Abstract: An example network information aggregator is disclosed. The network information aggregator includes a network interface, a memory, and processing circuitry. The processing circuitry is to receive a machine learning model trained using initialization data from a model creation device. The processing circuitry is also to generate a device steering rule to steer a client device from a first radio to a second radio using the machine learning model. The processing circuitry is also to send a steer command to the first radio.

Abstract: Some of examples disclosed herein relate to classifying access points (APs). Some of the example may enable automatically classifying a set of APs based on at least one AP attribute of each AP in the set of APs, determining, based on the automatic classification, that a subset of APs in the set of APs are classified into a same class; and automatically determining a recommended configuration setting for the subset of APs.

Abstract: Example implementations relate to determining when to adjust a power state of access points. A non-transitory computer readable medium may store instructions executable by a processing resource to: determine a subset of a group of access points (APs) that is to provide, to a client device, at least a performance threshold during a particular time interval, wherein an AP of the subset of the group of APs serves the client device; and determine when to adjust a power state of a remaining AP of the group of APs based on: a first degree of performance being provided by a first set of radios of the subset of the group of APs; and a second degree of performance to be provided by the remaining AP of the group of APs if the group of client devices is provided a network connectivity via a second radio of the remaining AP of the group of APs.

Abstract: Example implementations relate to predicting power states of access points (APs). A non-transitory computer readable medium may store instructions executable by a processing resource to: in response to a first client device being associated with a first access point (AP) of a group of APs, determine a first degree of performance a second AP of the group is to provide if the first client device is associated with the second AP; and predict, based on the determined degree of performance, a second degree of performance the group of AP is to provide to a second client device if the second client device is associated with the group of APs during a first time interval at a location.

Abstract: Example implementations relate to adjusting power states of access points based on a power model. A non-transitory computer readable medium may store instructions executable by a processing resource to: in response to a client device being associated with an access point (AP) of a group of APs, determine: a first degree of performance being provided to the client device via a first radio of the AP of a group of APs; and a second degree of performance to be provided to the client device via a second radio of the group of APs, if the client device is provided a network connectivity via the second radio; determine, based on the first degree of performance and the second degree of performance, a subset of the group of APs whose power state is adjustable to a different power state; and adjust a power state of the subset of the group of APs.

Abstract: Some of examples disclosed herein relate to classifying access points (APs). Some of the example may enable automatically classifying a set of APs based on at least one AP attribute of each AP in the set of APs, determining, based on the automatic classification, that a subset of APs in the set of APs are classified into a same class; and automatically determining a recommended configuration setting for the subset of APs.

Abstract: In some examples, a system generates event records representing anomalous events associated with respective devices in a network, each respective anomalous event of the anomalous events being identified as anomalous for a respective context of a device of the devices. The system clusters the event records to produce a plurality of clusters of the event records. The system outputs the plurality of clusters for application of a remediation of an issue in the network.

Abstract: A method for identifying a client device in a network, and a first radio in the network that is coupled with the client device is provided. The method includes determining one or more sequences of roaming events for multiple client devices in the network, evaluating a performance metric for a roaming event and evaluating an interaction between the client device and one or more radios involved in the roaming events for the plurality of client devices. The method also includes selecting a second radio in the network based at least in part on (1) the one or more sequences of roaming events, (2) the performance metric, and (3) the interaction between the client device and the one or more radios, and recommending switching the client device from the first radio to the second radio. A system and a predictive tool to perform the above method are also provided.

Abstract: An example network information aggregator is disclosed. The network information aggregator includes a network interface, a memory, and processing circuitry. The processing circuitry is to receive a machine learning model trained using initialization data from a model creation device. The processing circuitry is also to generate a device steering rule to steer a client device from a first radio to a second radio using the machine learning model. The processing circuitry is also to send a steer command to the first radio.

Abstract: In some examples, a system provides a machine learning model trained based on historical data including network-observed parameters for devices associated with a network. The system inputs values of the network-observed parameters for a first device into the machine learning model, the input values being from a plurality of devices in the network. The machine learning model produces information relating to a location or mobility of the first device.

Abstract: In some examples, a system generates event records representing anomalous events associated with respective devices in a network, each respective anomalous event of the anomalous events being identified as anomalous for a respective context of a device of the devices. The system clusters the event records to produce a plurality of clusters of the event records. The system outputs the plurality of clusters for application of a remediation of an issue in the network.

