Experience-oriented smart embedded system

Embedded systems have been in use since the 1970’s. For most of their history embedded systems were seen simply as small computers designed to accomplish one or a few dedicated functions; and they were usually working under limited resources i.e. limited memories, limited processing power, and limited energy sources. As such, embedded systems have not drawn much attention from researchers, especially from those in the artificial intelligence area. However, things have changed as today the drive for innovation is stronger than ever. Thanks to the efforts of scientists over recent years, great progress has been made in both computer hardware and software, which enables us to have much more powerful computers in very small sizes and with many more functions. Consequently, new needs and expectations for Embedded Systems (ESs) have increased dramatically. The current market demands embedded systems to be built smart so that they can finish tasks automatically, and assist users to make decisions more efficiently and effectively. Thus, how to make embedded systems smart is becoming one of researchers’ new challenges. Knowledge Management (KM) is a discipline that promotes a systematic approach to capturing, storing, distributing, and reusing information of an organization in order to make it available, actionable, and valuable to others. The prospects for applying KM technologies to embedded systems to meet these demands are very promising. The Experience-Oriented Smart Embedded System (EOSES) is proposed as a new technological platform providing a common knowledge management approach that allows mass embedded systems for experiential knowledge capturing, storage, involving, and sharing. Knowledge in the EOSES is represented as SOEKS, and organized as Decisional DNA. The platform is mainly based on conceptual principles from Embedded Systems and Knowledge Management. The objective behind this research is to offer large-scale support for intelligent, autonomous, and coordinated KM on various embedded systems. Several conceptual elements of this research have been implemented in testing prototypes, and the experimental results that were obtained show that the EOSES platform can provide active knowledge management to different embedded systems, and it can also enable various systems to learn from their daily operations in many different fields to gather valuable knowledge, assist decision making, reduce human workers’ workload, and improve the system itself. Consequently, the EOSES has great potential for meeting today’s demands for embedded systems, and providing a universe knowledge management platform for mass autonomous mechanisms.