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Using Compact Evolutionary Tabu Search algorithm for matching sensor ontologies

SWARM AND EVOLUTIONARY COMPUTATION 2019年 48卷 25-30页

作者： Xue, Xingsi Chen, Junfeng Fujian Univ Technol Coll Informat Sci & Engn Fuzhou 350118 Fujian Peoples R China Fujian Univ Technol Intelligent Informat Proc Res Ctr Fuzhou 350118 Fujian Peoples R China Fujian Univ Technol Fujian Prov Key Lab Big Data Min & Applicat Fuzhou 350118 Fujian Peoples R China Fujian Univ Technol Fujian Key Lab Automot Elect & Elect Drive Fuzhou 350118 Fujian Peoples R China Hohai Univ Coll IOT Engn Changzhou 213022 Jiangsu Peoples R China

To implement the semantic interoperability among intelligent sensor applications, it is necessary to match the identical entities across the sensor ontologies. Since sensor ontology matching problem requires matching thousands of sensor concepts and has many local optimal solutions, Evolutionary Algorithm (EA) becomes the state-of-the-art methodology for solving it. However, the premature convergence and long runtime are two drawbacks which make EA-based sensor ontology matchers incapable of effectively searching the optimal solution for sensor ontology matching problem. To improve the efficiency of EA-based sensor ontology matching technique, in this paper, a new optimal model of sensor ontology matching problem is first constructed, a novel sensor concept similarity measure is then presented to determine the identical sensor concepts, and finally, a problem-specific Compact Evolutionary Tabu Search algorithm (CETS) is presented to efficiently determine the sensor ontology alignment. In particular, CETS combines Compact Evolutionary Algorithm (global search) and Tabu Search algorithm (local search), and this marriage between global search and local search allows keeping high solution diversity via PV (reducing the possibility of the premature convergence) and increasing the convergence speed via the local search (reducing the runtime). The experimental results show that comparing with the state-of-the-art sensor ontology matching techniques, CETS can more efficiently determine the high-quality alignments.

关键词： semantic sensor web sensor ontology matching Compact Evolutionary Algorithm Tabu Search

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sensor Ontology Building in semantic sensor web

Sensor Ontology Building in Semantic Sensor Web

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International Workshop on Internet of Things (IOT Workshop 2012)

作者： Shi, Yimin Li, Guanyu Zhou, Xiaoping Zhang, Xianzhong Dalian Maritime Univ Informat Sci & Technol Coll Dalian Peoples R China

ISBN: (纸本)9783642324260

To address the shortcomings of existing sensor ontologies, such as lack of a unified ontology framework and being built completely manually, this work proposes the sensor ontology framework and algorithms for automatic sensor ontology update and extension. In addition, sensor discovery based on time, space and theme is implemented. sensor core ontology and its update and extension are evaluated. The experimental result reveals that the proposed sensor ontology framework is feasible, and the algorithms for sensor ontology update and extension are effective.

关键词： sensor Ontology sensor Core Ontology Building sensor Ontology Update and Extension semantic sensor web sensor Discovery

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Distributed semantic sensor web architecture

Distributed semantic sensor web architecture

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IEEE Region 10 Conference (TENCON) - Sustainable Development through Humanitarian Technology

作者： Choi, Hoan-Suk Rhee, Woo-Seop Hanbat Natl Univ Dept Multimedia Engn Taejon South Korea

ISBN: (纸本)9781467348232;9781467348225

Recently, data on the web has been increased by interactive service, social media and the pervasive computing technology. To utilize these huge data include sensor data, semantic sensor web service platform is needed. And it can provide the context-aware semantic web service. But, because sensor generated data is so dynamic, it can't always update and store. Therefore, we propose the distributed semantic sensor web architecture to separate processing of context information and service information. This paper analyzes the requirements of semantic web of things and proposes the semantic sensor web service environment to provide user centric context-aware semantic web service through the semantic sensor web platform.

