فایل ورد کامل استدلال معنایی در ابزارهای اینترنت اشیا آگاه به محیط

توجه : به همراه فایل word این محصول فایل پاورپوینت (PowerPoint) و اسلاید های آن به صورت هدیه ارائه خواهد شد

این مقاله، ترجمه شده یک مقاله مرجع و معتبر انگلیسی می باشد که به صورت بسیار عالی توسط متخصصین این رشته ترجمه شده است و به صورت فایل ورد (microsoft word) ارائه می گردد

متن داخلی مقاله بسیار عالی، پر محتوا و قابل درک می باشد و شما از استفاده ی آن بسیار لذت خواهید برد. ما عالی بودن این مقاله را تضمین می کنیم

فایل ورد این مقاله بسیار خوب تایپ شده و قابل کپی و ویرایش می باشد و تنظیمات آن نیز به صورت عالی انجام شده است؛ به همراه فایل ورد این مقاله یک فایل پاور پوینت نیز به شما ارئه خواهد شد که دارای یک قالب بسیار زیبا و تنظیمات نمایشی متعدد می باشد

توجه : در صورت مشاهده بهم ریختگی احتمالی در متون زیر ،دلیل ان کپی کردن این مطالب از داخل فایل می باشد و در فایل اصلی فایل ورد کامل استدلال معنایی در ابزارهای اینترنت اشیا آگاه به محیط،به هیچ وجه بهم ریختگی وجود ندارد

تعداد صفحات این فایل: ۳۳ صفحه

بخشی از مقاله انگلیسیعنوان انگلیسی:Semantic Reasoning for Context-aware Internet of Things Applications~~en~~

Abstract

Acquiring knowledge from continuous and heterogeneous data streams is a prerequisite for IoT applications. Semantic technologies provide comprehensive tools and applicable methods for representing, integrating, and acquiring knowledge. However, resource-constraints, dynamics, mobility, scalability, and real-time requirements introduce challenges for applying these methods in IoT environments. We study how to utilize semantic IoT data for reasoning of actionable knowledge by applying state-of-the-art semantic technologies. For performing these studies, we have developed a semantic reasoning system operating in a realistic IoT environment. We evaluate the scalability of different reasoning approaches, including a single reasoner, distributed reasoners, mobile reasoners, and a hybrid of them. We evaluate latencies of reasoning introduced by different semantic data formats. We verify the capabilities of promising semantic technologies for IoT applications through comparing the scalability and real-time response of different reasoning approaches with various semantic data formats. Moreover, we evaluate different data aggregation strategies for integrating distributed IoT data for reasoning processes.

۱ Introduction

ADVANCES in ICT are bringing into reality the vision of Internet of Things (IoT) where a large number of uniquely identifiable, interconnected objects and things gather information from diverse physical environments and deliver the information to a variety of intelligent applications and services. These sensing objects and things form the IoT that can improve energy and cost efficiency and automation in many different industry fields such as transportation and logistics, health care and manufacturing, and facilitate our everyday lives as well. IoT applications rely on real-time context data and allow sending information for driving the behaviors of users in intelligent environments. Current IoT solutions are mostly tailored for vertical applications and systems, utilizing knowledge only from some particular domain. To realize the full potential of IoT, these disparate silos of expert systems need to be replaced with horizontal collaborative systems, and harnessed by knowledge acquisition and sharing capabilities. [1] Large integrated IoT systems with interoperable nodes are challenging to be built due to the heterogeneity of protocols, data formats, data schemes, and service interfaces. Realtime and scalability requirements, resource-constraints, and device mobility introduce additional challenges in building such systems. To minimize the need for human intervention, these networks and devices should possess auto-connecting, self-healing, and self-organizing capabilities. Device coupling, message routing and integration of information are important issues in open IoT environments, where networks can be unreliable, and devices may be unavailable, connections are typically non-persistent and decoupled IoT nodes are common. These challenges need to be tackled before developing a general IoT infrastructure that enables horizontal IoT systems spanning over various application domains [1]. In this article, we focus on knowledge sharing and integration, that is, on providing and acquiring knowledge in IoT environments. Smart IoT applications and systems demand machine-interpretable data for decision making, and to adapt to different situations and contexts. Shared understanding (i.e. ontologies) is required as well. Semantic Web technologies provide these features and have been noted as essential enablers for IoT as they facilitate reasoning of actionable knowledge from multiple heterogeneous information sources, and disparate knowledge domains, and foster interoperability amongst a variety of applications and systems [2]. Knowledge sharing and integration calls for common representations and knowledge acquisition, in turn, for reasoning actionable knowledge from IoT data. In this article, we study Semantic Web technologies that can facilitate contextawareness, interoperability, and reasoning on IoT. We carry out experiments by evaluating the whole process of delivering real IoT data, aggregating this data, and reasoning from it with different system configurations, based on a real-world scenario. We also study the effect of data aggregation strategies on system performance. These reasoning and data aggregation experiments and their evaluations are our main contributions. Specifically, we do not target developing a general architecture or a platform for IoT systems, but rather evaluate different data provisioning approaches and reasoning in a realistic IoT environment. We study the scalability, latency, and resource usage of reasoning with different system configurations and with semantic data formats that can be supported by IoT devices. We have designed and implemented an IoT system with centralized, distributed, mobile, and hybrid reasoning configurations for carrying out these studies. This article is an extended version of [3] with a more de-tailed literature study, a novel mobile reasoner implementation, and a deeper analysis. The reminder of this article is organized as follows: Section II presents background and related work. Section III describes the scenario and the system architectures and set-ups. Section IV presents the evaluation results. Section V contains discussion, and finally, we conclude our work with suggestions for future work in Section VI.

$$en!!