In Industrial Internet of Things (IIoT), Peer-to-Peer (P2P) energy trading ubiquitously takes place in various scenarios, e.g., microgrids, energy harvesting networks, and vehicle-togrid networks. However, there are common security and privacy challenges caused by untrusted and nontransparent energy markets in these scenarios. To address the security challenges, we exploit the consortium blockchain technology to propose a secure energy trading system named energy blockchain. This energy blockchain can be widely used in general scenarios of P2P energy trading getting rid of a trusted intermediary. Besides, to reduce the transaction limitation resulted from transaction confirmation delays on the energy blockchain, we propose a credit-based payment scheme to support fast and frequent energy trading. An optimal pricing strategy using Stackelberg game for credit-based loans is also proposed. Security analysis and numerical results based on a real dataset illustrate that the proposed energy blockchain and credit-based payment scheme are secure and efficient in IIoT.

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Due to high costs and power consumptions, fully digital baseband precoding schemes are usually prohibitive in millimeter-wave massive MIMO systems. Therefore, hybrid precoding strategies become promising solutions. In this paper, we present a novel real-time yet high-performance precoding strategy. Specifically, the eigenvectors corresponding to the larger eigenvalues of the right unitary matrix after singular value decomposition on an array response matrix are used to abstract the angle information of an analog precoding matrix. As the obtained eigenvectors correspond to the larger singular values, the major phase information of channels is captured. In this way, the iterative search process for obtaining the analog precoding vectors is avoided, and thus the hybrid precoding can be realized in parallel. To further improve its spectral-efficiency, we enlarge the resultant vector set by involving more relevant vectors in terms of their correlation values with the unconstrained optimal precoder, and a hybrid precoder is thus produced by using the vector set. The simulation results show that our proposed scheme achieves near the same performance as the orthogonal matching pursuit does, whereas it costs much fewer complexities than the OMP, and thus can be realized in parallel. INDEX TERMS Millimeter wave communication, MIMO, wireless communication, hybrid precoding. I. INTRODUCTION

Compressed sensing technology is one of the effective techniques to effectively reduce the amount of data transmission in wireless sensor networks. Compressed sensing technology can reduce the amount of data that a node undertakes from n to m, where m n, but we still hope to further reduce the amount of data that the node bears to improve network lifetime. In this paper, a Compressive Sensing based Clustering Joint Annular Routing Data Gathering (CS-CARDG) scheme is proposed to improve the network life. The key technology adopted by CS-CARDG scheme is: data is collected by cluster. The network first forms a cluster, and each node in the cluster sends the data packet to the cluster head. Each cluster forms mdimensional data according to the requirements of the compressed sensing technology to ensure that the data can be recovered. When the cluster head node routes the m-dimensional data to the sink, the CS-CARDG scheme adopts a two-stage routing scheme with the same ring routing and shortest path that is completely different from the previous scheme. The same ring routing means that the cluster heads with the same number of sink hops are routed around the ring for one week to route the compressed sensing data of the same ring to a node in the ring. In this way, each sub-dimension data in the same ring is routed to the corresponding node of each ring through the same-loop route, and then the shortest-circuit strategy of the second phase is started. That is, from the outermost ring, the same fractal data is sequentially compressed from the outside to the inside, and is routed to the sink by the shortest path. In this round of data collection, the number of data packets that the nodes in the near-sink one-hop range bears is only m, and the nodes in the near-sink region directly send data to the sink node, thereby reducing the amount of data that the node bears to m k + 1, where k is the number of nodes within the node's broadcast radius.. In this paper, the compressed sensing strategies proposed in the past are compared by detailed theoretical analysis. The theoretical analysis results show that the CS-CARDG strategy proposed in this paper can effectively reduce the amount of data carried by nodes. This scheme reduces the amount of data in the network from 1Reduced the amount of data by at least 20%. In the network with R = 480 m, the energy utilization rate can reach more than 90%.INDEX TERMS Wireless sensor networks, annular routing, compressed sensing, clustering, energy utilization.The associate editor coordinating the review of this article and approving it for publication was Yuyu Yin. according Ref. [5]. The current sensing-based devices have far exceeded the number of humans, and are growing at a rapid rate. With the development of microprocessor technology [6], the computing and storage capabilities of these sensing-based devices have been greatly