关键词： semantic sensor web semantic web of Things service platform Context-aware semantic web Pervasive service

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Sensing Presence (PreSense) Ontology: User Modelling in the semantic sensor web

Sensing Presence (PreSense) Ontology: User Modelling in the ...

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8th Extended semantic web Conference (ESWC 2011)

作者： Cano, Amparo-Elizabeth Dadzie, Aba-Sah Uren, Victoria Ciravegna, Fabio Univ Sheffield Dept Comp Sci Sheffield S10 2TN S Yorkshire England

ISBN: (纸本)9783642259524;9783642259531

Increasingly, people's digital identities are attached to, and expressed through, their mobile devices. At the same time digital sensors pervade smart environments in which people are immersed. This paper explores different perspectives in which users' modelling features can be expressed through the information obtained by their attached personal sensors. We introduce the PreSense Ontology, which is designed to assign meaning to sensors' observations in terms of user modelling features. We believe that the Sensing Presence (PreSense) Ontology is a first step toward the integration of user modelling and "smart environments". In order to motivate our work we present a scenario and demonstrate how the ontology could be applied in order to enable context-sensitive services.

关键词： Linked data streams semantic sensor web user modelling smart objects

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A Compact Co-Evolutionary Algorithm for sensor ontology meta-matching

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KNOWLEDGE AND INFORMATION SYSTEMS 2018年第2期56卷 335-353页

作者： Xue, Xingsi Pan, Jeng-Shyang Fujian Univ Technol Coll Informat Sci & Engn Fuzhou 350118 Fujian Peoples R China Fujian Univ Technol Fujian Prov Key Lab Big Data Min & Applicat Fuzhou 350118 Fujian Peoples R China

With the proliferation of sensors, semantic web technologies are becoming closely related to sensor network. The linking of elements from semantic web technologies with sensor networks is called semantic sensor web whose main feature is the use of sensor ontologies. However, due to the subjectivity of different sensor ontology designer, different sensor ontologies may define the same entities with different names or in different ways, raising so-called sensor ontology heterogeneity problem. There are many application scenarios where solving the problem of semantic heterogeneity may have a big impact, and it is urgent to provide techniques to enable the processing, interpretation and sharing of data from sensor web whose information is organized into different ontological schemes. Although sensor ontology heterogeneity problem can be effectively solved by Evolutionary Algorithm (EA)-based ontology meta-matching technologies, the drawbacks of traditional EA, such as premature convergence and long runtime, seriously hamper them from being applied in the practical dynamic applications. To solve this problem, we propose a novel Compact Co-Evolutionary Algorithm (CCEA) to improve the ontology alignment's quality and reduce the runtime consumption. In particular, CCEA works with one better probability vector (PV) and one worse PV , where mainly focuses on the exploitation which dedicates to increase the speed of the convergence and pays more attention to the exploration which aims at preventing the premature convergence. In the experiment, we use Ontology Alignment Evaluation Initiative (OAEI) test cases and two pairs of real sensor ontologies to test the performance of our approach. The experimental results show that CCEA-based ontology matching approach is both effective and efficient when matching ontologies with various scales and under different heterogeneous situations, and compared with the state-of-the-art sensor ontology matching systems, CCEA-based ontology match

关键词： Compact Co-Evolutionary Algorithm Ontology meta-matching semantic sensor web

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A Preference-Based Multi-Objective Evolutionary Algorithm for Semiautomatic sensor Ontology Matching

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INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2018年第2期9卷 1-14页

作者： Xue, Xingsi Chen, Junfeng Fujian Univ Technol Coll Informat Sci & Engn Fuzhou Fujian Peoples R China Hohai Univ Coll IOT Engn Changzhou Peoples R China

This article describes how with the advent of sensors for collecting environmental data, many sensor ontologies have been developed. However, the heterogeneity of sensor ontologies blocks semantic interoperability between them and limits their applications. Ontology matching is an effective technique to solve the problem of sensor ontology heterogeneity. To improve the quality of sensor ontology alignment, the authors propose a semiautomatic ontology matching technique based on a preference-based multi-objective evolutionary algorithm (PMOEA), which can utilize the user's knowledge of the solution's quality to direct MOEA to effectively match the heterogeneous sensor ontologies. The authors specifically construct a new multi-objective optimal model for the sensor ontology matching problem, propose a user preference-based t-dominance rule, and design a PMOEA to solve the sensor ontology matching problem. The experimental results show that their approach can significantly improve the sensor ontology alignment's quality under different heterogeneous situations.

