Edge Sensor Actuation for IoT Event Monitoring
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Solution Overview
Problem
Conventional IoT systems face challenges in real-time sensor actuation and control, particularly in edge-based computing, due to power constraints and the need for accurate, low-latency data processing, which is hindered by the limitations of cloud-based analytics and network connectivity, especially in industrial settings where continuous monitoring is critical.
Innovation Solution
A method and system for edge-based sensor actuation and control in IoT networks that utilize a combination of hardware and software processing, employing a publish/subscribe communication protocol, hierarchical sensor selection, and waveform control mechanisms to adaptively choose and actuate sensors, reducing power consumption and latency while maintaining data accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all sensors are operated in full power mode for continuous monitoring, then measurement precision and reliability are improved, but energy consumption increases significantly
Solution Approach 1:
The patent implements dynamic sensor operation modes where sensors switch between full power, reduced power, and sleep modes based on detected event characteristics. The edge computing device adjusts sensor operation in real-time, transitioning from continuous full-power operation to event-triggered selective operation, thereby reducing energy consumption while maintaining detection accuracy for critical events.
Solution Approach 2:
The system employs periodic sampling and event-triggered activation instead of continuous full-power operation. Sensors are activated periodically or upon detecting specific event patterns, allowing the system to balance between energy savings and maintaining measurement precision for important monitoring parameters.
2Productivity
If cloud-based analytics are used for sensor data processing, then computational power is improved, but latency and network dependency increase
Solution Approach 1:
The patent segments the data processing architecture into edge computing devices deployed at distributed locations and cloud-based analytics. The edge devices perform real-time preprocessing, event detection, and initial analysis locally, while the cloud handles aggregate analysis and long-term storage. This segmentation enables real-time response at the edge while leveraging cloud computational power for non-time-critical tasks.
Solution Approach 2:
The edge computing device acts as an intermediary between sensors and the cloud. It receives sensor data, performs local processing and event detection, and only transmits relevant results or aggregated data to the cloud, reducing network dependency and latency for time-critical operations while maintaining cloud-based analytical capabilities.
3Use of energy by moving object
If sensors are activated selectively based on event stages, then energy consumption is reduced, but system complexity for sensor control increases
Solution Approach 1:
The patent implements self-service sensor control where the edge computing device autonomously determines which sensors to activate based on detected event patterns and predefined criteria. The system automatically transitions sensors between operation modes without requiring complex external control mechanisms, reducing overall system complexity while enabling selective sensor activation for energy savings.
4Speed
If edge-based decision making is implemented for real-time sensor control, then response time is improved, but computational resources at edge devices are constrained
Solution Approach 1:
The patent applies partial action at the edge by implementing only the essential real-time processing functions needed for immediate sensor control and event detection. Complex analytical tasks are deferred to the cloud or performed periodically, allowing the edge device to operate within its computational constraints while still achieving fast response times for critical control decisions.
Data Source
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AI summary
Any sensing system is faced with triangle of dilemma between accuracy, latency and energy. High energy and high latency sensing systems are often very accurate but less useful. Embodiments herein provide a method and system for edge based sensor controlling in the IoT network for event monitoring. The system disclosed herein applies a hierarchical sensor selection process and adaptively chooses sensors among multiple sensors deployed in the IoT network. Further, on-the-fly changes operation modes of the sensors to automatically produce the best possible inference from the selected sensor data, in time, power and latency at the edge. Further, sensors of the system include a waveform and diversity control mechanism that enables controlling of an excitation signal of the sensor.