Abstract: Systems, methods and apparatuses for estimating qualities of links between nodes of a wireless network are disclosed. One method includes obtaining, by a service engine, a plurality of link quality signatures, wherein each of the plurality of link quality signatures includes link qualities between a network node and a plurality of network sensors, and estimating, by the service engine, link qualities between a plurality of network nodes based on the plurality of link quality signatures and an estimation model.

Abstract: Systems, methods and apparatuses estimating a capacity of a least one stream of a wireless link, are disclosed. One method includes obtaining capabilities of a transmitter and of a receiver of the wireless link, collecting information of a wireless transmission, wherein the wireless transmission comprises at least a portion of a transmission signal with electromagnetic energy above a certain threshold within a frequency band of the wireless link, estimating transmission opportunities available for the transmitter to transmit wireless communications to the receiver based on the obtained capabilities and the collected information, estimating an achievable transmission rate by the transmitter based on the determined capabilities and the collected information, and estimating the capacity of the at least one wireless stream of the wireless link based on the estimated transmission opportunities and the estimated achievable transmission rate.

Abstract: Systems, methods and apparatuses for sensing performance of a wireless network are disclosed. One method includes sensing conditions of a wireless network, including receiving, by a network sensor, a packet, determining, by the network sensor, a time stamp for the packet, and determining, by the network sensor, whether to transmit at least a representation of the packet to a service engine of the wireless network based on at least the time stamp of the packet.

Abstract: Some of examples disclosed herein relate to classifying access points (APs). Some of the example may enable automatically classifying a set of APs based on at least one AP attribute of each AP in the set of APs, determining, based on the automatic classification, that a subset of APs in the set of APs are classified into a same class; and automatically determining a recommended configuration setting for the subset of APs.

Abstract: Example implementations relate to capacity comparisons. For instance, an example network device may include a processing resource and a memory resource storing machine readable instructions. The instructions may cause the processing resource to receive, from a device that performs a network application, a first stream of a wireless link between the device and a wireless network and a second stream of the wireless network that competes with the first stream, estimate a capacity of the stream that corresponds to a potential throughput of the stream over the wireless network, compare the estimated capacity with a demanding capacity of the network application whose associated data are transmittable by the stream, and determine a degree in which the demanding capacity of the network application is met by the estimated capacity.

Abstract: Systems, methods and apparatuses for a motion-controlled device that is operable to support deployment or operation of a wireless network, are disclosed. one apparatus (motion-controlled device) includes a receiver, a motion controller, and a controller. The receiver is operative to sense wireless communication signals of a wireless network. The motion controller is operative to control a positioning of the motion-controlled device in a plurality of dimensions. The controller is operative to at least facilitate analysis of the sensed wireless communication signals, and provide a control signal to the motion controller, wherein the control signal controls the positioning of the motion-controlled device based at least in part on the sensed wireless communication signals.

Abstract: Systems, methods and apparatuses for a motion-controlled device that is operable to support deployment or operation of a wireless network, are disclosed. One apparatus (motion-controlled device) includes a receiver, a motion controller, and a controller. The receiver is operative to sense wireless communication signals of a wireless network. The motion controller is operative to control a positioning of the motion-controlled device in a plurality of dimensions. The controller is operative to at least facilitate analysis of the sensed wireless communication signals, and provide a control signal to the motion controller, wherein the control signal controls the positioning of the motion-controlled device based at least in part on the sensed wireless communication signals.

Abstract: Systems, methods and apparatuses for mitigating a wireless networking problem of a wireless network are disclosed. One method includes determining, by an analytics engine, a problem associated with the wireless network, wherein the analytics engine is operative as a central intelligence for detection, analyzing, classifying, root-causing, or controlling the wireless network. The method further includes receiving, by the analytics engine, a collected user input and state information, mapping, by at least the analytics engine, a problem signature of the user input and the state information to at least one of a number of possible problem network conditions, determining, by the analytics engine, instructions for alleviating the problem based on the mapping of the problem signature, and providing, by the analytics engine, the instructions.

Abstract: Systems, methods and apparatuses for mitigating a wireless networking problem of a wireless network are disclosed. One method includes determining, by an analytics engine, a problem associated with the wireless network, wherein the analytics engine is operative as a central intelligence for detection, analyzing, classifying, root-causing, or controlling the wireless network. The method further includes receiving, by the analytics engine, a collected user input and state information, mapping, by at least the analytics engine, a problem signature of the user input and the state information to at least one of a number of possible problem network conditions, determining, by the analytics engine, instructions for alleviating the problem based on the mapping of the problem signature, and providing, by the analytics engine, the instructions.