关键词： Preference-Based Multi-Objective Evolutionary Algorithm semantic sensor web Semiautomatic Ontology Matching T-Dominance Rule

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Matching sensor Ontologies Through Compact Evolutionary Tabu Search Algorithm 11th

Matching Sensor Ontologies Through Compact Evolutionary Tabu...

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11th International Conference on Security, Privacy and Anonymity in Computation, Communication, and Storage (SpaCCS)

作者： Xue, Xingsi Liu, Shijian Fujian Univ Technol Coll Informat Sci & Engn Fuzhou 350118 Fujian Peoples R China Fujian Univ Technol Intelligent Informat Proc Res Ctr Fuzhou 350118 Fujian Peoples R China Fujian Univ Technol Fujian Prov Key Lab Big Data Min & Applicat Fuzhou 350118 Fujian Peoples R China Fujian Univ Technol Fujian Key Lab Automot Elect & Elect Drive Fuzhou 350118 Fujian Peoples R China

ISBN: (纸本)9783030053451;9783030053444

Although sensor ontologies are regarded as the solution to data heterogeneity on the semantic sensor web (SSW), these sensor ontologies themselves introduce heterogeneity by defining the same entity with different names or in different ways. To solve this problem, it is necessary to determine the semantic identical entities between heterogeneous sensor ontologies, so-called sensor ontology matching. Due to the complexity of the sensor ontology matching process, Evolutionary Algorithm (EA) can present a good methodology for determining ontology alignments. To overcome the EA-based ontology matcher's shortcomings, i.e. premature convergence, long runtime and huge memory consumption, this paper present a Compact Evolutionary Tabu Search algorithm (CETS) to efficiently match the sensor ontologies. The experiment utilizes Ontology Alignment Evaluation Initiative (OAEI)'s bibliographic benchmark and library track, and two pairs of real sensor ontologies test CETS's performance. The experimental results show that CETS is both effective and efficient when matching ontologies with various scales and under different heterogeneous situations, and comparing with the state-of-the-art sensor ontology matching systems, CETS can significantly improve the ontology alignment's quality.

关键词： semantic sensor web sensor ontology matching Compact evolutionary algorithm Tabu search

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A semantic sensor web for Environmental Decision Support Applications

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sensorS 2011年第9期11卷 8855-8887页

作者： Gray, Alasdair J. G. Sadler, Jason Kit, Oles Kyzirakos, Kostis Karpathiotakis, Manos Calbimonte, Jean-Paul Page, Kevin Garcia-Castro, Raul Frazer, Alex Galpin, Ixent Fernandes, Alvaro A. A. Paton, Norman W. Corcho, Oscar Koubarakis, Manolis De Roure, David Martinez, Kirk Gomez-Perez, Asuncion Univ Manchester Sch Comp Sci Manchester M13 9PL Lancs England Univ Southampton GeoData Inst Southampton SO17 1BJ Hants England Potsdam Inst Climate Impact Res Dept Climate Impacts & Vulnerabil D-14412 Potsdam Germany Natl & Kapodistrian Univ Athens Dept Informat & Telecommun Athens 15784 Greece Univ Politecn Madrid Ontol Engn Grp E-28660 Madrid Spain Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ Hants England

Sensing devices are increasingly being deployed to monitor the physical world around us. One class of application for which sensor data is pertinent is environmental decision support systems, e.g., flood emergency response. For these applications, the sensor readings need to be put in context by integrating them with other sources of data about the surrounding environment. Traditional systems for predicting and detecting floods rely on methods that need significant human resources. In this paper we describe a semantic sensor web architecture for integrating multiple heterogeneous datasets, including live and historic sensor data, databases, and map layers. The architecture provides mechanisms for discovering datasets, defining integrated views over them, continuously receiving data in real-time, and visualising on screen and interacting with the data. Our approach makes extensive use of web service standards for querying and accessing data, and semantic technologies to discover and integrate datasets. We demonstrate the use of our semantic sensor web architecture in the context of a flood response planning web application that uses data from sensor networks monitoring the sea-state around the coast of England.

