A Homomorphic Encryption Algorithm Based on Communication and Sensing Integration

In the development of the Internet of Things (IoT), the server layer handles data structures and algorithms, which can successfully solve the desert island problem of heterogeneous data, but it will occupy a large amount of server resources and cause system pressure. To address the additional computational burden, it is necessary to make full use of the computing power of the edge layer. Solving problems such as communication connections, data detection and channel reasoning between large-scale IoT terminal nodes in non-ideal environments is worthy of in-depth study. Traditional frequency offset compensation algorithms and clock synchronization technology can no longer be applied to large-scale IoT solution design. A technology that can accurately reason about frequency offset and increase data transmission rate is needed. For large-scale direct connection scenarios that consider frequency offset and data transmission rate, this paper considers that the equipment required by the IoT terminals sends downlink transmission data-related information to achieve signal coverage, combines the big data scenario of large-scale direct connection, and introduces the data transmission rate to build a homomorphic encryption transmission model for asynchronous data transmission in the IoT system. To reduce the impact of data transmission rate on reconstruction quality and performance, a method that combines asynchronous data transmission processing and channel inference prediction is constructed. An optimization scheme for multi-node detection and channel inference of Raft algorithm homomorphic encryption and convex optimization is designed. In the simulation test, the paper compares the detection bit error rate, channel inference quality and performance of the traditional Lasso algorithm and the OMP algorithm based on serial communication interference signal elimination. The experimental results show that the performance of the Raft algorithm proposed by the paper is better than the Lasso algorithm by 7.52% and the OMP algorithm by 10.64%, which has a greater technical advantage. Meanwhile, the innovative use of homomorphic encryption transmission method to asynchronously process big data information will promote the IoT and big data information processing, which can enable blockchain technology to play an important role in various types of IoT systems.