To reduce the unnecessary overhead of data source authentication in static security mechanism and defend the On-off attack in trust threshold mechanism, an adaptive security mechanism based on trust was proposed in the Internet of Things (IoT). Firstly, the trust evaluation model was built according to node behavior in information interaction, further the measure method for total trust value of nodes was given. Then, for the nodes whose total trust values were higher than the trust threshold, the trust-based adaptive detection algorithm was used to detect the changes of the total trust values of these nodes in real time. Finally, the relay nodes determined whether to authenticate the received message according to the returned result of adaptive detection algorithm. The simulation results and analysis show that the proposed mechanism reduces the energy overhead of relay nodes, and plays a better role in defense against On-off attacks in IoT.

As the source of the haze data, the reliability of the haze monitoring sites is very important to the reliability of the big data. Due to the lack of effective evaluation method for the haze monitoring points, the monitoring data is not reliable enough. In order to solve the problem that the perceived data was not reliable, a kind of perceptual source trust evaluation and selection model was proposed based on the data trigger detection method. When the perceived data arrived, the K-Means clustering algorithm and the statistical results were firstly used to calculate the benchmark data, then the trust degree of data was calculated by using the current perceived data, the benchmark data and the threshold values. Secondly, according to the location of the perceptual source, neighbor relationship was determined. The current perceived data and the data of the neighbors were compared, according to the absolute value of the difference and the value of the threshold, the neighbor recommendation trust degree was calculated. Finally, the comprehensive trust degree was calculated by using the truest degree of perceived data, the historical trust degree and the recommendation trust degree of the neighbor. The initial value of the historical trust was set as the number of monitoring items, and then updated by the comprehensive trust. Theoretical analysis and simulation results prove that the proposed method can effectively evaluate the perceived source, avoid the abnormal data, and reduce post processing overhead.

To resolve the problem of the complex 3D tunnel modeling generated by the changes of the tunnel sections at different rock formations during the project, the 3D tunnel modeling based on the characteristic sections was proposed. Through establishing the characteristic section model library, the 3D modeling of the changed tunnel sections was realized and the efficiency of the modeling of the complex tunnel sections was improved. Following the illustration of the data collection method and an analysis of the characteristic sections with detailed coordinates, the smoothing algorithm (smoothing the tunnel axis at the corner by an arc) of the changed tunnel sections at the corner was proposed. For the ordinary tunnel sections, the triangulation was applied in the 3D modeling; for the complex sections with simple quadrilateral structure, the 3D modeling was realized by using the Bézier surface method and the surface splicing techniques which has been validated through experiments.