Cisco Technology, Inc.

Ashok Krishnaji Moghe

John George Apostolopoulos

Flavio Giovanni Bonomi

Sateesh Kumar Addepalli

Lillian Lei Dai

Pascal Thubert

Thomas Szigeti

Vincent E Parla

Tao Zhang

Rong Pan

Andrew Zawadowskiy

Min He

Avraham A Poupko

Vijaynarayanan Subramanian

Aaron A Lung

Marc Jean-Philippe Girardot

Charles Calvin Byers

Eric Albin Voit

Fabio Rodolfo Maino

Xiaoqing Zhu

David A Maluf

Chiu Ngok Eric Wong

Pere Monclus

Vina Ermagan

Douglas Chan

Eliot Lear

Preethi Natarajan

Rodolfo Alberto Milito

Andrea A Gil Batres

Mohammand H Falaki

Yesu Lu

Techniques and systems described herein relate to monitoring executions of computer instructions on computing devices based on learning and generating a control flow directed graph. The techniques and systems include determining an observation phase for a process or application on a computing device. During the observation phase, CPU telemetry is determined and used to generate a control flow directed graph. After the control flow directed graph is generated, a monitoring phase may be entered where transfers of instruction pointers are monitored based on the control flow directed graph to identify invalid transfers.

Control flow integrity monitoring for applications running on platforms

Techniques and systems described herein relate to monitoring executions of computer instructions on computing devices based on learning and generating a control flow directed graph. The techniques and systems include determining telemetry representing execution of a process on a computing system and accessing a learned control flow diagram graph for the process. A transfer of an instruction pointer is determined based on the telemetry and a validity of the transfer is determined based on the learned control flow directed graph. If invalid, then an action to terminate the process is determined, otherwise the action may be allowed to execute when valid.

Control flow integrity enforcement for applications running on platforms

A computing device connected to a power source via a combined power/data connection obtains an authentication request from the power source. The authentication request includes a freshness mechanism provided by the power source. The computing device signs an authentication response with a private key associated with a verified identity stored on the computing device. The authentication response includes the freshness mechanism. The computing device provides the authentication response to the power source, and receives power from the power source.

Fast repowering using cryptographically protected identity

Presented herein are techniques for updating detailed maps used to navigate an autonomous vehicle. The techniques include determining that a vehicle has come within a predetermined range of a road side unit, establishing a communication link with the vehicle, receiving, from the vehicle, data sufficient to identify a vehicle type of the vehicle, based on the vehicle type, selecting a map, stored by the road side unit, for the vehicle, sending a query to a neighbor road side unit seeking data to augment the map, in response to the query, receiving the data to augment the map from the neighbor road side unit, updating the map based on the data to augment the map to obtain an updated map, and sending at least a aspects of the updated map to the vehicle.

Real-time updates to maps for autonomous navigation

In one embodiment, a device associates a charging coil with a coil identifier. The device sends the coil identifier to a communication module of the charging coil. The communication module of the charging coil forwards the coil identifier to an electric vehicle located above the charging coil. The device receives the coil identifier from the electric vehicle. The device associates the electric vehicle with the charging coil.

Coil association in multisite stationary wireless power transfer (WPT) and (quasi-)dynamic WPT deployments

In one embodiment, a device of a tractor unit determines that the tractor unit is connected to a trailer via physical cabling. The device sends, via the physical cabling, a powerline communication (PLC) message to the trailer that includes a service set identifier (SSID) and password for a Wi-Fi transceiver of the tractor unit. The Wi-Fi transceiver of the tractor unit receives an association request sent wirelessly from a Wi-Fi transceiver of the trailer that is based on the sent SSID and password. The device establishes the Wi-Fi transceiver of trailer as a Wi-Fi client of the Wi-Fi transceiver of the tractor unit.

Automatic provisioning of Wi-Fi connections for trailers

In one embodiment, a device obtains sensor data indicative of three dimensional (3-D) orientations of primary and secondary wireless power transfer (WPT) charging coils. The secondary coil is mounted to a vehicle and the primary coil provides charge to the secondary coil during charging. The device detects misalignment between the primary and secondary WPT coils based on the sensor data. The device determines a coil alignment correction to offset the detected misalignment. The device sends control commands to one or more actuators to implement the coil alignment correction by moving one or more of the coils, either directly (e.g., via directly-coupled actuators) or indirectly (e.g., via the suspension of a vehicle).

