【摘要】：物聯(lián)網(wǎng)(Internet of Things)是一個(gè)由各種智能傳感設(shè)備組成的龐大網(wǎng)絡(luò)。由于物聯(lián)網(wǎng)信息的不完備性和不確定性阻礙了物聯(lián)網(wǎng)中信息協(xié)調(diào)和交互,將語義技術(shù)引入物聯(lián)網(wǎng)中,從而產(chǎn)生了語義物聯(lián)網(wǎng)(Semantic Web of Things,SWoT)。語義物聯(lián)網(wǎng)服務(wù)特征表現(xiàn)為海量性、開放性、動(dòng)態(tài)性、不完全性和異構(gòu)性,導(dǎo)致難以根據(jù)用戶需求選擇服務(wù)。為了解決這一問題,本文提出了一種語義物聯(lián)網(wǎng)中基于上下文感知的服務(wù)選擇方法。充分考慮用戶選擇時(shí)的語義、顯性偏好、隱性偏好和上下文信息。首先,對(duì)于服務(wù)庫中信息的模糊性和用戶表達(dá)的不明確性,進(jìn)行語義標(biāo)注和分類使得信息更容易協(xié)同和交互,選擇出滿足用戶需要的服務(wù)。其次,獲得用戶的顯性和隱性偏好,選擇出滿足用戶顯性和隱性偏好的候選服務(wù)集。其中,顯性偏好是由用戶提出的需求直接獲得,隱性偏好則由傳感器所獲得的用戶上下文信息與模糊規(guī)則庫經(jīng)過推理機(jī)推理得到。再次,將候選服務(wù)集進(jìn)行量化(離散化)和歸一化處理,其結(jié)果作為螢火蟲優(yōu)化神經(jīng)網(wǎng)絡(luò)的輸入,選擇最佳螢火蟲來確定神經(jīng)網(wǎng)絡(luò)的權(quán)重與閾值以提高訓(xùn)練效率,最終為用戶返回有序的服務(wù)列表。為驗(yàn)證方法的可行性,本文設(shè)計(jì)了語義物聯(lián)網(wǎng)中基于上下文感知的服務(wù)選擇原型系統(tǒng)(SWoT-oriented context aware-based service selection prototype system,SWoT-CBSS)。該系統(tǒng)從功能上分為服務(wù)語義標(biāo)注、服務(wù)分類、服務(wù)篩選和螢火蟲優(yōu)化神經(jīng)網(wǎng)絡(luò)四個(gè)模塊,分別實(shí)現(xiàn)了語義標(biāo)注、分類與選擇功能。通過用戶選擇酒店服務(wù)對(duì)系統(tǒng)進(jìn)行驗(yàn)證。結(jié)果表明,本文設(shè)計(jì)的SWoT-CBSS在查準(zhǔn)率和查全率都有一定的保證,雖然響應(yīng)時(shí)間略長(zhǎng),但總體上能夠得到滿足用戶需求的服務(wù)。
[Abstract]:Internet of things (Internet of Things) is a huge network composed of various intelligent sensing devices. Because the incompleteness and uncertainty of Internet of things information hinder the coordination and interaction of information in the Internet of things, semantic technology is introduced into the Internet of things, resulting in semantic Internet of things (Semantic Web of Things,SWoT). The semantic Internet of things service is characterized by sea quantity, openness, dynamics, incompleteness and heterogeneity, which makes it difficult to choose services according to the needs of users. In order to solve this problem, this paper proposes a context-aware service selection method in semantic Internet of things. Fully consider the semantics, explicit preferences, implicit preferences and contextual information of user selection. First of all, for the fuzziness of information and the uncertainty of user expression in the service library, semantic tagging and classification make the information easier to cooperate and interact, and select the services to meet the needs of users. Secondly, the explicit and implicit preferences of users are obtained, and the candidate service sets that meet the explicit and implicit preferences of users are selected. Among them, the explicit preference is obtained directly by the requirements proposed by the user, and the implicit preference is obtained by the inference engine reasoning of the user context information and fuzzy rule base obtained by the sensor. Thirdly, the candidate service set is quantified (discretized) and normalized, and the results are used as the input of the firefly optimization neural network, and the optimal firefly is selected to determine the weight and threshold of the neural network in order to improve the training efficiency. Finally, an orderly list of services is returned for the user. In order to verify the feasibility of the method, a context-aware service selection prototype system (SWoT-oriented context aware-based service selection prototype system,SWoT-CBSS) in semantic Internet of things is designed in this paper. The system is divided into four modules: service semantic tagging, service classification, service screening and firefly optimization neural network. The functions of semantic tagging, classification and selection are realized respectively. The system is verified by the user's choice of hotel service. The results show that the SWoT-CBSS designed in this paper has a certain guarantee in precision and recall. Although the response time is a little longer, it can get the service to meet the needs of users as a whole.
【學(xué)位授予單位】：大連海事大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.44;TN929.5

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