5 results about "Wireless security" patented technology

This utility model relates to the field of tunnel lighting control technology, and more particularly to a wireless safety tunnel lighting controller. It includes a tunnel wall panel with an inner cavity groove on its surface. The controller body is mounted on the inner wall of the inner cavity groove. An adjustment device is provided on the bottom surface of the inner wall groove corresponding to the controller body. The adjustment device includes two fixed rods and a connecting plate. Both ends of the two fixed rods are fixedly connected to the bottom surface of the inner wall groove. Both ends of the connecting plate are rotatably connected to pulleys, and the arc surfaces of the pulleys are slidably connected to the arc surfaces of the fixed rods. A connecting rod is fixedly connected to the upper surface of the connecting plate, and a fixing frame is inserted into the surface of the connecting rod. The inner wall of the fixing frame is engaged with the surface of the controller body. This utility model provides a wireless safety tunnel lighting controller with the advantage of conveniently adjusting and limiting the position of the lighting controller within the tunnel.

This invention proposes a dual reconfigurable smart surface (RIS)-assisted "one-time pad" secure wireless communication method to address the problems of low physical layer key generation rate and insufficient key rate to meet the requirements of one-time pad data transmission in wireless communication. This method constructs a controllable-random channel environment by deploying two N-unit RISs. It utilizes a multi-agent reinforcement learning (MARL) algorithm to jointly optimize the RIS phase shift and the number of key negotiation rounds, achieving a dynamic balance between data transmission rate and key generation rate. Based on this, it achieves absolutely secure communication for one-time pad data transmission. First, the legitimate communicating parties (Alice and Bob) construct a multipath random wireless channel by controlling the dual RISs based on channel reciprocity in time-division duplex (TDD) mode. Second, the MARL algorithm (including a pre-training mechanism) is used to control and optimize the phase shift of the dual RISs and determine the optimal number of key negotiation rounds for one-time pad. Finally, the physical layer key is generated through channel probing, channel estimation, quantization negotiation, and privacy amplification. Before data transmission, the generated key is used to XOR the transmitted data, achieving lightweight encryption of the transmitted data. This method significantly improves the physical layer key generation rate, provides a feasible solution for realizing "one-time key" data transmission, and is applicable to various wireless security communication scenarios such as the Internet of Things, the Internet of Vehicles, and satellite communication. It can take into account both the complexity of edge device deployment and real-time requirements.

A security network provides reduced power consumption and more robust communication of messages in comparison to conventional wireless systems. Reducing power consumption as discussed herein ensures that the security system is able to operate for a long duration of time, potentially with minimal or no power from an electrical grid. Additionally, redundant communication paths as discussed herein provide a more robust way of selectively forwarding security data to a remote server. The availability of multiple communication paths ensures that a respective remote target recipient such as a server resource or remote communication device operated by a user can be notified of a trigger event during power failure conditions, such as when certain communication functionality of a security system is disabled.

Embodiments of the present application provide a deep learning-based IP geographical location identification method and device and electronic equipment, and relate to the field of wireless security network management. The method comprises: in response to a geographical location identification request triggered for a first IP address to be identified, issuing a calling request instruction to a deep learning-based IP geographical location identification model; obtaining online data for the first IP address; performing feature vector processing on the at least one first data to obtain a feature vector of the first IP address; using the feature vector of the first IP address as input, identifying the geographical location of the first IP address by using the geographical location identification model; and outputting the geographical location of the first IP address. The method can improve the identification accuracy, efficiency and practicability of the geographical location of the IP address on the basis of improving the reusability of the IP address.