Context-Aware Sensor Search, Selection and Ranking Model for Internet of Things Middleware

@article{Perera2013ContextAwareSS,
  title={Context-Aware Sensor Search, Selection and Ranking Model for Internet of Things Middleware},
  author={Charith Perera and Arkady B. Zaslavsky and Peter Christen and Michael Compton and Dimitrios Georgakopoulos},
  journal={2013 IEEE 14th International Conference on Mobile Data Management},
  year={2013},
  volume={1},
  pages={314-322}
}
As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a substantial acceleration of the growth rate in the future. It is also evident that the increasing number of IoT middleware solutions are developed in both research and commercial environments. However, sensor search and selection remain a critical… 
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References

SHOWING 1-10 OF 36 REFERENCES
Sensor ranking: A primitive for efficient content-based sensor search
TLDR
This paper introduces a primitive called sensor ranking to enable efficient search for sensors that have a certain output state at the time of the query and shows that sensor ranking can significantly improve the efficiency of content-based sensor search.
CA4IOT: Context Awareness for Internet of Things
TLDR
The Context Awareness for Internet of Things (CA4IOT) architecture is proposed to help users by automating the task of selecting the sensors according to the problems/tasks at hand, and focuses on automated configuration of filtering, fusion and reasoning mechanisms that can be applied to the collected sensor data streams using selected sensors.
A real-time search engine for the Web of Things
TLDR
It is shown how the existing Web infrastructure can be leveraged to support publishing of sensor and entity data and presented a real-time search engine for the Web of Things.
Fuzzy-based sensor search in the Web of Things
TLDR
This work proposes sensor similarity search, where given a sensor, other sensors on the WoT are found that produced similar output in the past, and finds that this approach results in a high accuracy.
The Linked Sensor Middleware — Connecting the real world and the Semantic Web
TLDR
The Linked Stream Middleware (LSM), a platform that brings together the live real world sensed data and the Semantic Web, is described and the benefits of the platform are demonstrated by showcasing its interface.
Searching in a web-based infrastructure for smart things
TLDR
The proposed infrastructure treats the location of a smart thing as its main property and is therefore structured hierarchically according to logical place identifiers and features an advanced caching mechanism that greatly reduces the response time and number of exchanged messages.
A Survey of the Semantic Specification of Sensors
TLDR
The state of the art for the semantic specification of sensors, one of the fundamental technologies in the semantic sensor network vision, is reviewed.
Internet of Things
TLDR
The fields of application for IoT technologies are as numerous as they are diverse, as IoT solutions are increasingly extending to virtually all areas of everyday.
The Berlin SPARQL Benchmark
TLDR
The Berlin SPARQL Benchmark (BSBM) is introduced, built around an e-commerce use case in which a set of products is offered by different vendors and consumers have posted reviews about products, and emulates the search and navigation pattern of a consumer looking for a product.
The SID Creator: A Visual Approach for Integrating Sensors with the Sensor Web
This paper describes the Sensor Interface Descriptor (SID) model and focuses on presenting and evaluating the SID creator, a visual approach to create instances of the SID model. Those SID instances
...
...