Fuzzy-based sensor search in the Web of Things

Truong, Cuong; Römer, Kay Uwe; Chen, Kai

doi:10.1109/iot.2012.6402314

Cited by 44 publications

(28 citation statements)

References 8 publications

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“…Truong et al [12] propose a fuzzy based similarity score comparison sensor search technique to compare the output of a given sensor with the outputs of several other sensors to find a matching sensor. Mayer et al [13] considers the location of smart things/sensors as the main context property and structures them in a logical structure.…”

Section: Background and Related Workmentioning

confidence: 99%

Sensor Search Techniques for Sensing as a Service Architecture for the Internet of Things

Perera¹,

Zaslavsky²,

Liu

et al. 2014

IEEE Sensors J.

175

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Abstract-The Internet of Things (IoT) is part of the Internet of the future and will comprise billions of intelligent communicating "things" or Internet Connected Objects (ICO) which will have sensing, actuating, and data processing capabilities. Each ICO will have one or more embedded sensors that will capture potentially enormous amounts of data. The sensors and related data streams can be clustered physically or virtually, which raises the challenge of searching and selecting the right sensors for a query in an efficient and effective way. This paper proposes a context-aware sensor search, selection and ranking model, called CASSARAM, to address the challenge of efficiently selecting a subset of relevant sensors out of a large set of sensors with similar functionality and capabilities. CASSARAM takes into account user preferences and considers a broad range of sensor characteristics, such as reliability, accuracy, location, battery life, and many more. The paper highlights the importance of sensor search, selection and ranking for the IoT, identifies important characteristics of both sensors and data capture processes, and discusses how semantic and quantitative reasoning can be combined together. This work also addresses challenges such as efficient distributed sensor search and relational-expression based filtering. CASSARAM testing and performance evaluation results are presented and discussed.Index Terms-Internet of Things, context awareness, sensors, search and selection, indexing and ranking, semantic querying, quantitative reasoning, multi-dimensional data fusion.

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Section: Background and Related Workmentioning

confidence: 99%

Sensor Search Techniques for Sensing as a Service Architecture for the Internet of Things

Perera¹,

Zaslavsky²,

Liu

et al. 2014

IEEE Sensors J.

175

View full text Add to dashboard Cite

show abstract

“…In response to above problems, a content-based sensor search engine (CSS) is proposed in [25]. In CSS, an architecture to realize content-based search is presented, including a number of sensor gateways.…”

Section: Related Workmentioning

confidence: 99%

“…In this experiment, we introduce the communication overhead to examine the performance of R-MSE and CSS search engine mentioned in [25], due to the reason that the returned search results are always accurate. The ordered verifying approach, included in R-MSE, needs to communicate with match sensors depending on the global ordered list GOL , which takes a vast proportion in the communication overhead of search system.…”

Section: R-mse Simulation Evaluationmentioning

confidence: 99%

“…In order to assess the validity of global ordered list, we introduce a normalized metric CO [25], which is proportional to the number of sensors that should be verified. For the sensor at rank , we use ( ) to denote the ranking error, which means the number of nonmatch sensors that rank higher than match ones at rank .…”

Section: R-mse Simulation Evaluationmentioning

confidence: 99%

“…. , (21) if (GOL ∈ [ , ]) (22) Adds GOL to the final list ; (23) else (24) continue (25) end if (26) end for (27) return search result list (28) end if (29) end while (30) end algorithm Algorithm 1: MSE scheme pseudocode. Accordingly, we define the normalized metric that essentially represents the communication overhead as follows:…”

Section: R-mse Simulation Evaluationmentioning

confidence: 99%

See 2 more Smart Citations

Matching State Estimation Scheme for Content-Based Sensor Search in the Web of Things

Zhang

Liu

et al. 2015

International Journal of Distributed Sensor Networks

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More recently, an increasing number of object-attached sensors are publishing their real-time state on the Internet by using stateof-the-art Web technologies, which make the sensor search service extremely important for the Web of Things (WoT). However, the existing issues that the sensor search service is facing bring huge challenges to the design of matching state estimation scheme. In this paper, an architecture of high-efficiency content-based sensor search system is depicted to provide a prototype system for sensor search. And then a matching state estimation scheme is proposed in detail, including a sensor state prediction approach to accurately estimate future sensor readings and a match estimating and verifying approach to effectively classify and verify candidate sensors, in order to enhance the performance of our search system. Simulation results show that our matching state estimation scheme dramatically reduces the communication overhead of search system and achieves excellent performance in terms of recall ratio and precision ratio.

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A multidimensional resource model for dynamic resource matching in internet of things

Zhao

Zhang

et al. 2013

Concurrency and Computation

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SummaryWith the development of Internet of Things (IoT), many middleware solutions have been proposed for the integration of physical world with the Web. However, most leading middleware solutions provide only limited resource matching and selection functionality. When significant amounts of resources are available, selecting the appropriate resources becomes challenging and time‐consuming. This paper addresses the growing issues of resource matching and selection in IoT solutions. In this paper, we propose a novel resource model to describe the IoT resources in a multidimensional manner. Based on the resource model, a resource matching algorithm that selects the well‐matched resources by matching the similarity between resources is proposed. Moreover, we constructed a combined resource set and used it and OWLS‐TC to evaluate our work comprehensively. Experimental results show that the proposed resource matching approach is more effective and efficient than existing approaches. Copyright © 2013 John Wiley & Sons, Ltd.

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Fuzzy-based sensor search in the Web of Things

Cited by 44 publications

References 8 publications

Sensor Search Techniques for Sensing as a Service Architecture for the Internet of Things

Sensor Search Techniques for Sensing as a Service Architecture for the Internet of Things

Matching State Estimation Scheme for Content-Based Sensor Search in the Web of Things

A multidimensional resource model for dynamic resource matching in internet of things

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