Coded caching has emerged as an effective technique to alleviate network congestion. While the individual impact of user cache sizes or file popularity has been investigated independently, their joint impact on coded caching remains unclear. In this paper, we first characterize two information-theoretic lower bounds on the expected transmission rate. The first bound introduces a novel cut-set method that allows users to repeatedly request files at different frequencies, accounting for the heterogeneity of user cache sizes and file popularity. The second bound is derived from the traditional cut-set bound, assuming each file is requested at most once. Then a group-based multi-round decentralized coded caching scheme is introduced. We show that the expected transmission rate for this scheme is at most anO(logK) factor away from the second lower bound, applicable to any cache and popularity distributions, whereKis the number of users. Additionally, by combining with the first lower bound, a tighterO(logKmK/m1) gap between the upper and lower bounds is derived for power function and Zipf cache distributions, wherem1andmKrepresent the minimum and maximum cache sizes, respectively. Simulation results show the superior performance of our proposed scheme.

Coded caching has emerged as a promising technique to alleviate traffic congestion by strategically creating coded multicasting opportunities, even for caches with different demands. For a two-layer cache-aided hierarchical network consisting of a central server, multiple helpers, and multiple users, prior works have characterized the fundamental performance limits of coded caching for this system with the constraint of a uniform user distribution (i.e., each helper serves an equal number of users). However, when the heterogeneity of user distribution is taken into account, there remain open questions. In this article, we consider a two-layer cache-aided hierarchical network with arbitrary user distributions, where a central server is connected via an error-free link to multiple helpers and each user can randomly access one helper. We introduce a new decentralized coded caching scheme and employ the cut-set technique to characterize lower bounds. Our results show that the gap between the upper and lower bound of the achievable rate from server to helpers is within a constant multiplicative (i.e., [1/32]) and additive (i.e., 2) factor, outperforming prior works under uniform user distribution. Moreover, we also show that the transmission rate from each helper to its attached users is at most a constant factor away from the corresponding lower bound. To our knowledge, this is the first work in hierarchical networks to eliminate the additive gap of the second layer. Finally, simulation results demonstrate the superiority of the proposed caching scheme compared with the state of the art.

Accurate beach topography mapping is crucial for understanding coastal dynamics and mitigating climate change impacts. However, traditional methods such as airborne LiDAR have limitations, leading to substantial gaps in national-scale elevation data. This study presents an innovative framework to reconstruct missing elevation data along New Zealand’s coastline by integrating airborne LiDAR, Sentinel-2 optical imagery, and geometric features (distance) using machine learning methods. Our results show that Artificial Neural Network (ANN) emerged as the best model (test set: R²=0.79, RMSE=0.91 m; validation set: 0.79, RMSE=0.93 m), outperforming other models in accuracy. The produced 10-m DEM for national-scale sandy beaches expands area coverage by 286.6% (114.15 km²), filling gaps in 1249 beaches, including remote areas such as Stewart Island. This novel framework offers a scalable solution for improving the comprehensiveness and accuracy of beach topography. It provides essential support for inundation prediction, habitat management, and the development of climate adaptation strategies, thereby facilitating more informed decision-making in coastal zone management and climate change mitigation efforts.

This paper proposes a replacement algorithm for file caching in mobile edge computing (MEC) networks. While there are numerous schemes for file replacement, it remains a challenge to achieve good, robust, and predictable performance simultaneously. To address this challenge, we introduce a general scheme called Incremental Least-Recently-Used (iLRU), which builds on the classic Least-Recently-Used (LRU) algorithm. iLRU initially caches only a “portion” of the file upon the first request and incrementally caches more when there are more requests for the file. In this regard, the request frequency can be inferred from the cached size without incurring additional overhead, where a larger cached size represents a higher request frequency. We derive the theoretical hit ratio of iLRU based on the Time-to-Live (TTL) analysis. With the Time-to-Live (TTL) analysis, we can theoretically derive the hit ratio and properties of iLRU and notably show that iLRU allocates more cache space to popular files, resulting in a higher hit ratio than LRU. Simulation results demonstrate the superior performance of iLRU and validate the accuracy of the theoretical hit ratio. Furthermore, we conduct simulations over various real-world traces to show that iLRU outperforms existing schemes across various real-world traces, defenestrating the robustness of iLRU.

Accurate topography of intertidal mudflats of a fine resolution is fundamental data to further understand the coastal process and achieve targeted coastal management. However, the mapping of mudflat topography is hindered by poor accessibility of muddy environments and a short observation time window caused by periodic tides. Commonly as a revolutionary technique owing to its low cost, flexibility, and quality data, the unmanned aerial vehicle (UAV)-based SfM photogrammetry has been widely applied in coastal areas. The conventional UAV photogrammetric accuracy significantly depends on the number and distribution of ground control points (GCPs), limiting its mapping efficiency. With the increasingly available UAVs with onboard RTK, photogrammetry without GCPs is becoming a promising alternative. However, the ability of this advanced RTK-assisted UAV to capture centimeter-scale elevation changes in intertidal mudflats still remains unclear. For this reason, this paper aims to evaluate the potential of RTK-assisted UAVs in quantifying intertidal topographic changes. The results showed that the RTK-assisted UAV structure-from-motion (SfM) photogrammetry without GCPs could accurately capture fine-scale topographical features such as mudflat gradient and creeks with root-mean-squared errors (RMSE) of ± 3.3 cm, ± 2.8 cm, and ± 4.7 cm on X-, Y-, and vertical directions, respectively. Therefore, this study identified that RTK-assisted UAV photogrammetry could be used to quantify intertidal mudflat morphodynamics and calculate sediment deposition volume within the uncertainty in a more cost-effective way.