Geographic Service Discovery for the Internet of Things

Bauer, Martin; Longo, Salvatore

doi:10.1007/978-3-319-13102-3_69

Cited by 7 publications

(6 citation statements)

References 5 publications

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“…The framework consists of 12 layers which focus on heterogeneity of devices, communication networks, data, reasoning and services. The authors of [80] present the idea of Bsensing as a service^, where standard service technologies are used as an interface that represents the IoT resources (i.e. the physical world devices) and provide an access to the functions and capabilities of these resources.…”

Section: Semantic Web Technologiesmentioning

confidence: 99%

Interoperability in Internet of Things: Taxonomies and Open Challenges

2018

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In the last few years, many smart objects found in the physical world are interconnected and communicate through the existing internet infrastructure which creates a global network infrastructure called the Internet of Things (IoT). Research has shown a substantial development of solutions for a wide range of devices and IoT platforms over the past 6-7 years. However, each solution provides its own IoT infrastructure, devices, APIs, and data formats leading to interoperability issues. Such interoperability issues are the consequence of many critical issues such as vendor lock-in, impossibility to develop IoT application exposing cross-platform, and/or cross-domain, difficulty in plugging non-interoperable IoT devices into different IoT platforms, and ultimately prevents the emergence of IoT technology at a large-scale. To enable seamless resource sharing between different IoT vendors, efforts by several academia, industry, and standardization bodies have emerged to help IoT interoperability, i.e., the ability for multiple IoT platforms from different vendors to work together. This paper performs a comprehensive survey on the state-of-the-art solutions for facilitating interoperability between different IoT platforms. Also, the key challenges in this topic is presented.

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Section: Semantic Web Technologiesmentioning

confidence: 99%

Interoperability in Internet of Things: Taxonomies and Open Challenges

2018

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“…The review also highlights the importance of the semantic models (in particular, the lightweight models) in knowledge representation. Besides the ontologies designed for sensors, the IoT Domain ontology provided in IoT-A Reference Model [ 103 ] is becoming more and more popular and finds its application in diverse areas such as business process modelling [ 104 ], dynamic association derivation between ICT and real-world objects [ 5 , 105 ], service discovery [ 41 , 106 ], service selection and ranking [ 107 ] and test case derivation for IoT service lifecycle management [ 108 ].…”

Section: Discussionmentioning

confidence: 99%

“…WoT objects and data have a strong focus on locality; this implies that spatial information is vital for description and search. In some cases, spatial information and other features of objects and data are separated to provide search services [ 23 , 37 , 38 , 39 , 40 , 41 , 42 ]. Spatial information is typically represented by latitude and longitude coordinates (sometimes including altitude as well).…”

Section: Classification Viewpoint—basic Principlesmentioning

confidence: 99%

“…For example, R-tree [ 84 ] is often used for indexing ranges. Approaches implementing R-tree based technique and its variants [ 23 , 37 , 38 , 39 , 41 ] index the Minimum Bounding Rectangles (MBR), which enclose the location range of all the children of a node. The nodes whose range intersects with the location in the query are retrieved.…”

Section: Classification Viewpoint—basic Principlesmentioning

confidence: 99%

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Search Techniques for the Web of Things: A Taxonomy and Survey

Zhou

Wang

et al. 2016

Sensors

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The Web of Things aims to make physical world objects and their data accessible through standard Web technologies to enable intelligent applications and sophisticated data analytics. Due to the amount and heterogeneity of the data, it is challenging to perform data analysis directly; especially when the data is captured from a large number of distributed sources. However, the size and scope of the data can be reduced and narrowed down with search techniques, so that only the most relevant and useful data items are selected according to the application requirements. Search is fundamental to the Web of Things while challenging by nature in this context, e.g., mobility of the objects, opportunistic presence and sensing, continuous data streams with changing spatial and temporal properties, efficient indexing for historical and real time data. The research community has developed numerous techniques and methods to tackle these problems as reported by a large body of literature in the last few years. A comprehensive investigation of the current and past studies is necessary to gain a clear view of the research landscape and to identify promising future directions. This survey reviews the state-of-the-art search methods for the Web of Things, which are classified according to three different viewpoints: basic principles, data/knowledge representation, and contents being searched. Experiences and lessons learned from the existing work and some EU research projects related to Web of Things are discussed, and an outlook to the future research is presented.

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“…A recent survey [ 2 ] of indexing mechanisms for the WoT identifies the R-family of data structures as suitable for spatial access methods. A resolution framework for IoT services based on the R-Tree data structure is presented in [ 14 , 15 ] where the operational areas covered by individual IoT services are mapped into indexing servers. A distributed hierarchical discovery architecture is outlined with catalogue servers storing the service areas of geographic indexing servers.…”

Section: Related Workmentioning

confidence: 99%

Spatial Indexing for Data Searching in Mobile Sensing Environments

Zhou

Wang

et al. 2017

Sensors

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Data searching and retrieval is one of the fundamental functionalities in many Web of Things applications, which need to collect, process and analyze huge amounts of sensor stream data. The problem in fact has been well studied for data generated by sensors that are installed at fixed locations; however, challenges emerge along with the popularity of opportunistic sensing applications in which mobile sensors keep reporting observation and measurement data at variable intervals and changing geographical locations. To address these challenges, we develop the Geohash-Grid Tree, a spatial indexing technique specially designed for searching data integrated from heterogeneous sources in a mobile sensing environment. Results of the experiments on a real-world dataset collected from the SmartSantander smart city testbed show that the index structure allows efficient search based on spatial distance, range and time windows in a large time series database.

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Geographic Service Discovery for the Internet of Things

Cited by 7 publications

References 5 publications

Interoperability in Internet of Things: Taxonomies and Open Challenges

Interoperability in Internet of Things: Taxonomies and Open Challenges

Search Techniques for the Web of Things: A Taxonomy and Survey

Spatial Indexing for Data Searching in Mobile Sensing Environments

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