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The industrial internet of things (IIoT) has great potential in achieving automation and intelligent production. However, existing networks struggle to meet the low-latency and high-reliability communication requirements in industrial control scenarios. Motivated by this fact, the power allocation problem of superimposed pilot (SP) for downlink short-packet transmission in IIoT was studied and a lower bound for achievable transmission rates with imperfect channel state information and maximum ratio transmission was derived. Furthermore, the downlink weighted sumrate maximization problem was formulated and transformed into a geometric programming problem aiming to optimize pilot and data power allocation via successive convex approximation method. Simulation results demonstrate the superiority of the proposed power optimization scheme of SP in short-packet transmission.

Considering the malicious risks associated with diverse terminals in the industrial Internet of things (IIoT), a practical Byzantine fault tolerant (PBFT) efficient consensus algorithm based on credit score and dynamic double layer (CD-PBFT) was proposed to meet the requirements of high efficiency and security fault tolerance in the consensus process. The participation of good nodes in the consensus and the removal of faulty nodes were ensured by the credit score model. The parallelism of transaction verification and read write operations was achieved through the implementation of a double layer architecture. Nodes with high credit were randomly selected as the master node by the adaptive master node algorithm, ensuring its security. Experimental results show that CD-PBFT not only can maintain the safety and liveness of the consensus algorithm but also can reduce network delay by 34.8% and increase throughput by 25.2% compared with PBFT, which meets the double requirements of efficiency and security fault tolerance.

In the industrial Internet of things, the generation of tasks is observed to exhibit both continuity and periodicity, along with stringent latency requirements. These characteristics posed challenges to system's cost-efficiency. To address these challenges, a cost minimization resource allocation algorithm based on the urgency of tasks was proposed. By employing a genetic algorithm, the task offloading strategy and the system's resource allocation strategy were optimized. For offloaded tasks, they were scheduled according to their level of urgency. Additionally, the optimal transmission power for each task was calculated to meet latency constraints. Simulation results indicate that the proposed algorithm effectively reduces the overall energy cost of the system.

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In the age of 6G internet of things, massive terminal devices are connected intelligently to explore environmental information to improve life quality and create a more intelligent world.With the development of economic society and industry, the number of industrial nodes in the industrial internet of things (IIoT) grows at an significant speed.The real-time transmission and exchange of information between these industrial nodes puts forward higher requirements for communication delay and reliability.Under limited time-frequency resources and strict communication quality requirements, in order to ensure ultra-reliable and low-latency communication (URLLC) of massive terminals in IIoT, it is necessary to analyze the existing key problems from the aspects of low delay, reliability, energy efficiency, spectrum efficiency and scalability.In the scenario of IIoT, combined with the characteristics and requirements of URLLC, the key issues of communication in IIoT were studied, including challenges and opportunities in active user detection, random access, resource allocation and convergence of communication and computation.After summarizing and analyzing the key issues and existing technologies, the future research direction was proposed.

The unprecedented prosperity of the industrial internet of things (IIoT) has opened up a new path for the traditional industrial manufacturing model.Intelligent shop scheduling is one of the key technologies to achieve the overall control and flexible production of the whole production process.It requires an effective plan with a minimum makespan to allocate multiple processes and multiple machines for production scheduling.Firstly, the shop scheduling problem was defined as a Markov decision process (MDP), and a shop scheduling model based on the pointer network was established.Secondly, the job scheduling process was regarded as a mapping from one sequence to another, and a new shop scheduling algorithm based on deep reinforcement learning (DRL) was proposed.By analyzing the convergence of the model under different parameter settings, the optimal parameters were determined.Experimental results on different scales of public data sets and actual production data sets show that the proposed DRL algorithm can obtain better performances.

In view of the fact that most of the existing range query schemes for fog-enhanced IoT cannot achieve both multi-dimensional query and privacy protection,a privacy-preserving multi-dimensional secure query scheme for fog-enhanced IIoT was proposed.Firstly,the multiple ranges to be queried were mapped into a certain query matrix.Then,auxiliary vectors were constructed to decompose the query matrix,and then the auxiliary vector was processed by BGN homomorphic encryption to form a query trapdoor.Finally,with the homomorphic computation utilized by an IoT device terminal,the query trapdoor could be matched to its sensor data.Spatial complexity could be effectively reduced with the used specific auxiliary vectors.The confidentiality of sensor data could be ensured and the privacy of user’s query mode could be protected by the homomorphic encryption property.Experiments results show that the computational and communication costs are relatively low.

The industrial Internet of things makes full use of sensors,computer networks,big data process and other technologies to solve the problem of “information island” caused by the closed operation of traditional industrial systems,enabling the rapid and convenient data exchange between systems.How to establish high quality and low bandwidth data exchange mode in industrial field and cloud is a big challenge to realize intelligent manufacturing.MQTT protocol is widely used in the Internet of things system because of its lightweight and flexible configuration.MQTT-based industrial Internet of things communication system was built to realize multi-source and heterogeneous data exchange between cloud and industrial field,remote monitoring of device status,user service subscription and other functions,so that the performance and related applications of MQTT protocol in industrial Internet of things communication system could be studied.

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