| 1. |
- Koponen, Teemu, et al.
(författare)
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Architecting for Innovation
- 2011
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Ingår i: Computer communication review. - : Association for Computing Machinery (ACM). - 0146-4833 .- 1943-5819. ; 41:3, s. 24-36
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Tidskriftsartikel (refereegranskat)abstract
- We argue that the biggest problem with the current Internet architecture is not a particular functional deficiency, but its inability to accommodate innovation. To address this problem we propose a minimal architectural "framework" in which comprehensive architectures can reside. The proposed Framework for Internet Innovation (FII) - which is derived from the simple observation that network interfaces should be extensible and abstract - allows for a diversity of architectures to coexist, communicate, and evolve. We demonstrate FII's ability to accommodate diversity and evolution with a detailed examination of how information flows through the architecture and with a skeleton implementation of the relevant interfaces.
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| 2. |
- Wahlström, Johan, et al.
(författare)
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Smartphone Placement Within Vehicles
- 2020
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Ingår i: IEEE transactions on intelligent transportation systems (Print). - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1524-9050 .- 1558-0016. ; 21:2, s. 669-679
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Tidskriftsartikel (refereegranskat)abstract
- Smartphone-based driver monitoring is quickly gaining ground as a feasible alternative to competing in-vehicle and aftermarket solutions. Currently the main challenges for data analysts studying smartphone-based driving data stem from the mobility of the smartphone. In this paper, we use kernel-based k-means clustering to infer the placement of smartphones within vehicles. The trip segments are mapped into fifteen different placement clusters. As a part of the presented framework, we discuss practical considerations concerning e.g., trip segmentation, cluster initialization, and parameter selection. The proposed method is evaluated on more than 10 000 kilometers of driving data collected from approximately 200 drivers. To validate the interpretation of the clusters, we compare the data associated with different clusters and relate the results to real-world knowledge of driving behavior. The clusters associated with the label "Held by hand" are shown to display high gyroscope variances, low maximum speeds, low correlations between the measurements from smartphone-embedded and vehicle-fixed accelerometers, and short segment durations.
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