Sensingbased

Sensingbased, often written sensing-based, is an approach in engineering and computer science that relies on data from sensors to observe the state of a system, environment, or process, and to guide perception, analysis, and action. It emphasizes direct measurement and observation over purely theoretical models.

A sensing-based system typically comprises sensors, communication and data processing components, and a decision or control module. Data are acquired, preprocessed, and fused to form situational awareness, then used to make decisions, adjust behavior, or trigger alerts in real time.

Applications span robotics and automation (perception-based navigation, manipulation), autonomous vehicles, Internet of Things and smart cities (sensor networks for traffic, energy use), environmental monitoring (climate, air and water quality), agriculture (soil moisture, crop health), and healthcare (wearable monitors, early warning systems).

Benefits include responsiveness to current conditions, robustness to model errors, and the ability to adapt to unforeseen scenarios. Challenges involve sensor noise and failure, calibration, data fusion complexity, latency, privacy, and cost.

In published literature the term is commonly hyphenated as sensing-based, though some usages write sensingbased, especially in branding or when used as a single word in code or product names. It is related to but distinct from model-based or data-driven approaches; sensing-based emphasizes the role of direct observation in guiding decisions.