Internet of Things (IoT) devices operate in an untrusted environment, and blockchain technology can provide a secure distributed collaboration method. However, the widespread distribution of heterogeneous devices in the IoT creates complex information asymmetry issues, leading to inefficient resource allocation. This is especially true when information asymmetry occurs in long-term cooperative relationships, where contract design often faces more complex incentive problems. This article focuses on studying long-term cooperation between IoT devices based on the blockchain technology and proposes a solution to information asymmetry in long-term cooperation based on contract theory and reputation mechanism: 1) we design more flexible pricing strategies based on contract theory to attract more devices to become validators and provide different long-term job contracts to overcome information asymmetry issues in the cooperative process and 2) we propose a subjective reliability-linked reputation evaluation mechanism to construct the career of validators, which can constrain unethical behavior and incentivize validators to comply with rules and protect data security in long-term cooperation. Experimental results show that our scheme can improve device willingness to cooperate, overcome information asymmetry in long-term cooperation, improve the efficiency of computing resource allocation, reduce network security risks, achieve long-term trusted cooperation between devices, and provide effective security for the development of the IoT.

The development of cognitive radio and electronic warfare brings new challenges to radar electronic reconnaissance, the recognition of radar signal plays an extreme important role in radar electronic reconnaissance. In order to realize the reliable recognition of radar signal at the condition of low signal-to-noise ratio (SNR), we propose a new radar signal recognition system based on non-negative matrix factorization network (NMFN) and ensemble learning, which can recognize radar signals including BPSK, LFM, NLFM, COSTAS, FRANK, P1, P2, P3 and P4. First, we explore feature extractor based on convolutional neural network (CNN), which applies transfer learning to solve the problem of small sample size. Second, we propose non-negative matrix factorization network to extract features, which can reduce the redundant information. Third, we develop feature fusion algorithm based on stacked autoencoder (SAE), which can acquire essential expression of features and reduce dimension of features. Finally, we propose improved artificial bee colony algorithm (IABC) as the strategy of ensemble learning, which can improve the recognition rate. The simulation results show that the recognition rates reach 94.23% at -4 dB, 99.82% at 6 dB.