基于谓词检测方法的上下文感知案例研究

当前，开发灵活的适应环境变化的上下文感知应用较为复杂。在上下文感知应用开发过程中，很多未知情况无法事先被充分考虑到，实际的开发又需要处理很多意外情形，因此有效地表示和处理上下文尤为重要。谓词检测作为实现上下文感知的重要方法之一，能够有效对上下文信息进行建模，但在实际应用中对于真实物理设备的支持如何在很大程度上仍然是未知的。为了应对以上问题，通过构建一个简单的物理场景，将谓词检测方法应用于真实的机器小车上，支持其在一个既定轨道上完成运行任务。在该场景下对原始的上下文环境进行建模，将环境特性逐步规约成特定的快照谓词和序列谓词，并在案例研究中，对规约化的谓词进行检测，将谓词检测应用于真实的机器小车实验上。实验分析的结果表明,谓词检测的方法能有效检测小车的上下文特性并成功支撑小车完成既定的行走目标。

Case study of achieving context-awareness based on predicate detection

Currently, to develop context-aware applications that are flexible and adaptable is complex and laborious. There are many unexpected cases to handle. As one of the important approaches to achieve context-awareness, predicate detection can represent context effectively. However, how predicate detection supports the development of context-aware applications on a real device is still largely unknown. In order to cope with these issues, a simple scenario was created. Predicate detection was practically applied to control the car running in a designated environment. The original context was formally modeled and contextual properties were specified into snapshot predicates and sequence predicates. By detecting these specified predicates in the case study, predicate detection was applied to the robot car. The performance analysis shows that predicate detection can effectively detect the car's contextual properties and successfully help the car finish the running task.