Recently, to reduce the increasing energy consumption along with the explosive growth of smart terminals and network data in Internet of Things (IoT), green IoT is attracting more and more attention from both academia and industry. Towards green IoT, this paper devotes attention to Device-to-Device (D2D) communication and social network which are two essential components in green IoT and first reviews the latest developments about them. Further, this paper proposes a social interaction assisted resource sharing scheme for D2D communication, to improve the utilization of spectrum resources in green IoT. Specifically, the proposed scheme abstracts the D2D communication system into social layer and physical layer, by combining social network with D2D communication. Extensive simulations with real social interaction data are conducted and they show that the proposed scheme can achieve improvements in terms of the transmission success rate for green IoT.

With the rapid development of technologies and improvement of living conditions, people are increasingly concerned about their life safety, convenience, and comfortableness. Household intelligence starts to play an increasingly important role in improving people living environment. This paper focuses on service prediction in smart home automatic control systems, and proposes an intelligent human behavior-based reasoning model on the basis of linear prediction and case-based reasoning. Hardware and software are designed after analysis of the embedded architecture. In addition, an embedded smart home platform is built up and implemented to validate the proposed human behavior-based reasoning algorithm.

Spectrum sensing (SS) has attracted much concern of researchers due to its significant contribution on the spectral efficiency. Energy Detection (ED) has been a critical method for Spectrum Sensing in Cognitive Radio Networks (CRNS) due to its low complexity and simple implement. However, noise uncertainty in ED greatly degrades the detection performance, especially under a low Signal-to-Noise Ratio (SNR). To remove noise uncertainty as much as possible, a scheme based on subspace decomposition is proposed for SS, where the received signal is decomposed into two parts: noise subspace and signal-plus-noise subspace. Then the closed-form solution of the detection and false alarm probabilities is given on the basis of the signal-plus-noise subspace in Rayleigh fading channel. The energy of the remainders after removal of noise subspace and noise contribution in signal-plus-noise subspace is used to decide whether the primary user (PU) exists by a comparison with a redesigned threshold. Eventually, some simulations based on MATLAB platform is made to validate the proposed method.

With the rapid development of hardware and software technology, modern industry has produced a large amount of high-dimensional unlabeled data, such as pictures and videos. As clusters of these data sets may exist in some subspaces, traditional algorithms are no longer applicable. The related algorithms based on sparse subspace can find clusters in the subspace, which solves the problem of high data dimension. However, their clustering process based on only one single feature, which results in their performance being particularly sensitive to this single view. Affected by the integrated algorithm, a large number of multi-view methods began to emerge. These methods improve the clustering performance by integrating the subspace expressions of multiple views, but the problem is that the complementary information of multiple views cannot be fully considered. In addition, the problem of non-uniform distribution in clusters also exists in high-dimensional data sets. In this paper, based on the multi-view subspace clustering method, a clustering algorithm based on tensor low rank expression is proposed to solve the clustering problem of high-dimensional datasets. On the one hand, this paper solves the problem of noise and data corruption by combining the method of $\ell _{2,1} $ norms, which transform the optimization problem of solving multi-view subspace expression into a low-rank expression problem of tensor to fully consider the complementarities between views. On the other hand, the proposed scheme solves the problem of non- uniform distribution in clusters in high-dimensional data by combining with the density peak algorithm based on hierarchical strategy. Experiment results show the effectiveness of the algorithm.