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The networks can consist of high- and low-resource nodes ranging from generic computers to microchips, and the supported network classes range from body area networks to the Internet including any kind of sensor and ambient network.
Finally, note that the ambient network is a superset of the intimate network, as we allow for short range interactions as well in the former.
Based on the above models, Murata[15] developed and extended ambient network management to facilitate future human life closely related to environmental adaptability.
As the top figures in Figure 6 show, the intimate network outperforms the ambient network in predicting links in both digital communication networks (phone call and Facebook), although the difference is more pronounced in the phone call network.
The fact that these sets of links consist of the same pairs of users, suggests that even though the structure of the ambient network is blurred by spurious encounters, after removing those links that are not present in other channels, it is still possible to recover the strong links represented in the intimate network.
Additionally, due to environmental noise, the mapping from bluetooth strength to distance is not perfect and a small fraction of links from the ambient network will erroneously appear in the intimate network (see Methods for the details on the construction of these interactions).
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However, it has been shown in Ref [32] that using a fixed threshold results in significantly different network structures, capturing notions of intimate and ambient networks.
When compared to the communication networks, the distributions (P_{mathrm{int.}}(theta)) and (P_{mathrm{amb.}}(theta)) characterize how similar the intimate and ambient networks are to the call and Facebook activity networks.
Also, its proposed vertical handover system might be considered as a reference model for other models in any future framework such as the ambient networks [21] and Y-Comm [22].
Even in Vancouver, an area with a dense ambient monitoring network, exposure assessment based on regulatory monitoring network data is more suited to characterizing temporal variability.
Overall, results from the traffic-based land use regression models agree with analyses based on exposures estimated from the ambient monitoring network.
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