Traditional Chinese Medicine （TCM） electronic medical records face challenges in data mining， low utilization rates， and difficulty in extracting meaningful information due to their complex and diverse structures， as well as non-standard diagnosis and treatment terminology. To address these issues， a TCM electronic medical record classification model called TCM-GCN was proposed based on Linguistically-motivated bidirectional Encoder Representation from Transformer （LERT） pre-training model and Graph Convolutional Network （GCN）， and represented by a heterogeneous graph. The model was used to improve the extraction and classification of effective features in TCM electronic medical records. Firstly， the medical records were converted into sentence vectors using the word embedding method of the LERT layer and integrated into the heterogeneous graph to complement the overall semantic features that were missing in the graph structure. Next， to mitigate the negative impact of the structural characteristics on feature extraction， keywords were added to the nodes of the heterogeneous graph. The BM25 and Pointwise Mutual Information （PMI） algorithms were employed to construct edges representing the features of medical records， such as “medical record - keyword” and “keyword - keyword”. Finally， the task of medical record classification was completed by TCM-GCN， relying on the heterogeneous graph constructed by using LERT-BM25-PMI to aggregate and extract the feature relationships between medical records. Experimental results on the TCM electronic medical record dataset show that， compared to the suboptimal LERT， TCM-GCN achieves improvements of 2.24%， 2.38%， and 2.32% in accuracy， recall， and F1 value， respectively， after applying a weighted average， which confirms the effectiveness of the algorithm in capturing hidden features in medical records and classifying TCM electronic medical records.

Aiming at the energy efficiency and scene adaptability problems of synchronization topology， a Greedy Synchronization Topology algorithm based on Formal Concept Analysis for traffic surveillance based sensor network （GST-FCA） was proposed. Firstly， scene adaptability requirements and energy efficiency model of the synchronization topology in traffic surveillance based sensor network were analyzed. Secondly， correlation analysis was performed on the adjacent features of sensor nodes in the same layer and adjacent layers by using Formal Concept Analysis （FCA）. Afterward， Broadcast Tuples （BT） were built and synchronization sets were divided according to the greedy strategy with the maximum number of neighbors. Thirdly， a backtracking broadcast was used to improve the broadcast strategy of layer detection in Timing-synchronization Protocol of Sensor Network （TPSN） algorithm. Meanwhile， an upward hosting mechanism was designed to not only extend the information sharing range of synchronous nodes but also further alleviate the locally optimal solution problem caused by the greedy strategy. Finally， GST-FCA was verified and tested in terms of energy efficiency and scene adaptability. Simulation results show that compared with algorithms such as TPSN， Linear Estimation of Clock Frequency Offset （LECFO）， GST-FCA decreases the synchronization packet overhead by 11.54%， 24.59% and 39.16% at lowest in the three test scenarios of deployment location， deployment scale and road deployment. Therefore， GST-FCA can alleviate the locally optimal solution problem and reduce the synchronization packet overhead， and it is excellent in energy efficiency when the synchronization topology meets the scene adaptability requirements of the above three scenarios.

Centroid Localization (CL) algorithm is a representative range-free localization algorithm in Wireless Sensor Network (WSN). To improve the localization accuracy of CL when anchors are unevenly distributed, Smallest Enclosing Polygon Localization (SEPL) algorithm was presented. In SEPL, the centroid of the smallest polygon which enclosed the neighbor anchors of the unknown node was regarded as the estimated location of the unknown node. The simulation results show that SEPL is robust when the topology of anchors is not uniform. The proposed algorithm outperforms CL by an average of 15% in localization accuracy.