Abstract
This paper proposes a profile-based sensing framework for adaptive sensor systems based on models that relate possibly heterogeneous sensor data and profiles generated by the models to detect events. With these concepts, three phases for building the sensor systems are extracted from two examples: a combustion control sensor system for an automobile engine, and a sensor system for home security. The three phases are: modeling, profiling, and managing trade-offs. Designing and building a sensor system involves mapping the signals to a model to achieve a given mission.
Highlights
Sensor fusion involves integrating multiple and heterogeneous sensors, intelligent sensors to integrate detection and post-processing of signals, and sensor networks of simple low-power sensors.sensor systems based on profiles that adapt to the environment require domaindependent sensor technology on which each sensor depends and domain-independent common frameworks
Sensor systems based on profiles that adapt to the environment require domaindependent sensor technology on which each sensor depends and domain-independent common frameworks
By fully exploiting the goal-driven mechanism, intelligent sensing improves flexibility, for the goal-driven mechanism can deal with unexpected situations without enumerating all possible cases. This flexibility is essential for sensor systems because the goal-driven activation of sensors can save energy and can guide the system to focus on the target
Summary
Sensor fusion involves integrating multiple and heterogeneous sensors, intelligent sensors to integrate detection and post-processing of signals, and sensor networks of simple low-power sensors. Sensor systems based on profiles that adapt to the environment require domaindependent sensor technology on which each sensor depends and domain-independent common frameworks. Such frameworks are lacking, so this paper proposes a basis for such frameworks. Biological sensing is more goal-oriented than artificial sensing, which merely measures or detects a property of the target object. Another remarkable difference is that biological sensing can deal with a collection of properties of qualitatively different types, while artificial sensing mainly handles a collection of properties of the same type. We will explain the design principle and basic concepts required for adaptive sensing using two examples: automobile engine monitoring and home security with adaptive sensor systems
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
