Industrial applications can produce significant amounts of data that require low delay and high data rate communications. Multiple input multiple output filter bank multicarrier (MIMO-FBMC) communications employing offset quadrature amplitude modulation has been proposed for industrial big data (IBD) due to its reliability and high spectrum efficiency. One of the difficulties in implementing a MIMO-FBMC system is accurate channel estimation (CE). The main factor affecting CE performance is intrinsic imaginary interference, and conventional preamble-based CE is not effective in this case. Thus, in this paper, a low-complexity sparse adaptive CE scheme is proposed which is based on a dynamic threshold. This reduces the number of inner product calculations by considering only the columns of the measurement matrix greater than the threshold. Simulation results are presented which show that the proposed scheme is better than other well-known methods in terms of computational complexity and CE accuracy.

This work was supported in part by the National Natural Science Foundation of China under Grant 61701462 and Grant 41527901, in part by the Qingdao National Laboratory for Marine Science and Technology under Grant 2017ASKJ01, in part by the Qingdao Science and Technology Plan under Grant 17-1-1-7-jch, in part by the Fundamental Research Funds for the Central Universities under Grant 201713018, in part by the Shandong Province Natural Science Foundation under Grant ZR2017BF023, in part by the China Postdoctoral Science Foundation funded project under Grant 2017M612223, and in part by the Shandong Province Postdoctoral Innovation Project under Grant 201703032.

The global healthcare industry and artificial intelligence have promoted the development of the diversified intelligent healthcare applications. Internet of Things (IoT) will play an important role in meeting the high throughput requirements of diversified intelligent healthcare applications. However, the mobile IoT-enabled healthcare networks are diverse and open, the healthcare big data transmission is vulnerable to a potential attack, which can cause network outages and serious healthcare security issues. To process the complex healthcare security event in real time, security performance prediction is critical for mobile IoT-enabled healthcare networks. In this article, we first analyze the security performance, and derive the novel expressions for the security performance in a closed form. Then, to analyze the security performance in real time, a security performance intelligent prediction algorithm is proposed. An improved convolutional neural network (CNN) model is designed, which combines the four-layer convolution and a four-branch inception block, and can adopt different convolution kernels in the same layer. The four-branch inception block can increase the width of the CNN while reducing the parameters. The improved CNN model can not only increases the width of the CNN, extract different sizes of healthcare data features, but also increases the adaptability to the nonlinear healthcare big data. Compared with different methods, the proposed intelligent algorithm can obtain better security performance prediction. In particular, for prediction precision, the proposed intelligent algorithm is increased by 20%.

Advances in sensor technology have accelerated the development of mobile internet applications and contributed to the tremendous growth of the Internet of Things (IoT). Mobile IoT networks typically operate in complex and highly variable urban wireless channel environments. This makes effective and reliable communications challenging. To ensure efficient and stable communications, mobile IoT networks must adapt to the complex environment. This can be achieved by predicting the outage probability (OP). This paper investigates OP analysis and prediction for these networks. Exact closed-form OP expressions are derived and Monte-Carlo simulation is used to verify the analysis and evaluate the OP performance. Then, an OP prediction approach based on an improved grey wolf optimization (IGWO) algorithm and Elman neural network (IGWO-Elman) is proposed. The IGWO algorithm employs improved opposition-based learning to optimize the population initialization and ensure sufficient population diversity. The Elman neural network uses the IGWO algorithm to obtain good network parameters. Simulation results are presented which demonstrate that the prediction accuracy of IGWO-Elman is better than SVM, ELM, and BP algorithms. In terms of prediction accuracy, the IGWO-Elman algorithm is increased by 44%.

Recently, recommender systems are applied to provide personalized recomendation for healthcare wearables. However, due to the sparsity problem, traditional recommendation algorithms are difficult to achieve desired performance. Considering that consumers often buy and rate other types of items on E-commerce platforms, we can leverage significant information in the auxiliary domains to improve the recommendation performance of healthcare wearables, which can be regarded as cross-domain recommendation. However, traditional cross-domain recommendation model cannot fully represent user’s characteristics and fail to consider the leaks of original auxiliary domain ratings during the information transfer process. To overcome the two shortcomings, this paper proposes a Privacy-Preserving Cross-Domain Healthcare Wearables Recommendation algorithm (PPCDHWRec). Firstly, user’s characteristics are divided into domain-dependent features and domain-independent features, which complement each other and fully depict the user’s characteristics. Secondly, inspired by the latent factor model, we factorize the original rating information of each auxiliary domain by Funk-SVD and Orthogonal Nonnegative Matrix Tri-Factorization (ONMTF) model, to obtain user’s domain-dependent and domain-independent features, respectively. Finally, the Factorization Machine algorithm is used to fuse the obtained user’s features with the target domain information to provide the recommendation results. By hiding the item latent factors obtained in the factorization process, PPCDHWRec ensures that the original information cannot be inferred from the transferred user hidden vector. Hence, PPCDHWRec is a privacy-preserving recommendation model. Experiments on two groups of auxiliary domains, having high and low correlations with target domain, show the effectiveness of PPCDHWRec.