Abstract: Aspects of categorizing MAC address randomization include obtaining probe data for a plurality of time intervals. A likelihood measure of a specified OS type and version belonging to each of a plurality of MAC address randomization categories is determined and the specified OS type and version is categorized based on the likelihood measure.

Abstract: Example method includes: receiving, by a network device, a plurality of signal strength values associated with a particular timestamp corresponding to a particular client device from a plurality of access points in a wireless network at a site; determining a mobility pattern based on the plurality of signal strength values associated with the particular timestamp corresponding to the particular client device; comparing the mobility pattern associated with the particular timestamp corresponding to the particular client device with mobility patterns associated with other timestamps or corresponding to other client devices to identify a set of frequent mobility patterns; determining whether an unassociated client device exhibits a threshold number of frequent mobility patterns for a threshold amount of time; and determining that the unassociated client device presents in the site in response to the unassociated client device exhibiting the threshold number of the frequent mobility patterns for the threshold amou

Abstract: Example method includes: receiving, by a network device, a plurality of signal strength values associated with a particular timestamp corresponding to a particular client device from a plurality of access points in a wireless network at a site; determining a mobility pattern based on the plurality of signal strength values associated with the particular timestamp corresponding to the particular client device; comparing the mobility pattern associated with the particular timestamp corresponding to the particular client device with mobility patterns associated with other timestamps or corresponding to other client devices to identify a set of frequent mobility patterns; determining whether an unassociated client device exhibits a threshold number of frequent mobility patterns for a threshold amount of time; and determining that the unassociated client device presents in the site in response to the unassociated client device exhibiting the threshold number of the frequent mobility patterns for the threshold amou

Abstract: Example implementations relate to network deployment of devices. For example, a non-transitory computer readable medium storing instructions executable by a processing resource can determine a plurality of deployment thresholds of a plurality of devices, wherein the plurality of deployment thresholds are associated with a type of the plurality of devices. The instructions can cause the processing resource to monitor deployment data associated with the plurality of devices to identify a device with a deployment outlier. The device with the deployment outlier is a device with deployment data that is outside a deployment threshold of the device. The instructions can cause the processing resource to adjust the deployment threshold of the device based on the monitoring.

Abstract: Example method includes: receiving, by a network device, a plurality of signal strength values associated with a particular timestamp corresponding to a particular client device from a plurality of access points in a wireless network at a site; determining a mobility pattern based on the plurality of signal strength values associated with the particular timestamp corresponding to the particular client device; comparing the mobility pattern associated with the particular timestamp corresponding to the particular client device with mobility patterns associated with other timestamps or corresponding to other client devices to identify a set of frequent mobility patterns; determining whether an unassociated client device exhibits a threshold number of frequent mobility patterns for a threshold amount of time; and determining that the unassociated client device presents in the site in response to the unassociated client device exhibiting the threshold number of the frequent mobility patterns for the threshold amou

Abstract: In some examples, a non-transitory computer-readable medium may contain instructions executable by a processing resource to collect Wi-Fi data points. The Wi-Fi data points may correspond to a plurality of users of an enterprise for a particular number of days. In some examples, the Wi-Fi data points may indicate a time period during each of the particular number of days when each of the plurality of users was connected to a Wi-Fi network. Instructions may further be executable to determine that a subset of the Wi-Fi data points represent outliers and remove the subset of Wi-Fi data points to create a data set. The data set may comprise non-outlier Wi-Fi data points. Instructions may further be executable to determine a Wi-Fi Adoption Index (WAI) for each of the plurality of users based on the data set.

Abstract: Example implementations relate to network deployment of devices. For example, a non-transitory computer readable medium storing instructions executable by a processing resource can determine a plurality of deployment thresholds of a plurality of devices, wherein the plurality of deployment thresholds are associated with a type of the plurality of devices. The instructions can cause the processing resource to monitor deployment data associated with the plurality of devices to identify a device with a deployment outlier. The device with the deployment outlier is a device with deployment data that is outside a deployment threshold of the device. The instructions can cause the processing resource to adjust the deployment threshold of the device based on the monitoring.

Abstract: In some examples, a non-transitory computer-readable medium may contain instructions executable by a processing resource to collect Wi-Fi data points. The Wi-Fi data points may correspond to a plurality of users of an enterprise for a particular number of days. In some examples, the Wi-Fi data points may indicate a time period during each of the particular number of days when each of the plurality of users was connected to a Wi-Fi network. Instructions may further be executable to determine that a subset of the Wi-Fi data points represent outliers and remove the subset of Wi-Fi data points to create a data set. The data set may comprise non-outlier Wi-Fi data points. Instructions may further be executable to determine a Wi-Fi Adoption Index (WAI) for each of the plurality of users based on the data set.

Abstract: Example implementations relate to network deployment of devices. For example, a non-transitory computer readable medium storing instructions executable by a processing resource can determine a plurality of deployment thresholds of a plurality of devices, wherein the plurality of deployment thresholds are associated with a type of the plurality of devices. The instructions can cause the processing resource to monitor deployment data associated with the plurality of devices to identify a device with a deployment outlier. The device with the deployment outlier is a device with deployment data that is outside a deployment threshold of the device. The instructions can cause the processing resource to adjust the deployment threshold of the device based on the monitoring.

Abstract: In some examples, a non-transitory computer-readable medium may contain instructions executable by a processing resource to collect Wi-Fi data points. The Wi-Fi data points may correspond to a plurality of users of an enterprise for a particular number of days. In some examples, the Wi-Fi data points may indicate a time period during each of the particular number of days when each of the plurality of users was connected to a Wi-Fi network. Instructions may further be executable to determine that a subset of the Wi-Fi data points represent outliers and remove the subset of Wi-Fi data points to create a data set. The data set may comprise non-outlier Wi-Fi data points. Instructions may further be executable to determine a Wi-Fi Adoption Index (WAI) for each of the plurality of users based on the data set.