In cognitive wireless networks, multi-node cooperative spectrum sensing can effectively improve the accuracy of spectrum sensing, but there is a non-linear relationship between the number of nodes and sensing accuracy. Nodes with low reliability participate in cooperative sensing, which is not conducive to the improvement of sensing accuracy, and reduces the energy efficiency of spectrum sensing, which poses challenges to the normal operation of cognitive wireless networks. In order to improve energy efficiency and sensing performance, this paper proposes the node evaluation and scheduling (NES) algorithm and the Secure Spectrum Sensing based on Blockchain (SSSB) algorithm, which can evaluate the reliability of sensing nodes in real time, and obtain the trust value of the node. The nodes information is stored in the management center of blockchain. Blockchain encrypts nodes information to ensure that a node corresponds to its own trust value without confusion. Fusion Center of cognitive wireless networks select good performance nodes to participate in cooperative spectrum sensing. Which can reduce energy consumption while improving the sensing performance. The simulation experiment results show that the new algorithm in this paper is far superior to the traditional algorithm. Under the same other conditions, the detection probability is increased by 5%, and the energy consumption is reduced by 10%, and the safety index has also been greatly improved.INDEX TERMS Cognitive wireless networks, blockchain, energy efficiency, sensing accuracy.

In multi-node cooperative sensing of cognitive networks, as the number of nodes increases, and the energy consumption must increase, but the sensing performance does not necessarily improve. The nodes with less information are not helpful for the sensing performance but will increase the unnecessary energy consumption. To improve the sensing performance and reduce the energy consumption of nodes, a dynamic node selection algorithm based on reinforcement learning is proposed in this paper. The algorithm can evaluate the reliability of sensing nodes in real-time, select the nodes with the highest reliability to participate in cooperative sensing, and update the reliability of nodes in real-time through the method of combining feedback energy consumption and sensing performance. In a real-time environment, nodes with high reliability are selected to participate in cooperative sensing, and the optimal balance between sensing performance and energy consumption is achieved. The experimental results show that the proposed algorithm can reduce energy consumption and improve the perception performance at the same time. Under the same conditions, the detection probability is 5% higher than that of the traditional method, while the energy consumption is only 16.7% of that of the traditional method.

Multi-node cooperative sensing can effectively improve the performance of spectrum sensing. Multi-node cooperation will generate a large number of local data, and each node will send its own sensing data to the fusion center. The fusion center will fuse the local sensing results and make a global decision. Therefore, the more nodes, the more data, when the number of nodes is large, the global decision will be delayed. In order to achieve the real-time spectrum sensing, the fusion center needs to quickly fuse the data of each node. In this article, a fast algorithm of big data fusion is proposed to improve the real-time performance of the global decision. The algorithm improves the computing speed by reducing repeated computation. The reinforcement learning mechanism is used to mark the processed data. When the same environment parameter appears, the fusion center can directly call the nodes under the parameter environment, without having to conduct the sensing operation again. This greatly reduces the amount of data processed and improves the data processing efficiency of the fusion center. Experimental results show that the algorithm in this article can reduce the computation time while improving the sensing performance.

Based on the research of social network and the Internet of Things, a new research topic in the field of Internet of Things, Social Internet of Things is gradually formed. The SIoT applies the research results of SIoT from different aspects of the Internet of Things, and solves the specific problems in the research of Internet of Things, which brings new opportunities for the development of the Internet of Things. With the development of the Internet of Things technology, in the spatial social Internet of Things structure, user information includes sensitive attributes and non-sensitive attribute information. This information can be inferred from public user information to infer the information of the private user and even speculate on sensitive attributes. This article proposes an information speculation method based on the core users of spatial social networks, and estimates the non-core user information through the core user public information. First, the user’s spatial social network is divided into communities, and the core nodes of the community in the spatial social network are calculated by PageRank algorithm and the convergence of the algorithm is proved. Then, through the public information of the core nodes divided by the community in the space social network, the private information of relevant users to these core nodes can be speculated. Finally, by experimental analyzing the community structures of SIoT (Social Internet of Things) like Twitter, Sina Weibo, ER random networks, and NW small-world network, and making 5%, 10%, 15%, 20% information anonymous respectively in these four kinds of networks, we can analyze their clustering coefficient, Q-modularity and properties. Finally, the key node information of the four spatial social structures is speculated to analyze the effectiveness of the proposed method. Compared with the non-core speculation method, this method has advantages in speculative information integrity and time.

This paper suggests a cooperative spectrum sensing using dynamic double thresholds energy detection and adaptive grid search to obtain the highest probability detection.