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-to-grid 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.

Cloud-edge collaboration can overcome the shortcomings of high latency of the centralized model of cloud computing and insufficient computing power of edge computing, and better meet the needs of various application scenarios, which has become a research hotspot in recent years. The 6 G network is driven by the deep integration of network and computing, which will further accelerate the collaborative and integrated development of the cloud datacenter, edge servers, end devices, and application. This paper considers the combination of edge computing and cloud computing, and studies the problem of task offloading with the optimization objectives of energy consumption, delay, and multi-node load balancing under an “End-Edge-Cloud” collaborative architecture for 6G network. We regard the problem of task offloading as a multi-objective optimization problem. By improving AR-MOEA, a task offloading algorithm based on the “End-Edge-Cloud” architecture (TO-EEC) is proposed. It is verified through experiments that TO-EEC has a fast convergence speed. Compared with other similar offloading algorithms, TO-EEC has significant optimization effects on energy consumption, delay and multi-node load variance under multiple constraints.

Due to high costs and power consumptions, fully digital baseband precoding schemes are usually prohibitive in millimeter-wave massive MIMO systems. Therefore,hybrid pre-coding strategies become the promising solutions. In this paper, we present a novel real-time yet high performance precoding strategy. Speci?cally, the eigenvectors corresponding to the larger eigenvalues of the right unitary matrix after singular value decomposition (SVD) 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 hybrid precoding can be realized in parallel.To further improve its spectral-ef?ciency, we enlargetheresultantvectorsetbyinvolving morerelevantvectors in termof their correlationvalues withthe unconstrained optimal precoder, and a hybrid precoder is thus produced by using the vector set. Simulation results show that our proposed scheme achieves near the same performance as the orthogonal matching pursuit (OMP) does, whereas it costs much less complexities than the OMP, and thus can be realized in parallel.

UAVs-enabled Mobile Crowdsensing (UMCS) has gained considerable attention recently, but it is challenging to meet the data collection needs of the entire city using only the UAV with limited energy. Furthermore, how to effectively minimize Age-of-Information (AoI) and ensure data quality has not been well solved in previous studies. Therefore, this paper proposes a hybrid optimization framework for AoI minimization, which recruits massive distributed workers as the main force for data collection, while the UAV acts as a data collection collaborator and is more inclined to fly to the SNs that cannot establish connections with workers, To mitigate the potential security threats incurred by dishonest workers of the MCS system, we first provide a Greedy-based Multi-worker Task Assignment (GMTA) strategy, aiming to assign more urgent data collection tasks to reliable workers under workload constraints. Then, we propose a Deep-Reinforcement-Learning-based Global AoI Minimization (DRL-GAM) strategy for the UAV path planning to find a set of optimal actions to minimize the global AoI. Based on the real dataset, our simulation experiments show that compared with traditional strategies, our DRL-GAM strategy can reduce the global AoI by an average of 6.49% $\sim$ 68.21% in various network sizes, and is more stable for the average standard deviation is only 51.75% of other strategies.

Field Programmable Gate Array (FPGA) has become an efficient accelerator for convolutional neural network (CNN) inference due to its high performance and flexibility. To further improve the performance of CNN inference on FPGAs, an Intellectual Property core (IP core) called Deep Learning Processor Unit (DPU) is released by Xilinx. Unlike previous FPGA-based hardware designs focusing on specific functions or CNNs, the DPU IP supports ample basic functions of deep learning, and the developers can take advantage of DPUs to accelerate CNN inference conveniently. In DPU-based CNN acceleration platform, an encapsulated scheduler plays a crucial role in task scheduling between heterogeneous ARM and multiple DPUs. However, the current scheduler is unsatisfactory because its low schedule efficiency. This paper thus presents a high performance task assignment framework built upon Xilinx hybrid CPU-FPGA MPSoC devices. We first evaluate the main causes of low schedule efficiency problem. Then, we explore the scheduler rules and improve shedule efficiency through purposeful observations and analysis. Finally, we integrate our optimizations, and propose an efficient task assignment framework to maximize performance on DPU-based CNN acceleration platform. Experimental results on Xilinx Zynq UltraScale+ MPSoC zcu104 show that our efficient task assignment framework significantly boosts schedule efficiency for small-scale CNNs (from 36% to 70%), medium-scale CNNs (from 65% to 95%), and large-scale CNNs (from 77% to 99%) compared with original schedule strategy.