Dynamic Environmental Sensor Sampling for Access Point Resource Limits
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Solution Overview
Problem
Existing environmental sensors integrated with access points (APs) in IoT networks face challenges due to resource constraints, such as power, computational, and bandwidth overhead, leading to degraded performance and potential operational failure when static sampling configurations are used in dynamic environments.
Innovation Solution
A dynamic sampling configuration process that adjusts sensor measurements based on environmental and network conditions, using sensor measurements and traffic telemetry data to optimize sampling rates, resolutions, and communication schedules, while detecting and correcting for anomalous data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If static sampling configurations are used in environmental sensors, then device complexity is reduced and ease of operation is improved, but resource overhead (power, computational, bandwidth) increases leading to degraded performance and potential operational failure
Solution Approach 1:
The patent implements dynamic sampling configurations that automatically adjust sensor sampling rates based on real-time environmental conditions and network status. The system transitions from static to dynamic configuration, where sampling parameters are continuously optimized to balance resource consumption with data quality requirements, preventing operational failure while maintaining ease of use through automation.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor resource usage, environmental conditions, and data quality metrics to automatically adjust sampling configurations. This closed-loop control ensures that the sensor operates reliably under varying conditions without requiring manual intervention, resolving the contradiction between ease of operation and reliability.
2Device complexity
If static sampling configurations are used in environmental sensors, then device complexity is reduced, but resource overhead increases leading to degraded performance
Solution Approach 1:
The patent employs dynamic sampling configurations that adaptively adjust sensor operation based on real-time conditions. By making sampling rates variable rather than fixed, the system optimizes energy consumption during different operational phases while maintaining acceptable complexity through automated management, thus resolving the contradiction between device complexity and energy usage.
Solution Approach 2:
The system changes sampling parameters dynamically based on environmental conditions and network status. By adjusting sampling rates, resolutions, and communication schedules according to actual needs, the system reduces unnecessary energy consumption while maintaining data quality, effectively resolving the contradiction between device complexity and energy usage.
3Ease of operation
If static sampling configurations are used in environmental sensors, then ease of operation is improved, but resource overhead increases leading to operational failure
Solution Approach 1:
The patent implements dynamic sampling configurations that automatically adjust sensor operation to minimize energy loss while maintaining operational reliability. The system transitions from static to adaptive configuration, where sampling parameters are optimized in real-time based on actual environmental conditions and network status, preventing energy depletion without requiring manual intervention.
Solution Approach 2:
The system uses feedback loops to monitor energy consumption, environmental conditions, and operational status, automatically adjusting sampling configurations to prevent energy loss and operational failure. This automated feedback mechanism maintains ease of operation while significantly reducing energy waste compared to static configurations.
4Reliability
If dynamic sampling configurations are implemented, then resource overhead is reduced and reliability is improved, but device complexity increases
Solution Approach 1:
The patent implements a centralized configuration management system that handles multiple sensors and environmental conditions through a universal dynamic sampling framework. This multi-functional approach allows the system to manage complex adaptive behaviors across multiple sensors while centralizing the complexity in a manageable controller, thus improving reliability without proportionally increasing overall system complexity.
Solution Approach 2:
The system enables sensors to automatically adjust their own sampling configurations based on local environmental conditions and network status, without requiring external management for each adjustment. This self-service capability distributes the complexity management across individual sensors while maintaining centralized coordination, improving reliability while keeping overall system complexity manageable.
5Use of energy by moving object
If dynamic sampling configurations are implemented, then resource overhead is reduced, but device complexity increases
Solution Approach 1:
The patent employs dynamic sampling configurations that adaptively adjust sensor operation based on real-time conditions. By making sampling parameters variable rather than fixed, the system optimizes energy consumption during different operational phases while maintaining acceptable complexity through automated management, thus resolving the contradiction between device complexity and energy usage.
Solution Approach 2:
The system changes sampling parameters dynamically based on environmental conditions and network status. By adjusting sampling rates, resolutions, and communication schedules according to actual needs, the system reduces unnecessary energy consumption while maintaining data quality, effectively resolving the contradiction between device complexity and energy usage.
6Measurement precision
If sampling rates are increased to maintain effective environmental sensing, then measurement precision is improved, but resource overhead increases
Solution Approach 1:
The patent implements dynamic sampling configurations that adjust sampling rates based on actual environmental conditions and data quality requirements. The system increases sampling rates only when necessary to maintain measurement precision, and reduces rates during stable conditions, thereby maintaining precision while minimizing energy loss through adaptive optimization.
Solution Approach 2:
The system dynamically changes sampling parameters including rates, resolutions, and communication schedules based on environmental conditions and network status. This allows the system to maintain measurement precision when needed while reducing energy consumption during periods of stability, effectively resolving the contradiction between measurement precision and energy loss.
Data Source
AI summary
In one embodiment, a device may obtain sensor measurements from an environmental sensor of an access point. The device may obtain traffic telemetry data regarding network traffic handled by the access point. The device may generate, based on the sensor measurements and the traffic telemetry data, a sampling configuration of the environmental sensor of the access point. The device may cause the access point to collect additional sensor measurements from its environmental sensor according to the sampling configuration.