关键词： semantic sensor web application and visualisation semantic data integration

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An Observation Capability semantic-Associated Approach to the Selection of Remote Sensing Satellite sensors: A Case Study of Flood Observations in the Jinsha River Basin

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sensorS 2018年第5期18卷 1649-1649页

作者： Hu, Chuli Li, Jie Lin, Xin Chen, Nengcheng Yang, Chao China Univ Geosci Wuhan Fac Informat Engn Wuhan 430074 Hubei Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Hubei Peoples R China

Observation schedules depend upon the accurate understanding of a single sensor's observation capability and the interrelated observation capability information on multiple sensors. The general ontologies for sensors and observations are abundant. However, few observation capability ontologies for satellite sensors are available, and no study has described the dynamic associations among the observation capabilities of multiple sensors used for integrated observational planning. This limitation results in a failure to realize effective sensor selection. This paper develops a sensor observation capability association (SOCA) ontology model that is resolved around the task-sensor-observation capability (TSOC) ontology pattern. The pattern is developed considering the stimulus-sensor-observation (SSO) ontology design pattern, which focuses on facilitating sensor selection for one observation task. The core aim of the SOCA ontology model is to achieve an observation capability semantic association. A prototype system called SemOCAssociation was developed, and an experiment was conducted for flood observations in the Jinsha River basin in China. The results of this experiment verified that the SOCA ontology based association method can help sensor planners intuitively and accurately make evidence-based sensor selection decisions for a given flood observation task, which facilitates efficient and effective observational planning for flood satellite sensors.

关键词： semantic sensor web observation capability ontology sensor selection observation planning flood satellite sensors flood observation

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An Ontology-Driven Approach for Integrating Intelligence to Manage Human and Ecological Health Risks in the Geospatial sensor web

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sensorS 2018年第11期18卷 3619-3619页

作者： Meng, Xiaoliang Wang, Feng Xie, Yichun Song, Guoqiang Ma, Shifa Hu, Shiyuan Bai, Junming Yang, Yiming Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430079 Hubei Peoples R China Natl Adm Surveying Mapping & Geoinformat Engn Res Ctr Geoinformat & Digital Technol Wuhan 430079 Hubei Peoples R China Eastern Michigan Univ Inst Geospatial Res & Educ Ypsilanti MI 48197 USA Hubei Environm Monitoring Cent Stn Wuhan 430079 Hubei Peoples R China Land & Resources Technol Ctr Guangdong Prov Guangzhou 510075 Guangdong Peoples R China Wuhan Univ Sch Resource & Environm Sci Wuhan 430079 Hubei Peoples R China

Due to the rapid installation of a massive number of fixed and mobile sensors, monitoring machines are intentionally or unintentionally involved in the production of a large amount of geospatial data. Environmental sensors and related software applications are rapidly altering human lifestyles and even impacting ecological and human health. However, there are rarely specific geospatial sensor web (GSW) applications for certain ecological public health questions. In this paper, we propose an ontology-driven approach for integrating intelligence to manage human and ecological health risks in the GSW. We design a Human and Ecological health Risks Ontology (HERO) based on a semantic sensor network ontology template. We also illustrate a web-based prototype, the Human and Ecological Health Risk Management System (HaEHMS), which helps health experts and decision makers to estimate human and ecological health risks. We demonstrate this intelligent system through a case study of automatic prediction of air quality and related health risk.

关键词： ecological public health heterogeneous geospatial sensors computational intelligence semantic sensor web software agents

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