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Book ChapterDOI

Semantic Technologies for IoT

TL;DR: In this article, the authors introduce the key concepts in the semantic technology domain and key business applications in Internet of Things (IoT), and cover the emerging ontologies such as semantic sensor networks (SSN) and IoT and the impact they will have on IoT smart solutions and future collaborative interoperable applications.
Abstract: This chapter introduces the key concepts in the semantic technology domain and key business applications in Internet of Things (IoT). It covers the emerging ontologies such as semantic sensor networks (SSN) and IoT and the impact they will have on IoT smart solutions and future collaborative interoperable applications. Ontologies and alternative semantic technologies are often key enabling technologies for sensor networks, as they facilitate semantic interoperability and integration, reasoning, classification, different kinds of assurance, and automation not addressed within the OGC standards. Semantic sensor networks work on the interoperability of physical sensor networks to ease the sensor discovery. The chapter examines evolving standards and consortiums that were instigated to advance standardization work and interoperability of IoT. It presents final remarks using a case study and guidelines to encourage readers to innovate and build differentiated solution that contributes toward refining the quality of lives and smart business practices in the domain of IoT.
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Proceedings ArticleDOI
03 Sep 2004
TL;DR: A novel, two-phase solution to the wireless sensor network adaptivity problem, where nodes in the network, organized as clusters, execute an efficient algorithm to dynamically calibrate sensed data.
Abstract: A wireless sensor network deployed in an area of interest is affected by variations in environmental conditions associated with that area. It must adapt to these variations in order to continue functioning as desired by the user. We present a novel, two-phase solution to the wireless sensor network adaptivity problem. In the first phase, nodes in the network, organized as clusters, execute an efficient algorithm to dynamically calibrate sensed data. Each node provides its current energy level and the state of each on-board sensor to a cluster-head. In the second phase, each cluster-head executes an efficient, ontology-driven algorithm to determine the future state of the network under existing conditions, based on information received from each sensor node. We describe an example application scenario to show how our two-phase solution can be employed to enable a real-world wireless sensor network to adapt itself to variations in environmental conditions.

78 citations