The real-time， unboundedness， and dynamically changing characteristics of data streams lead to time-varying data distributions， a phenomenon termed concept drift. Traditional methods for detecting and adapting to concept drift typically rely on the assumption of complete label availability. However， the prohibitively high cost of data annotation in real-world scenarios makes fully supervised learning approaches infeasible. Consequently， active learning is commonly utilized for classification tasks with scarce labels. Nevertheless， in streaming environments， factors such as concept drift and single-label strategies often introduce sampling bias into active learning. To address these challenges， a Multiple Active Learning method based on Concept Drift detection （MALCD） was proposed. An online deep neural network model incorporating dynamically weighted skip connections was designed and combined with a weakly supervised drift detection method to detect concept drift. At the same time， multiple sampling strategies were incorporated to apply differentiated processing strategies across different sample regions. By integrating multiple active learning methods with concept drift detection techniques， this method can precisely select data with high uncertainty and categorical diversity while efficiently avoiding redundancy. Experimental results on eight real-world and synthetic datasets demonstrate that MALCD achieved the highest average ranking in cumulative accuracy compared to Online Ensemble Adaptive Classification （AC_OE） method， Weakly Supervised Concept Drift Detection （WSCDD） method， etc. This indicates that the MALCD can quickly learn new concept distributions after drift occurs， thereby enhancing the model's overall generalization performance.

To dynamically adjust the bandwidth price according to the change of channel quality so as to change the users' profit, an improved spectrum sharing model considering user's mobility was proposed．The improved model comprehensively considers the impact on the price of the offered bands by all secondary users' bandwidth requirement and channel quality; at the same time, the requested bandwidth gets constrained by price, and thus users' profit change with position. The simulation results indicate that users can gain maximum profit by choosing a optimal position in the moving process.

To transmit data accurately in strong interference environments, a communication protocol based on Manchester code was designed. It is composed of sending module and receiving module: data were transferred in the form of “packets” at the sending end; dislocated code and a counter were used to solve the problem of clock synchronization at the receiving end. The communication protocol could solve the issue of identifying the data boundaries and the phase error caused by the accumulated error of clock. In a simulated interference environment, a transfer rate of 40Mbps was achieved on a platform. The experimental results indicate that the communication protocol is able to transmit data accurately.

The Shin's signcryption scheme with public verifiability of Shin et al.and the proxy signcryption scheme of Jung et al. were cryptoanalyzed and improved. By studying the confidentiality of Shin's scheme and Jung's scheme, and the proxy authority of Jung's scheme, the fact is that the confidentiality of Shin's scheme and Jung's scheme was lost, and the proxy authority of Jung's scheme was not controlled. Then a modification based on Shin's scheme was presented. The modification was a security proxy signcryption scheme with public verifiability and controllable authority. Based on some cryptographic assumptions, the security of the scheme was also proved.