Artificial intelligence tools can aid sensor systems: extended paper for electronic journal and WWW publication

Seven artificial intelligence (AI) tools are reviewed that have proved to be useful with sensor systems. They are: knowledge-based systems, fuzzy logic, automatic knowledge acquisition, neural networks, genetic algorithms, case-based reasoning, and ambient-intelligence. Each AI tool is outlined, together with some examples of its use with sensor systems. Applications of these tools within sensor systems have become more widespread due to the power and affordability of present-day computers. Many new sensor applications may emerge, and greater use may be made of hybrid tools that combine the strengths of two or more of the tools reviewed.

The tools and methods reviewed here have minimal computation complexity and can be implemented with small sensor systems, single sensors, or system arrays with low-capability microcontrollers. The appropriate deployment of the new AI tools will contribute to the creation of more competitive sensor systems and applications. Other technological developments in AI that will impact sensor systems include data mining techniques, multi-agent systems, and distributed self-organizing systems. Ambient sensing involves integrating many microelectronic processors and sensors into everyday objects to make them “smart.” They can explore their environment, communicate with other smart things, and interact with humans. Advice provided aims to help users cope with their tasks in intuitive ways, but the repercussion of such integration into our lives is difficult to predict. Using ambient intelligence and a mix of AI tools is an effort to use the best of each technology. The concepts are generically applicable across industrial processes, and this research is intended to show that the concepts work in practice.