Wireless Mesh Network Route Prediction for Power Reduction
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Solution Overview
Problem
Existing wireless mesh network arrangements lack efficient route prediction for incoming messages, resulting in equal time and power consumption for both outgoing and incoming data transfers, which is inefficient for battery-powered and safety-critical systems.
Innovation Solution
A method of route prediction and device configuration that retrieves routing information from successful data transfers to anticipate and configure devices for active or sleep modes, optimizing data transfer routes and reducing power consumption by ensuring only necessary devices are active during communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If standard device signalling and selection methods are used for routing incoming messages, then routing is simple to implement, but data transfer time is increased and power consumption is higher
Solution Approach 1:
The system performs preliminary routing predictions based on historical successful data transfer routes before incoming messages arrive. Devices are pre-configured with predicted routing information, allowing them to switch to active state and prepare for message reception in advance, thereby reducing actual data transfer time without adding complex real-time routing logic
Solution Approach 2:
The system uses its own historical routing data to generate predictions for future routing decisions. Each device maintains and contributes to the routing information database, which is then used by the system to predict optimal routes, eliminating the need for external routing controllers or complex centralized routing algorithms
2Speed
If all devices remain in active state to ensure immediate message reception, then data transfer speed is maintained, but power consumption increases
Solution Approach 1:
Devices switch to active state in advance based on predicted routing information before incoming messages arrive. This allows them to be ready for immediate message reception without remaining continuously active, thereby maintaining message reception speed while reducing overall power consumption through periodic sleep modes
Solution Approach 2:
The system dynamically adjusts device states between active and sleep modes based on predicted routing requirements. Devices transition to active state only when predicted to be part of a data transfer route, and return to sleep mode when not needed, creating a dynamic power management system that adapts to actual communication demands
3Use of energy by moving object
If devices switch between active and de-active states based on route prediction, then power consumption is reduced, but routing complexity increases
Solution Approach 1:
The system generates routing predictions using its own historical successful data transfer routes stored in the network. This self-service approach uses existing network data rather than requiring complex external routing algorithms, reducing the complexity burden while achieving effective power management through predictive routing
4Productivity
If route prediction is implemented to reduce data transfer time, then productivity improves, but device complexity increases
Solution Approach 1:
Routing predictions and device configurations are established in advance based on historical data before actual message transfer begins. This preliminary configuration enables faster data transfer by eliminating real-time routing decisions, while the complexity is managed through automated prediction algorithms that use existing network routing information
Data Source
AI summary
A method of route prediction and device configuration for enhancing data transfer across a wireless network arrangement by using routing information to predict data transfer routes and configuring devices to switch between states to reduce data transference time and power consumption of a wireless network arrangement.