Wireless Access Control Routing via Energy and Latency Metrics
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Solution Overview
Problem
Access control systems face challenges in efficiently managing energy consumption and latency metrics across wireless access controls, particularly in determining optimal data routes that balance energy conservation and latency constraints.
Innovation Solution
The method involves determining energy and latency metrics for each access control, transmitting these metrics wirelessly via Bluetooth, and using a head node to determine energy-constrained or latency-constrained data routes, ensuring efficient data propagation across the access control system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If wireless transmission is used for data communication between access controls, then system flexibility and ease of installation are improved, but energy consumption increases
Solution Approach 1:
The system dynamically adapts transmission parameters including data rate, power level, and modulation scheme based on real-time channel conditions and energy status. Access controls adjust their transmission behavior dynamically to optimize the trade-off between communication reliability and energy consumption, allowing flexible operation while conserving battery power.
Solution Approach 2:
The system changes multiple transmission parameters including data rate, power spectral density, coding rate, and modulation format based on channel quality and energy constraints. By adjusting these parameters dynamically, the system achieves efficient energy utilization while maintaining communication performance across varying operational conditions.
2Speed
If higher data transmission rates are used, then communication speed is improved, but energy consumption and transmission errors increase
Solution Approach 1:
The system dynamically adjusts data transmission rates based on real-time channel conditions and energy status. When channel quality is good and energy is sufficient, higher data rates are used to improve communication speed. When energy is constrained or channel conditions deteriorate, the system automatically reduces data rates to maintain reliable communication while conserving energy.
Solution Approach 2:
The system changes data rate parameters in response to varying operational conditions. By selecting appropriate data rates from available options based on channel quality metrics and energy status, the system optimizes the balance between transmission speed and energy consumption, avoiding unnecessary energy expenditure at high rates when conditions don't permit.
3Speed
If higher data transmission rates are used, then communication speed is improved, but transmission errors increase
Solution Approach 1:
The system dynamically adjusts data rates based on real-time channel quality feedback. When channel conditions are favorable, higher data rates are employed to improve speed. When errors are detected or channel quality degrades, the system automatically reduces data rates and adjusts forward error correction parameters to maintain transmission reliability, ensuring a balance between speed and accuracy.
Solution Approach 2:
The system implements feedback mechanisms where transmission performance is monitored and used to adjust subsequent transmission parameters. Based on error rates and channel quality feedback, the system adapts data rates and error correction levels to maintain reliable communication while optimizing for speed when conditions allow.
4Productivity
If complex routing algorithms are implemented to balance energy and latency, then route optimization is improved, but computational complexity increases
Solution Approach 1:
The routing function is segmented and distributed across multiple access controls rather than centralized in one node. Each access control independently performs simplified routing decisions based on local energy status and pre-exchanged routing information. This segmentation reduces computational burden on individual devices while achieving system-wide route optimization through cooperative distributed decision-making.
Solution Approach 2:
Each access control autonomously determines its own energy status and makes routing decisions based on local information and pre-exchanged data. The system enables self-service routing where nodes independently optimize their participation in data transmission based on their own energy constraints, eliminating the need for complex centralized computation while achieving efficient route selection.
Data Source
AI summary
A method of operating an access control system comprising a plurality of access controls, the method comprising: determining an energy metric of each of the plurality of access controls; determining a latency metric of each of the plurality of access controls; transmitting the energy metric of each of the plurality of access controls; transmitting the latency metric of each of the plurality of access controls; collecting the energy metric and the latency metric at a head node or collecting energy metric at each of the plurality of access controls from a 1-hop transmission distance; and determining a data route through the plurality of access controls in response to the energy metric of each of the plurality of access controls and the latency metric of each of the plurality of access controls.