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Contribution of the Web of Things and of the Opportunistic Computing to the Smart Agriculture: A Practical Experiment

IRISA-UBS, Université Bretagne Sud, BP 573, 56017 Vannes CEDEX, France
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Future Internet 2019, 11(2), 33; https://doi.org/10.3390/fi11020033
Received: 31 December 2018 / Revised: 28 January 2019 / Accepted: 29 January 2019 / Published: 1 February 2019
(This article belongs to the Special Issue WSN and IoT in Smart Agriculture)
With the emergence of the Internet of Things, environmental sensing has been gaining interest, promising to improve agricultural practices by facilitating decision-making based on gathered environmental data (i.e., weather forecasting, crop monitoring, and soil moisture sensing). Environmental sensing, and by extension what is referred to as precision or smart agriculture, pose new challenges, especially regarding the collection of environmental data in the presence of connectivity disruptions, their gathering, and their exploitation by end-users or by systems that must perform actions according to the values of those collected data. In this paper, we present a middleware platform for the Internet of Things that implements disruption tolerant opportunistic networking and computing techniques, and that makes it possible to expose and manage physical objects through Web-based protocols, standards and technologies, thus providing interoperability between objects and creating a Web of Things (WoT). This WoT-based opportunistic computing approach is backed up by a practical experiment whose outcomes are presented in this article. View Full-Text
Keywords: Web of Things; intermittent connectivity; opportunistic computing; opportunistic sensing; precision agriculture; smart agriculture Web of Things; intermittent connectivity; opportunistic computing; opportunistic sensing; precision agriculture; smart agriculture
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Touseau, L.; Le Sommer, N. Contribution of the Web of Things and of the Opportunistic Computing to the Smart Agriculture: A Practical Experiment. Future Internet 2019, 11, 33.

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