Abstract
Context- and self- awareness are two terms that have been living with us for many years. In principle, both state a similar meaning even though the literature points out a very different path. One is inspired by location-related mechanisms in mobile environments, whereas the other is inspired by biology. In the area of the Internet of Things, the term context-awareness has seen a higher adoption in the field of Cloud Computing, while the term self-awareness is more widely used in the area of Wireless Sensor Networks. This paper evaluates the entire IoT Cloud-to-Thing Continuum in an attempt to reconcile both terms. We contextualize and discuss the literature around context and self-awareness, and we propose a conceptual architecture that handles both concepts, with the aim of having a better understanding of how to develop a software environment that integrates both concepts. To show the real-life applicability of our proposed architecture, it is introduced in a realistic setting such as wildfire monitoring, including a conceptual overview of how the proposed architecture could be implemented in this domain. Additionally, our evaluation of a river flooding scenario concluded that the proposed architecture significantly reduced flood detection delay by over 47% compared to the naive method and over 20% compared to standalone self-awareness and context-awareness methods.
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Notes
- 1.
Illustrated by dashed lines in Fig. 1 to emphasize the different interactions’ nature.
- 2.
River Level and Flows dataset by Alberta Environment and Parks [4].
- 3.
Node datasheet: https://www.worldsensing.com/product/tilt90-x-2/.
- 4.
The water level prediction model–lightweight linear regression algorithm–employed in the sensor node has some energy overhead, but it is expected to have minimal impact on the sensor node’s overall energy consumption. Hence, this has not been factored into the simulation nor Table 1.
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Acknowledgments
This work is partially funded by the Industrial Doctorates DI-2019 from Generalitat de Catalunya (2019 DI 075 to David Arnaiz and 2019 DI 001 to Marc Vila). The SUDOQU project (PID2021-127181OB-I00) from MCIN/AEI. FEDER “Una manera de hacer Europa”. And the 2021-SGR-01252 project from Generalitat de Catalunya. Thanks to Xavier Vilajosana for his advice in this work. With the support of inLab FIB at UPC.
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Arnaiz, D., Vila, M., Alarcón, E., Moll, F., Sancho, MR., Teniente, E. (2024). Relating Context and Self Awareness in the Internet of Things. In: Sellami, M., Vidal, ME., van Dongen, B., Gaaloul, W., Panetto, H. (eds) Cooperative Information Systems. CoopIS 2023. Lecture Notes in Computer Science, vol 14353. Springer, Cham. https://doi.org/10.1007/978-3-031-46846-9_21
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