Smart Embedded Systems with Decisional DNA Knowledge Representation

Embedded systems have been in use since the 1970s. 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 computing power, limited memories, and limited energy sources. As such, embedded systems have not drawn much attention from researchers, especially from those in the artificial intelligence area. 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 expectations and needs for embedded systems have increased considerably. Today, smart embedded systems are expected, which are supposed to have capability to learn from past task executions and evolve their performance based on learnt knowledge, and assist users to make good decisions more efficiently. Therefore, how to make embedded systems smart is becoming one of the researchers’ new challenges. In this chapter, we introduce the Experience-Oriented Smart Embedded Systems (EOSES) that is proposed as a new technological scheme providing embedded systems with capabilities for experiential knowledge capturing, storage, reuse, evolving, and sharing. In this scheme, knowledge is represented as the Set of Experience Knowledge Structure (SOEKS or shortly SOE) and organized as Decisional DNA. The scheme 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 knowledge management on various embedded systems. Several conceptual elements of this research have been implemented in testing prototypes, and the experimental results show that the EOSES scheme can not only provide active knowledge management to different embedded systems, it can also enable various systems to learn from their daily operations in many different fields to acquire valuable knowledge, assist decision making, reduce human workers’ workload, and improve the system’s performance. As a result, the EOSES has great potential for meeting today’s demands for embedded systems, and providing a universe knowledge management scheme for mass autonomous mechanisms.