Aiming at the shortcoming of large access delay in industrial Internet of Things （IoT）， a sink location algorithm of Power Domain NonOrthogonal Multiple Access （PD-NOMA） for real-time industrial IoT was proposed. In this algorithm， based on the PD-NOMA technology， the location of the sink was used as an optimization method to minimize access delay by realizing power division multiplexing among users as much as possible. Firstly， for any two users， an assertion that the decodable area of the qualified sink must be a circle if parallel transmissions are successful was proven， and therefore， the decodable area set of the sink was able to be obtained by combining all of the combinations of two users， and every minimal intersection of the area set must be a convex region. So， the optimal location of the sink must be included in these minimal intersection areas. Secondly， for each minimal intersection area where the sink was deployed， the minimum number of chain partition of the network generation graph in the area was computed and used as the metric for evaluating the access delay. Finally， the optimal location of the sink was determined by comparing these minimum number of chain partitioning. Experimental results show that when the decoding threshold is 2 and the number of users is 30， the average access delay of the proposed algorithm is about 36.7% of that of the classic time division multiple access， and besides， it can be decreased almost linearly with the decrease of the decoding threshold and the increase of the channel decay factor. The proposed algorithm can provide reference from the access layer perspective for massive ultra-reliable low-latency communications.

In real life, many data samples of systems can be easily obtained, but only a small part of accurate laabels can be obtained. In order to obtain a better classification learning model, a semi-supervised classification algorithm based on weight diversity was proposed by introducing semi-supervised learning and improving Unlabeled Data to Enhance Ensemble Diversity (UDEED), namely UDEED ⁺. In UDEED ⁺, based on the prediction disagreement of unlabeled data by base learners, the loss of weight diversity was proposed. The disagreement between base learners was represented by the cosine similarity of the weights of base learners. The diversity of model was fully expanded from different perspectives of loss function, and the unlabeled data were used to encourage the diversity representation of ensemble learners in the process of model training, so as to improve the performance and generalization of the classification learning model. Comparative experiments were conducted on 8 UCI public datasets with semi-supervised algorithms of UDEED algorithm, Safe Semi-Supervised Support Vector Machine (S4VM) and Semi-Supervised Weak-Label (SSWL). Compared with UDEED, UDEED ⁺ has the accuracy and F1 score improved by 1.4 percentage points and 1.1 percentage points respectively; compared with S4VM, UDEED ⁺ has the accuracy and F1 score improved by 1.3 percentage points and 3.1 percentage points respectively; compared with UDEED, UDEED ⁺ has the accuracy and F1 score improved by 0.7 percentage points and 1.5 percentage points respectively. Experimental results illustrate that the increase of weight diversity can improve the classification performance of the model, verifying its positive effect on the improvement of the classification performance of UDEED ⁺.

In the container terminal system,the effective quay crane assignment for vessels is helpful to ease the strain that berths and quay cranes are in short supply in container terminals and improve the operational efficiency of ports. Aiming at the integrated optimization problem of berth allocation and quay crane assignment of dynamic arriving vessels under continuous berth,the quay crane assignment for vessels was dynamically adjusted based on ship efficiency,a model with the purpose of minimizing the total cost containing delayed berthing cost,preference deviated berthing cost,delayed departure cost and quay crane reassignment cost was established,and a heuristic algorithm based on the adjustment rules of quay crane assignment was designed and Genetic Algorithm(GA) was used to solve the model. Finally,the experimental results verified the effectiveness of the proposed model and algorithm in solving the problem of berth allocation and quay crane assignment in actual ports,and by comparing with the results calculated by the traditional GA,the optimization effect of the proposed algorithm was proved.

Concerning the problem brought by a large number of spam, an incremental passive attack learning algorithm was proposed. The passive attack learning method was based on the Simple Mail Transfer Protocol (SMTP) session log initiated by the email host in the campus during half a year. Analysis on the status of delivery rate and many types of failure message of the host behavior in the session record was conducted, and the effective adaptation was ultimately achieved by detecting spam source host behavior on the recent email classification. The experimental results show that after implementing several rounds of classification strategy adjustment, the detection accuracy of the proposed model can reach 94.7%. The design is very useful to effectively detect internal spam host and control the spam from the source.