Abstract: A method for monitoring network activity includes initiating a training phase by a machine learning (ML) server. Data associated with normal network traffic through the ML server during the training phase is collected. A classification model is generated based on the collected data. The ML server switches the training phase to an active phase. An outbound request is received during the active phase. Whether the outbound request is an anomalous request is determined based on the classification model.

Abstract: Systems, methods, and software can be used to provide inter-vehicle data communications for multimedia content. In some aspects, a method is disclosed comprising: receiving, at a first vehicle, a multimedia content request message from a second vehicle, wherein the multimedia content request message indicates a target location at which multimedia content is requested; determining, a current location of the first vehicle; in response to determining that the current location matching the target location, generating the multimedia content; and transmitting, by the first vehicle, the multimedia content.

Abstract: Systems, methods, and software can be used to improve efficiency of a cargo shipping system. In some aspect, a shipping request for a load is received. A plurality of cargo transportation unit (CTU) status notifications are received. Each of the plurality of CTU status notifications is generated by a cargo tracking device coupled with a respective CTU, and each of the plurality of CTU status notifications indicates a load status of the respective CTU. A CTU is selected to ship the load based on the plurality of CTU status notifications. A shipping order is sent. The shipping order identifies the selected CTU.

Abstract: Systems, methods, and software can be used to improve efficiency of a cargo shipping system. In some aspect, a cargo transportation unit (CTU) status notification is received from a cargo tracking device coupled with a CTU. The CTU status notification indicates a load status of the CTU. The CTU is determined to be ready for shipment based on the CTU status notification. Availability information associated with each of a plurality of drivers is received. A payment associated with each of the plurality of drivers is determined. A driver is selected from the plurality of drivers based at least in part on the availability information and the payment for the driver. A delivery request is sent to the selected driver.

Abstract: Systems, methods, and software can be used to improve efficiency of a cargo shipping system. In some aspect, a cargo transportation unit (CTU) status notification is received from a cargo tracking device coupled with a CTU. The CTU status notification indicates a location of the CTU. A load event is determined based on the location of the CTU. A delivery record is generated based on the load event. A rating of a driver that is assigned to the CTU is generated, based on the delivery record.