Multi-access edge computing (MEC) has been booming in recent years, as it promises to fulfill the growing low-latency requirements of applications on large amounts of Internet of things (IoT) devices. Nevertheless, as latency-sensitive applications tend to become more complicated, existing schemes are too sophisticated, which may result in exceeding the real-time requirements of IoT systems. In this paper, we investigate the task offloading problem for the multi-device multi-edge server IoT system integrated with MEC. Firstly, according to the performance gains obtained by offloading different subtasks, we formalize a system latency minimization problem with energy utilization consideration, which has been proven to be NP-hard. Then, to address it, we propose a heuristic computation offloading scheduling scheme, which offloads appropriate subtasks to edge servers such that the system latency is minimized. Additionally, we theoretically prove the upper and lower boundaries of the system latency. Extensive simulation results corroborate that the proposed algorithm is low-complexity yet effective in decreasing the system latency by 16.15% (and up to 51.18%), improving the energy efficiency of local devices by 8.97% (and up to 21.69%) and shortening the offloading strategy execution time by 96.33% (and up to 98.95%).

Multiview digital surface model (DSM) fusion has emerged as an important technique for 3-D reconstruction of multiview satellite images. However, existing multiview DSM fusion approaches are prone to the problems of blurred elevation divisions at the object edges, salt and pepper noises on smooth surfaces, and severe loss of surface details in weakly textured regions. In this article, we present a cascade domain clustering (CDC) algorithm for fusing multiview DSMs, which is realized by the combination of salient domain clustering and model domain clustering. Initially, the salient domain clustering is employed to demarcate prominent objects and identify regional edges using 2-D spectral and 3-D elevation information. Subsequently, to further segment the intricate surface structures, particularly for objects with low texture attributes, we implement the model domain clustering to iteratively aggregate 3-D points and fit geometric models corresponding to the aggregated clusters. Finally, the multiview DSMs are fused iteratively through the weighted least-squares (WLS) method, with model clusters serving as the fundamental units, under the constraints of the geometric models. Sufficient experiments show that the proposed CDC algorithm surpasses other popular multiview DSM fusion algorithms in terms of completeness and accuracy, achieving 91.51% completeness and 0.93-m root-mean-square error (RMSE), representing a 79.89% improvement in completeness and an 86.40% reduction in RMSE compared to the popular stereo 3-D reconstruction pipeline.

Matched line segments are crucial geometric elements for reconstructing the desired 3D structure in stereo satellite imagery, owing to their advantages in spatial representation, complex shape description, and geometric computation. However, existing line segment matching (LSM) methods face significant challenges in effectively addressing co-linear interference and the misdirection of parallel line segments. To address these issues, this study proposes a “continuous–discrete–continuous” cyclic LSM method, based on the Voronoi diagram, for stereo satellite images. Initially, to compute the discrete line-point matching rate, line segments are discretized using the Bresenham algorithm, and the pyramid histogram of visual words (PHOW) feature is assigned to the line segment points which are detected using the line segment detector (LSD). Next, to obtain continuous matched line segments, the method combines the line segment crossing angle rate with the line-point matching rate, utilizing a soft voting classifier. Finally, local point-line homography models are constructed based on the Voronoi diagram, filtering out misdirected parallel line segments and yielding the final matched line segments. Extensive experiments on the challenging benchmark, WorldView-2 and WorldView-3 satellite image datasets, demonstrate that the proposed method outperforms several state-of-the-art LSM methods. Specifically, the proposed method achieves F1-scores that are 6.22%, 12.60%, and 18.35% higher than those of the best-performing existing LSM method on the three datasets, respectively.