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Ubiquitous Computing-Oriented Distributed Fuzzy Reasoning Petri Net Modeling and Simulation

机译:面向通用计算的分布式模糊推理Petri网建模与仿真

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In a ubiquitous computing environment, contexts are initially got and stored on those nodes scattered over the environment. However, the traditional reasoning about contexts applied a centralized approach which aggravated the load of reasoning server and cost for communication of context. As an important approach for context reasoning, the rule-based reasoning can be easily decomposed of independent propositions. Therefore, the rule-based context reasoning can be decomposed and distributed over those nodes of the ubiquitous environment to decrease the computing load of reasoning server. In this paper, we propose a distributed fuzzy reasoning Petri net model (dFRPN) towards the decomposition of distributed fuzzy reasoning. dFRPN model is able to formalize the distribution of fuzzy reasoning over every node of the whole environment. dFRPN model is characterized as a hierarchical structure to make a description of reasoning on nodes more clear. Considering the limited capabilities of some mobile nodes such as PDA and smart phone, we add the special migrating transition to define the load detection and corresponding actions. At the end of the paper, the feasibility of dFRPN model is validated through a case of context-awareness based personalized recommendation system.
机译:在无处不在的计算环境中,首先获取上下文并将其存储在分散在环境中的那些节点上。然而,关于上下文的传统推理应用了集中式方法,这加重了推理服务器的负担和上下文通信的成本。作为上下文推理的一种重要方法,基于规则的推理可以轻松地分解为独立的命题。因此,基于规则的上下文推理可以分解并分布在普适环境的那些节点上,以减少推理服务器的计算量。在本文中,我们针对分布式模糊推理的分解提出了分布式模糊推理Petri网模型(dFRPN)。 dFRPN模型能够形式化模糊推理在整个环境的每个节点上的分布。 dFRPN模型的特征是分层结构,以使对节点的推理描述更加清晰。考虑到PDA和智能手机等某些移动节点的功能有限,我们添加了特殊的迁移过渡来定义负载检测和相应的操作。最后,通过基于上下文感知的个性化推荐系统案例,验证了dFRPN模型的可行性。

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