广义世界中的实体作为一个统一的物联网框架:GENIUS项目案例分析
After having briefly discussed some possible interpretations of the (still at least partially ambiguous ambiguous) "IoT" term, this Chapter sums up the aims and the main characteristics of an on-going IoT-inspired project, GENIUS. GENIUS concerns the creation of a flexible, internet-based, IoT cognitive architecture, able to support a wide range of 'intelligent' applications focused on the recognition and interaction with the so-called Generalized World Entities (GWEs). The GWE paradigm intends to fill up the present fracture between the detection of entities at the sensor/physical level and their representation/management at the conceptual level. It deals in a unified way with physical objects, humans, robots, media objects and low-level events generated by sensors and with GWEs at higher level of abstraction corresponding to complex, structured events/situations/behaviours implying mutual relationships among GWEs captured at lower conceptual level. GWEs of both classes will be recognised and categorised by using, mainly, a conceptual "representation of the world", ontology-based, auto-evolving and general enough to take into account both the "static" and "dynamic" characteristics of the GWEs. When all the GWEs (objects, agents, events, complex events, situations, circumstances, behaviours etc.) involved in a given application scenario have been recognised, human-like reasoning procedures in the form of "set of services", general enough to be used in a vast range of GWE-based applications, can be used to solve real-life problems. Details about the use of the GWE paradigm to set up an "Ambient Assisted Living (AAL)" application for dealing with the "elderly at home problem" are provided in the Chapter.
计算研究的可持续发展
Computer science and information technologies offer a wide range of tools for examining sustainability challenges. Advances in computer science have already provided environmental and sustainability researchers with a valuable tool setcomputational modeling, data management, sensor technology, machine learning, and other toolsand additional research in computer science may provide advanced approaches, tools, techniques, and strategies toward understanding, addressing, and communicating sustainability challenges.