensor type, reducing overall system complexity.
2Measurement precision
If the system stores comprehensive measurement models for all sensor types, then measurement precision improves, but the use of energy and memory increases
Solution Approach 1:
Each sensor node stores only the measurement models locally relevant to its specific sensor type and sensing capabilities, rather than storing all possible models. This local quality approach ensures that each node has the precision needed for its specific function while avoiding the energy and memory overhead of storing comprehensive model sets across the entire network.
Solution Approach 2:
The system divides the measurement model storage responsibility across multiple sensor nodes, with each node storing a segmented subset of models appropriate to its capabilities. This segmentation reduces the storage burden on individual nodes and optimizes energy usage by avoiding redundant storage of identical models across all nodes.
3Measurement precision
If the system processes all sensor data centrally, then computation accuracy improves, but the communication overhead and system scalability worsen
Solution Approach 1:
The system extracts and processes only the essential likelihood function components at each sensor node before transmission to the data-fusing mechanism. By taking out only the necessary processed information rather than transmitting all raw sensor data, the system maintains computation accuracy while significantly reducing communication bandwidth consumption.
Solution Approach 2:
The data processing function is segmented and distributed across multiple sensor nodes rather than centralized in one location. Each node performs local processing to generate likelihood functions, which are then aggregated by the data-fusing mechanism. This segmentation reduces communication overhead by eliminating the need to transmit all raw data to a central processor while maintaining the benefits of distributed computation for scalability.
Data Source
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AI summary
A sensing system includes a set of sensors and a data-fusing mechanism coupled to at least one of these sensors. In the set of sensors, at least one sensor is configured to store one or more measurement models for one or more phenomenon states. Furthermore, at least one sensor in the set of sensors is configured to sample a measurement value and generate a likelihood function based on the sampled measurement and the measurement models. The data-fusing mechanism coupled to a respective sensor in the set of sensors is configured to collect one or more likelihood functions generated by the one or more sensors and use the collected likelihood functions to compute an aggregate probability of a phenomenon state.