Privacy preservation-driven communication-computing collaboration for multi-mode heterogeneous IoT network management

Abstract

The multi-mode heterogeneous network combines the advantages of high-speed power line communication (HPLC) and high radio frequency (HRF), ensuring service quality and meeting the requirements for data transmission delay and reliability even when devices are flexibly deployed. Queuing delay and privacy entropy are important metrics for managing multi-mode heterogeneous internet of things (IoT) networks, which require collaborative optimization of the transmission phase (server selection and sub-channel allocation) and the computing phase (computing resource allocation) to ensure low latency and high privacy entropy. However, existing communication-computing collaborative optimization methods face issues such as low privacy security of electricity-carbon service data, high difficulty in solving the joint optimization problem, and resource competition. Therefore, this paper proposes a privacy preservation-driven communication-computing collaboration method for the management of multi-mode heterogeneous IoT networks. Firstly, the architecture for the management of multi-mode heterogeneous IoT networks is constructed and a privacy entropy model for electricity-carbon computing service data is established to measure the privacy security performance of the network management. Secondly, a joint optimization problem of queuing delay and privacy entropy under long-term privacy entropy constraints are constructed and the long-term privacy entropy constraints from short-term decisions is decoupled based on Lyapunov optimization. Finally, a joint optimization algorithm for server selection and multi-mode sub-channel allocation driven by privacy protection is proposed. This algorithm reduces the three-dimensional matching optimization problem among different devices, servers, and channels, and uses auction matching to solve the conflict of resource block selection, further optimizing the computing resource allocation of edge servers based on the Karush–Kuhn–Tucker (KKT) conditions. Simulation results show that the proposed algorithm effectively reduces queuing delay and improves privacy security of data transmission.