Link prediction focuses on the design of prediction algorithms that can describe a given network mechanism more accurately to achieve the prediction result with higher accuracy. Based on an analysis of the existing research achievements, it is found that the similarity characteristics of a network has a great impact on the link prediction method used. In networks with low tag similarity between nodes, increasing the tag similarity is able to improve the prediction accuracy; in networks with high tag similarity between nodes, more attention should be paid to the contribution of structural information to link prediction to improve the prediction accuracy. Then, a tag-weighted similarity algorithm was proposed by weighting the tags, which was able to improve the accuracy of link prediction in networks with low similarity. Meanwhile, in networks with relatively high similarity, the structural information of the network was introduced into the node similarity calculation, and the accuracy of link prediction was improved through the preferential attachment mechanism. Experimental results on four real networks show that the proposed algorithm achieves the highest accuracy compared to the comparison algorithms Cosine Similarity between Tag Systems (CSTS), Preferential Attachment (PA), etc. According to the network similarity characteristics, using the proposed corresponding algorithm for link prediction can obtain more accurate prediction results.

Concerning the low energy efficiency and network energy imbalance of cluster head in Wireless Sensor Network (WSN), a sensor network clustering algorithm with Clustering Time Span Optimization (CTSO) was proposed. Firstly, the constraints within the cluster membership and cluster head spacing in cluster head election was considered to avoid overlapping between the various clusters as much as possible and optimize the energy of the cluster nodes. Secondly, the cluster head election cycle was optimized and divided into rounds by considering the task excution cycle as time span, by minimizing the cluster head election rounds, the cost for selecting cluster heads and the energy for broadcasting messages were reduced, and energy utilization of cluster nodes was improved. Simulation results showed that, compared to the homogeneous state data routing scheme based on multiple Agents and adaptive data aggregation routing policy, the average energy efficiency of CTSO was increased by 62.0% and 138.4% respectively, and the node life was increased by 17% and 9 % respectively. CTSO algorithm has a good effect on promoting the energy efficiency of cluster head node and balancing the energy of nodes in WSN.

Nowadays, as low carbon economy has been advocated worldwide, the power consumption of embedded software has become a critical factor in embedded system design. The hardware/software partitioning is an important method of embedded software power optimization. Firstly, this paper constructed a hardware/software bi-partitioning model with the goal of embedded software power consumption under the constraints of performance; then, a hybrid algorithm was proposed based on the fusion of discrete Hopfield Neural Network (HNN) and Tabu Search (TS), in which HNN as the main method could quickly obtain a feasible solution of partitioning, and the TS algorithm could "taboo" the current solution and transferred to the other minimum points that could jump out from the local optimal solution. Lastly, the experimental results show that the proposed algorithm posses better time performance and higher probability of acquiring the global optimal solution in contrast with other similar algorithms.