Abstract: Examples to restore a trusted backup configuration for a node. Example techniques include failover to an alternate firmware of the node, in response to an unverifiable condition of an existing firmware of the node. The node may validate a first configuration file stored in the node. The first configuration file includes a first backup configuration. The node may validate a second configuration file stored in the node based on the validation of the first configuration file. The second configuration file includes a second backup configuration. In response to the validation of at least one of the first configuration file and the second configuration file, the node may select one of the first backup configuration and the second backup configuration, and apply the selected backup configuration to the node.

Abstract: Examples to restore a trusted backup configuration for a node. Example techniques include failover to an alternate firmware of the node, in response to an unverifiable condition of an existing firmware of the node. The node may validate a first configuration file stored in the node. The first configuration file includes a first backup configuration. The node may validate a second configuration file stored in the node based on the validation of the first configuration file. The second configuration file includes a second backup configuration. In response to the validation of at least one of the first configuration file and the second configuration file, the node may select one of the first backup configuration and the second backup configuration, and apply the selected backup configuration to the node.

Abstract: Examples disclosed herein relate to initializing a fan speed of a computing device using a high-water value when a pre-runtime event occurs. A management controller (MC) receives sensor data from sensors of the computing device. A fan speed of a fan is controlled using a fan control value based on the sensor data. The fan control value is monitored to determine a high-water value of the fan control value. The high-water value is stored in a location that is accessible by a pre-runtime fan control engine of the computing device. In response to a pre-runtime event, the fan speed is initialized to a speed using the high-water value.

Abstract: Examples disclosed herein relate to initializing a fan speed of a computing device using a high-water value when a pre-runtime event occurs. A management controller (MC) receives sensor data from sensors of the computing device. A fan speed of a fan is controlled using a fan control value based on the sensor data. The fan control value is monitored to determine a high-water value of the fan control value. The high-water value is stored in a location that is accessible by a pre-runtime fan control engine of the computing device. In response to a pre-runtime event, the fan speed is initialized to a speed using the high-water value.

Abstract: A traffic control monitoring and abnormality determination system and associated methods are disclosed for receiving and analyzing traffic controller input/output data during a learning phase to determine a model indicative of normal or healthy operation of the traffic controller in regulating traffic flow at an intersection and receiving and evaluating additional traffic controller input/output data against the model during an evaluation phase to determine whether an abnormality exists in operation of the traffic controller. If an abnormality is detected during the evaluation phase, the system may initiate a corrective action to resolve the abnormality such as sending an alarm signal to a traffic controller to cause the traffic controller to alter an operating state to resolve the abnormality.

Abstract: A traffic control monitoring and abnormality determination system and associated methods are disclosed for receiving and analyzing traffic controller input/output data during a learning phase to determine a model indicative of normal or healthy operation of the traffic controller in regulating traffic flow at an intersection and receiving and evaluating additional traffic controller input/output data against the model during an evaluation phase to determine whether an abnormality exists in operation of the traffic controller. If an abnormality is detected during the evaluation phase, the system may initiate a corrective action to resolve the abnormality such as sending an alarm signal to a traffic controller to cause the traffic controller to alter an operating state to resolve the abnormality.

Abstract: A traffic control monitoring and abnormality determination system and associated methods are disclosed for receiving and analyzing traffic controller input/output data during a learning phase to determine a model indicative of normal or healthy operation of the traffic controller in regulating traffic flow at an intersection and receiving and evaluating additional traffic controller input/output data against the model during an evaluation phase to determine whether an abnormality exists in operation of the traffic controller. If an abnormality is detected during the evaluation phase, the system may initiate a corrective action to resolve the abnormality such as sending an alarm signal to a traffic controller to cause the traffic controller to alter an operating state to resolve the abnormality.

Abstract: A traffic control monitoring and abnormality determination system and associated methods are disclosed for receiving and analyzing traffic controller input/output data during a learning phase to determine a model indicative of normal or healthy operation of the traffic controller in regulating traffic flow at an intersection and receiving and evaluating additional traffic controller input/output data against the model during an evaluation phase to determine whether an abnormality exists in operation of the traffic controller. If an abnormality is detected during the evaluation phase, the system may initiate a corrective action to resolve the abnormality such as sending an alarm signal to a traffic controller to cause the traffic controller to alter an operating state to resolve the abnormality.

Abstract: Embodiments include a method for adjusting a signal control plan for a traffic signal at an intersection based on local environmental conditions is provided. The method includes operating the traffic signal based on a standard signal control plan, receiving an indication of an adverse environmental condition, and determining if the standard signal control plan should be adjusted based on the indication of the adverse environmental condition. Based on determining that the standard signal control plan should be adjusted based on the indication of the adverse environmental condition, the method includes creating a modified signal control plan based on the adverse environmental condition and the standard signal control plan and operating the traffic signal based on the modified signal control plan.

Abstract: Embodiments include a method for adjusting a signal control plan for a traffic signal at an intersection based on local environmental conditions is provided. The method includes operating the traffic signal based on a standard signal control plan, receiving an indication of an adverse environmental condition, and determining if the standard signal control plan should be adjusted based on the indication of the adverse environmental condition. Based on determining that the standard signal control plan should be adjusted based on the indication of the adverse environmental condition, the method includes creating a modified signal control plan based on the adverse environmental condition and the standard signal control plan and operating the traffic signal based on the modified signal control plan.