Precision coil alignment techniques for vehicle wireless power transfer

In one embodiment, a static wireless power transfer (WPT) system (with charging spots and non-charging parking spots) receives a request to charge a vehicle. The device schedules a period of time during which the vehicle is allocated access to a particular charging spot based on scheduling characteristics and one or more other vehicles also requesting charging. The device may then send instructions to control the vehicle to autonomously move from a current parking spot to the particular charging spot for the scheduled period of time, the instructions precisely aligning the vehicle in the particular charging spot for optimum power transfer based on a charging coil of the vehicle and a respective ground-based charging coil of the particular charging spot. After the scheduled period of time, the device may send additional instructions to control the vehicle to autonomously move out of the particular charging spot.

Automated vehicle parking and wireless power transfer (WPT) charging

In one embodiment, a fog computing-based fueling kiosk forms a fueling connection with a vehicle and a direct network connection between the kiosk and a gateway for a network of the vehicle. The fueling kiosk provides energy to the vehicle via the fueling connection and receives, via the network connection with the gateway for the network of the vehicle, operational data from the network of the vehicle, while providing the energy to the vehicle via the fueling connection. The fueling kiosk performs an analysis of the received operational data from the vehicle and provides a result of the performed analysis to a remote device.

Interfacing a vehicle with a fog device during fueling

In one embodiment, a device in a wireless power transfer (WPT) system receives data regarding a vehicle equipped with a vehicle-based charging coil configured to receive electrical power transferred from a ground-based charging coil of the WPT system. The device determines, based on the received data, a time at which the vehicle-based charging coil is expected to be in charging proximity of the ground-based coil. Based on the received data, the device determines a gap distance between the vehicle-based charging coil and the ground-based charging coil to optimize the transfer of electrical power from the ground-based charging coil to the vehicle-based charging coil when the coils are in charging proximity of one another. The device sends control instructions to an adjustment system to implement the identified gap distance in advance of the determined time by adjusting a height of the vehicle-based charging coil or the ground-based charging coil.

Gap control for vehicle wireless power transfer (WPT)

In some embodiments, a device in a network receives vehicle characteristic data regarding one or more autonomous vehicles. Each autonomous vehicle of the one or more autonomous vehicles is equipped with a vehicle-based charging coil configured to receive electrical power from a ground-based charging coil of a wireless power transfer (WPT) system. The device, based on the received vehicle characteristic data, identifies one or more ground-based charging coils of the WPT system available to provide power to the one or more autonomous vehicles. The device determines driving parameters for the one or more autonomous vehicles to optimize power transfer from the one or more ground-based charging coils to the one or more autonomous vehicles. The device sends the driving parameters to the one or more autonomous vehicles to control movement of the one or more autonomous vehicles.

Intelligent vehicle control for wireless power transfer (WPT)

In some embodiments, a device in a network receives vehicle characteristic data regarding a set of vehicles. The device identifies, based on the received vehicle characteristic data regarding the set of vehicles, a particular ground-based charging coil with which charging coils of the set of vehicles are expected to be in a close proximity. The device determines, based on the received vehicle characteristic data regarding the set of vehicles, a set of power levels and times at which the particular ground-based charging coil is to be powered. The device dynamically controls the particular ground-based charging coil to be powered at the determined set of power levels and times.

Intelligent coil control for vehicle wireless power transfer (WPT)

In one embodiment, a prediction agent process collects travel information of a vehicle, and determines a profile of the vehicle, the profile indicative of one or more real-time resource requirements of the vehicle. The prediction agent process also predicts a path of the vehicle based on the travel information, and determines a next resource node along the predicted path having one or more real-time resources corresponding to the one or more real-time resource requirements of the vehicle. After further predicting a time of arrival of the vehicle being within range of the next resource node based on the travel information, the prediction agent process informs the next resource node of the profile of the vehicle and the predicted time of arrival, the informing causing the next resource node to operate the one or more real-time resources for the vehicle for the predicted time of arrival.

Predictive resource preparation and handoff for vehicle-to-infrastructure systems

Controlled startup of devices is based on dynamic statistical predictions. Timely startup of onboard associated vehicle devices is based on dynamic statistical predictions and driver proximity to the vehicle. An apparatus for timely startup includes an interface operatively coupled with a power consuming device and control logic coupled with the interface. The control logic is operable in a first mode to perform processing for determining a presence of a first condition of the vehicle, and to selectively activate the power consuming device of the vehicle, via the interface, responsive to determining the presence of the first condition. The control logic is operable in a second mode to suspend, via the interface, the processing for determining the presence of the first condition of the vehicle. The control logic selectively transitions between the first and second modes in accordance with a stochastic modeling of the presence of the first condition over time.

Growing community of inventors

Pleasanton, CA, United States of America

Ashok Krishnaji Moghe