University of Utah Research Foundation

Battelle Energy Alliance, LLC

Entry Point, LLC

At&t Intellectual Property I, L.p.

Sneha Kumar Kasera

Christopher D Becker

Jacobus Erasmus Van Der Merwe

Kurt Warren Derr

Neal K Patwari

Samuel Ramirez

Aniqua Z Baset

Suman Jana

Joe H Novak

Jessica Erin Croft

Vijay Gopalakrishnan

Seungjoon Lee

Jeffrey Robert Peterson

Arijit Banerjee

Robert Preston Reikenberg Ricci

Matt Aaron Strum

Ryan Stutsman

Carl Robert Peterson

Disclosed embodiments relate to ensemble wireless signal classification and systems and devices the incorporate the same. Some embodiments of ensemble wireless signal classification may include energy-based classification processes and machine learning-based classification processes. In some embodiments, incremental machine learning techniques may be incorporated to add new machine learning-based classifiers to a system or update existing machine learning-based classifiers.

Spectrum monitoring and analysis, and related methods, systems, and devices

Processing flows and related systems and methods are disclosed. A computing system includes one or more data interfaces, one or more other components, and a controller. The one or more data interfaces are configured to provide an interface to a data source. The one or more other components include one or more controller plugins, one or more processing nodes, or both the one or more controller plugins and the one or more processing nodes. The controller is configured to manage interactions between the one or more data interfaces and the one or more other components and enable a user to chain together the one or more data interfaces and the one or more other components according to one or more flows. The one or more controller plugins are configured to provide results of the one or more flows to one of a user interface and a system interface.

Processing flows and related systems and methods are disclosed

Improved techniques for dynamically responding to a fluctuating workload. Resources are reactively scaled for memory-intensive applications and automatically adapted to in response to workload changes without requiring pre-specified thresholds. A miss ratio curve (MRC) is generated for an application based on application runtime statistics. This MRC is then modeled as a hyperbola. An area on the hyperbola is identified as satisfying a flatten threshold. A resource allocation threshold is then established based on the identified area. This resource allocation threshold indicates how many resources are to be provisioned for the application. The resources are scaled using a resource scaling policy that is based on the resource allocation threshold.

Auto-scaling cloud-based memory-intensive applications

Wireless signal classifiers and systems that incorporate the same may include an energy-based detector configured to analyze an entire set of measurements and generate a first signal classification result, a cyclostationary-based detector configured to analyze less than the entire set of measurements and generate a second signal classification result; and a classification merger configured to merge the first signal classification result and the second signal classification result. Ensemble wireless signal classification and systems and devices the incorporate the same are disclosed. Some ensemble wireless signal classification may include energy-based classification processes and machine learning-based classification processes. In some embodiments, incremental machine learning techniques may be incorporated to add new machine learning-based classifiers to a system or update existing machine learning-based classifiers.

Wireless signal monitoring and analysis, and related methods, systems, and devices

Authentication of devices to receive services provided by service providers over communications networks such as open access networks, wherein the provider of the network need not have access to secret information shared between the devices and the service providers. A request is received from a device to receive services from a service provider and is forwarded to the service provider. A challenge and an expected response is received from the service provider and the challenge is forwarded to the device. A response to the challenge is received from the device the device is authenticated to receive services from the service provider if the response received from the device matches the expected response provided by the service provider.

Open access network secure authentication systems and methods

When traffic arrives from the network for an idle mobile device, the network executes device activation procedures to awaken the device, which can result in a significant amount of signaling to complete. Adaptive device activation mechanisms are provided that adapt to network conditions and potentially to machine-to-machine device application requests to realize scalable device activation without increasing the resources used for this purpose and without negatively impacting existing human-to-human or human-to-machine traffic.

Adaptive group paging for a communication network

Methods, systems, and devices for automatically and dynamically managing and coordinating network resources used by services between users over a data network having a network infrastructure. A network resources coordinator can include a knowledge store, rules library, rules engine, and orchestrator. The knowledge store can have a plurality of layered abstraction stores having stored thereon facts representative of a present state of the network. The rules library can include production rules representative of knowledge of the network and of users. The orchestrator can receive service requests from users and use the rules engine in conjunction with the production rules of the rules library and facts of the knowledge store to: determine network resources required and available for requested services, dynamically make automated decisions with respect to the network and users, detect and react to changes in the network in an automated fashion, and enforce constraints of the network.

Programmable data network management and operation

A method for calculating a drop probability can comprise determining, based on measurements within a network architecture of the computer system, a current packet delay and current link utilization within network architecture. The method can also comprise predicting a change in the packet delay within the network architecture at a predefined time interval in the future. Additionally, the method can comprise predicting a change in the link utilization within the network architecture at a predefined time interval in the future. Further, the method can comprise computing a drop probability that will ensure that the ratio of the predicted change in the packet delay over the predicted change in the link utilization approximates an predetermined ideal.

Auto-tuning active queue management

Identifying valid wireless access points based on clock skews. A computing device receives plurality of frames, each including a corresponding timestamp, from a wireless access point. The computing device calculates transmit and receive offsets based on transmit and receive times of the plurality of frames, and also calculates differences between the transmit and receive offsets. The computing device also calculates a clock skew of a clock at the wireless access point based on a slope of the differences. The client computing device determines whether the wireless access point is an authorized wireless access point or an unauthorized wireless access point based on comparing the calculated clock skew with one or more known baseline clock skews of one or more authorized wireless access points.

Method and system for detecting unauthorized wireless access points using clock skews

The ability of a receiver to determine when a transmitter has changed location is important for energy conservation in wireless sensor networks, for physical security of radio tagged objects, and for wireless network security in detection of replication attacks. In embodiments of the present invention, a measured temporal link signature is determined to uniquely identify the link between a transmitter and a receiver. The temporal link signature is an impulse response of a transmitted signal from the transmitter over a channel between the transmitter and a receiver over a channel. When the transmitter changes location, or if an attacker at a different location assumes the identity of the transmitter, the proposed link distinction methodology reliably detects the change in the physical channel. This detection can be performed at a single receiver or collaboratively by multiple receivers.

Robust location distinction using temporal link signatures

A new methodology to exchange a random secret key between two parties. The diverse physical characteristics of the wireless medium and device mobility are exploited for secure key exchange. Unique physical characteristics of wireless channels between the two devices are measured at different random locations. A function of these unique characteristics determines the shared secret key between the two devices.

Growing community of inventors

Salt Lake City, UT, United States of America

Sneha Kumar Kasera