Abstract: A method for classifying devices may include determining that a device is located at a location for more than a threshold amount of time and identifying that the device is a moving candidate based on a percentage of times a plurality of signal strength values for the device fall inside a baseline interval. The method may also include determining a most frequent pattern set of signal strength values for the device, classifying the device as fixed if the most frequent pattern set of signal strength values for the device includes at least one overlapping interval, and classifying the device as moving if the most frequent pattern set of signal strength values for the device includes at least one non-overlapping interval.

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: An example system in accordance with an aspect of the present disclosure includes a controller to store Received Signal Strength Indicator (RSSI) information for an unassociated client detected by an access point (AP) of a wireless network. The controller is to identify that the unassociated client has associated with the wireless network, and identify the AP as a doorway AP in response to the client associating to the AP.

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 implementations relate to neighboring network devices. For example, a non-transitory computer readable medium storing instructions executable by a processing resource can cause a computer to determine a characteristic affecting a subset of a plurality of network devices. The instructions can cause the processing resource to determine that at least two of the subset of the plurality of network devices that are affected by the characteristic are neighbors. The instructions can cause the processing resource to indicate a region to be associated with each network device of the subset based on the determination that at least two of the subset of the plurality of network devices are neighbors.

Abstract: A method for classifying devices may include determining that a device is located at a location for more than a threshold amount of time and identifying that the device is a moving candidate based on a percentage of times a plurality of signal strength values for the device fall inside a baseline interval. The method may also include determining a most frequent pattern set of signal strength values for the device, classifying the device as fixed if the most frequent pattern set of signal strength values for the device includes at least one overlapping interval, and classifying the device as moving if the most frequent pattern set of signal strength values for the device includes at least one non-overlapping interval.

Abstract: Access points in different areas of a site detect client devices present in their areas. To be detected, the clients need only be enabled for a wireless technology (for example, Wi-Fi); they need not activate location services or any specific application. A network management interface may control the access points to monitor the number of clients in different areas at different times and store the results to a database. Algorithms may operate on the stored data to discover predictable cycles of client presence such as working and nonworking days or peak and nonpeak hours.

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 client boundary location. For example, a non-transitory computer readable medium storing instructions executable by a processing resource to cause a computer to estimate a location of a client within a first boundary; determine whether there is a pathway between the first boundary and a second boundary for the client to move; and indicate whether the client is in the first boundary or the second boundary based on the determination of the pathway and the estimated location of the client.

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: Examples disclosed herein relate to determining a scale associated with received signal strength indicators (RSSIs). In one example, a computing device receives access point (AP) RSSIs. The RSSIs are determined at APs. The APs are associated with coordinate information. In one example, records are maintained for AP reference pairs. Each AP RSSI can be determined at one AP with reference to another AP. In one example, the computing device determines a scale associated with each record.

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 implementations relate to neighboring network devices. For example, a non-transitory computer readable medium storing instructions executable by a processing resource can cause a computer to determine a characteristic affecting a subset of a plurality of network devices. The instructions can cause the processing resource to determine that at least two of the subset of the plurality of network devices that are affected by the characteristic are neighbors. The instructions can cause the processing resource to indicate a region to be associated with each network device of the subset based on the determination that at least two of the subset of the plurality of network devices are neighbors.

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 neighboring network devices. For example, a non-transitory computer readable medium storing instructions executable by a processing resource can cause a computer to determine a characteristic affecting a subset of a plurality of network devices. The instructions can cause the processing resource to determine that at least two of the subset of the plurality of network devices that are affected by the characteristic are neighbors. The instructions can cause the processing resource to indicate a region to be associated with each network device of the subset based on the determination that at least two of the subset of the plurality of network devices are neighbors.

Abstract: Examples disclosed herein relate to determining a scale associated with received signal strength indicators (RSSIs). In one example, a computing device receives access point (AP) RSSIs. The RSSIs are determined at APs. The APs are associated with coordinate information. In one example, records are maintained for AP reference pairs. Each AP RSSI can be determined at one AP with reference to another AP. In one example, the computing device determines a scale associated with each record.

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 client boundary location. For example, a non-transitory computer readable medium storing instructions executable by a processing resource to cause a computer to estimate a location of a client within a first boundary; determine whether there is a pathway between the first boundary and a second boundary for the client to move; and indicate whether the client is in the first boundary or the second boundary based on the determination of the pathway and the estimated location of the client.