It is extremely important to build a reasonable traffic network structure for traffic flow prediction. Owing to the complexity and dynamic of traffic networks, the graph neural network model has become one of the most effective methods for mining the spatial-temporal relationship between traffic flow data. However, most current methods use two components to extract the spatial dependence and time dependence separately and do not consider the auxiliary effect of additional traffic factors on the prediction target. Based on the above problems, this paper proposes a neural network prediction model for a comprehensive spatial-temporal synchronous graph based on information aggregation. The model is composed of a fusion feature attention module, an information aggregation module, and a comprehensive information integration framework. The fusion feature attention module considers the impact of each traffic factor on the traffic flow and strengthens the internal relationship of various traffic features; the information aggregation module synchronously extracts the temporal-spatial dependence of traffic flow; The multiinformation combination module combines the traffic flow with the secondary information to mine the hidden relationship between the primary and secondary information. The experimental results on two realworld datasets show that the prediction effect of the model set out in the present paper is significantly better than that of the baseline.

No abstract

No abstract

No abstract

Traditional range query methods of work still have shortcomings in node energy consumption and privacy security, so a two-layer secure and efficient range query method for wireless sensor networks is proposed. In the data storage stage, the sensing node obtains the data ciphertext and timestamp by the Advanced Encryption Standard encryption algorithm, receives the new encryption constraint chain by the reverse 0-1 encoding method and Hashbased Message Authentication Code encryption algorithm, and sends the chain to the storage node. In the query response phase, the storage node responds to the request of the base station and sends the data that meet the query requirements. After receiving the data, the base station verifies the consistency with the new encryption constraint chain and timestamp. During the experiment, the energy consumption is analysed from three aspects: the number of data collected in the period, the data length of the sensing node and the partition factor of the encryption constraint chain. The results show that this method has low energy consumption and can maintain the consistency of data.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.