The video traffic offloading in edge networks is an effective method for remission of congestion of backward paths in 5G networks by continual optimization of video distribution to promote scale and efficiency of video delivery in edge networks (e.g., D2D-based near-end sharing). Because the video resources are dispersedly cached in local buffer of mobile devices of video users, the management of local video resources of video users in edge networks (e.g., caching and removing of local videos) causes dynamic variation of video distribution in networks. The real-time adjustment of local resources of users in terms of the influence levels (e.g., promotion and recession) of video sharing performance is significant for the continual distribution optimization. In this paper, we propose a novel Social-aware Edge Caching Strategy of Video Resources in 5G Ultra-Dense Network (SECS). SECS designs an estimation method of interest domain of users, which employs the Spectral Clustering to generate initial video clusters and makes use of the Fuzzy C-Means (FCM) to refine the initial video clusters. A user clustering method is proposed, which enables the users with common and similar interests to be clustered into the same groups by estimating similarity levels of interest domain between users. SECS designs a performance-aware video caching strategy, which enables the users intelligently implement management (caching and removing) of local video resources in terms of influence for the intragroup sharing performance. Extensive tests show how SECS achieves much better performance results in comparison with the state-of-the-art solutions.

Aggregate measurement and analysis are critical for civil engineering. Multiple entropy thresholding (MET) is inefficient, and the accuracy of related optimization strategies is unsatisfactory, which results in the segmented aggregate images lacking many surface roughness and aggregate edge features. Thus, this research proposes an autonomous segmentation model (i.e., PERSSA-MET) that optimizes MET based on the chaotic combination strategy sparrow search algorithm (SSA). First, aiming at the characteristics of the many extreme values of an aggregate image, a novel expansion parameter and range-control elite mutation strategies were studied and combined with piecewise mapping, named PERSSA, to improve the SSA’s accuracy. This was compared with seven optimization algorithms using benchmark function experiments and a Wilcoxon rank-sum test, and the PERSSA’s superiority was proved with the tests. Then, PERSSA was utilized to swiftly determine MET thresholds, and the METs were the Renyi entropy, symmetric cross entropy, and Kapur entropy. In the segmentation experiments of the aggregate images, it was proven that PERSSA-MET effectively segmented more details. Compared with SSA-MET, it achieved 28.90%, 12.55%, and 6.00% improvements in the peak signal-to-noise ratio (PSNR), the structural similarity (SSIM), and the feature similarity (FSIM). Finally, a new parameter, overall merit weight proportion (OMWP), is suggested to calculate this segmentation method’s superiority over all other algorithms. The results show that PERSSA-Renyi entropy outperforms well, and it can effectively keep the aggregate surface texture features and attain a balance between accuracy and speed.