Sensor Node Agent Transfer for Energy-Constrained Networks
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Solution Overview
Problem
Existing systems for agent-based processing in embedded real-time environments face challenges in balancing real-time responsiveness with the need for infrequent sensor readings or network requests due to energy or bandwidth constraints, leading to unresponsiveness in time-critical situations.
Innovation Solution
A system and method that allow sensor nodes to operate in processing and non-processing modes, enabling data processing to be transferred between nodes, using cached data when real-time data is unavailable, and optimizing energy/network usage by considering factors like sensor density and energy costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If frequent sensor readings or network requests are made to ensure real-time responsiveness, then agent/application responsiveness is improved, but energy consumption and bandwidth usage increase
Solution Approach 1:
The system performs preliminary actions by caching sensor data at multiple nodes before it is needed. When a node enters sleep mode, its data is pre-cached at neighboring nodes. This allows the system to respond to queries using cached data rather than requiring the sleeping node to wake up and provide real-time readings, thus maintaining responsiveness while reducing energy consumption.
Solution Approach 2:
The invention introduces intermediary nodes that act as mediators between sleeping sensor nodes and the system needing data. When a node is in sleep mode, neighboring nodes serve as intermediaries by providing cached data from the sleeping node. This intermediary mechanism maintains system responsiveness without requiring the original sensor node to be active, thereby reducing energy consumption.
2Use of energy by moving object
If sensor nodes operate in sleep mode to reduce energy consumption, then energy usage is reduced, but real-time data availability deteriorates
Solution Approach 1:
The system merges the data storage functionality across multiple nodes. When node A sleeps, its sensor data is combined with the storage capacity of neighboring nodes B and C. This distributed caching approach ensures that data from sleeping nodes remains available to the system, preventing information loss while allowing nodes to conserve energy through sleep mode operation.
Solution Approach 2:
Before sensor nodes enter sleep mode, they perform preliminary actions by transferring their data to neighboring nodes. This pre-positioning of data ensures that even when nodes are dormant, the information remains accessible in the network, thus reducing energy usage without causing data unavailability.
3Loss of energy
If data is cached at remote nodes instead of the original sensor node, then energy and bandwidth costs are reduced, but data freshness may be compromised
Solution Approach 1:
The system applies partial action by caching data selectively rather than continuously updating all nodes. Cached data is used when sufficient for the application's needs, and real-time updates are performed only when necessary. This partial approach reduces energy and bandwidth costs while maintaining adequate data freshness for many applications without requiring constant synchronization.
Data Source
AI summary
In a network of sensor nodes, operational efficiency may be increased by configuring the sensor nodes so that sensor agents may be transferred to alternative sensor nodes to process sensor node data, such as when the host sensor node is in a low-power mode. A processing node of the network may be configured to retrieve real-time data from a sensor node, but if real-time data is not available, the processing node may perform calculations on cached data retrieved from a processor node cache or data of a nearby sensor node.