A management electronic device includes a processing circuit configured to: obtain a spectrum in an N-th round of spectrum trading and acquire the interference received by the management electronic device when an electronic device uses the traded spectrum; receive the interference received by another electronic device as determined by another electronic device related to spectrum trading; determine competition rights parameters of the management electronic device and the other electronic device, respectively, the competition rights parameters representing the credibility of the electronic devices and the magnitude of competitiveness when competing for management rights in an N+1-th round of spectrum trading; and select a new management electronic device on the basis of at least one among the interference and the competition rights parameters, the new management electronic device having management rights in the N+1-th round of spectrum trading, instead of the management electronic device having management rights.

Aspects described herein may allow for determining network membership to facilitate detecting fraudulent messages. A computing device may receive, from one or more terminals at a first location, a plurality of interactive messages during a pre-determined time period. Each interactive message may comprise personally identifiable information of a user. The computing device may store a first record and a second record that indicate interactive messages were received from a group of users in temporal proximity to each other at the first location and the second location respectively. The computing device may send a request to confirm users in the subset are members of a network and update a membership list based on a reply received from the user. If further messages are received from devices outside the membership list, an alert may be sent to alert the possibility of a fraudulent message.

Aspects of the subject disclosure may include, for example, initializing a secure timer in a wireless device, determining whether a subscriber identification module (SIM) card installed in the wireless device comprises a carrier identity that matches a carrier identity stored in the machine-readable medium, establishing a network connection with a trusted server, starting the secure timer if the SIM card and network connection are satisfactory, periodically checking the network connection and SIM card until expiry of the secure timer, penalizing the secure timer responsive to a failure of the network connection or SIM card check, and responsive to expiry of the secure timer, unlocking a SIM lock. Other embodiments are disclosed.

According to some embodiments, there is provided a Collective Perception Message, CPM, characterizing a plurality of Vulnerable Road Users based on a plurality of received VAMs, thereby allowing an ITS station to efficiently aggregate VAM messages from VRUs and retransmit information about the VRUs to other ITS stations. Consequently, the security is improved as some ITS stations may not be able to detect or identify VRU stations by themselves but thanks to the CPM, these stations can still be informed of the VRUs. According to other aspects, congestion is avoided while maintaining safety vis-à-vis VRUs thanks to the use of a different transmission scheme when the VRU is already characterized in a CPM sent to the ITS stations. Also, a receiving station can evaluate whether the content of a CPM can be trusted or not. Safety is thus improved. This is achieved thanks to the CPM that references a certificate.

A method includes establishing a multi-link security association between a transmitter upper Media Access Control (MAC) logic entity of a transmitter and a receiver upper MAC logic entity of a receiver. The transmitter includes one or more transmitter links. The receiver includes one or more receiver links.

This disclosure provides methods, devices and systems for a vehicle user equipment (VUE) to obtain extrinsic information about an object or location. The VUE may transmit a request for information about the object or the location to a road side unit (RSU). The RSU may receive the request and determine a set of extrinsic information for the first UE regarding the object or the location based on a set of information from one or more other UEs. The extrinsic information includes information that is not provided by the VUE. The RSU may transmit the set of extrinsic information to the VUE. The VUE may determine whether to accept a feature of the object or the location in the extrinsic information based on the set of extrinsic information and a set of intrinsic information detected by the VUE, The VUE may select an autonomous driving action based on the accepted feature.

Information is received from a first networked device for a first user and from a second networked device for a second user. The first user and the second user are verified and registered. A first set of data for the first user and a second set of data for the second user that each specify one or more network parameters per network address that communicates with each user are received from a networked collector device. Addresses are selected from each of the first set and the second set where each of the one or more network parameters are above a first activity threshold level for that parameter. A first set and a second set of first level activity addresses are produced. A whitelist is generated for the first user from an intersection of the first set of first level activity addresses and the second set of first level activity addresses.

A device (110) arranged for wireless communication (130) according to a communication protocol has a processor (112) to execute a connection sequence according to a discovery protocol. The connection sequence comprises determining a current cluster identity and a current discovery window timing used by the device. Next, at least one other device (120,120′) within wireless range is detected, while further determining a detected cluster identity and a detected discovery window timing of the detected other device. Then it is detected whether the detected device is operating in a different cluster than the device by comparing the current cluster identity with the detected cluster identity or comparing the current discovery window timing with the detected discovery window timing. Finally, upon detecting said different cluster, a security process is executed, which may warn the user or abort the connection sequence. Thereby, a malicious device trying to manipulate the connection sequence is detected.

Concepts and technologies are disclosed herein for managing access based on activities of entities. A computing device can collect data that comprises an image. The computing device can identify an entity that is located in a range of a sensor. The computing device can determine an identity that is associated with the entity and an activity associated with the entity. The computing device can obtain a trust indicator associated with the entity. The computing device can determine, based on the trust indicator, if the activity should be allowed. If the computing device determines that the activity should be allowed, the computing device can initiate allowing of the activity. If the computing device determines that the activity should not be allowed, the computing device can initiate blocking of the activity.

Methods and apparatus automatically identify which Internet-of-Things (IoT) devices within a set are behaving in a manner non-compliant with a target behavior. Each IoT device is assigned to a grid point in a notional m-dimensional space. A respective assessment module is arranged to monitor behavior of a group of IoT devices assigned to grid points aligned with one another at a respective position along the respective dimension, and to produce an output indicative of non-compliant behavior if the monitoring indicates that behavior in the group of IoT devices deviates from a behavioral model of the IoT devices of said group. An identification module identifies at least one non-compliant IoT device in the set of IoT devices by combining outputs from the assessment modules assigned to the different dimensions of the space. The assessment modules may use trained machine-learning algorithms embodying a model of normal behavior of the group of devices.