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Researcher profile

Zhiyu Wang

Zhiyu Wang contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Distributed, Parallel, and Cluster Computingmath.COComputer VisionDiscrete Mathematics

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Published work

8 published item(s)

preprint2026arXiv

Performance and Security Aware Distributed Service Placement in Fog Computing

The rapid proliferation of IoT applications has intensified the demand for efficient and secure service placement in Fog computing. However, heterogeneous resources, dynamic workloads, and diverse security requirements make optimal service placement highly challenging. Most solutions focus primarily on performance metrics while overlooking the security implications of deployment decisions. This paper proposes a Security and Performance-Aware Distributed Deep Reinforcement Learning (SPA-DDRL) framework for joint optimization of service response time and security compliance in Fog computing. The problem is formulated as a weighted multi-objective optimization task, minimizing latency while maximizing a security score derived from the security capabilities of Fog nodes. The security score features a new three-tier hierarchy, where configuration-level checks verify proper settings, capability-level assessments evaluate the resource security features, and control-level evaluations enforce stringent policies, thereby ensuring compliant solutions that align with performance objectives. SPA-DDRL adopts a distributed broker-learner architecture where multiple brokers perform autonomous service-placement decisions and a centralized learner coordinates global policy optimization through shared prioritized experiences. It integrates three key improvements, including Long Short-Term Memory networks, Prioritized Experience Replay, and off-policy correction mechanisms to improve the agent's performance. Experiments based on real IoT workloads show that SPA-DDRL significantly improves both service response time and placement security compared to current approaches, achieving a 16.3% improvement in response time and a 33% faster convergence rate. It also maintains consistent, feasible, security-compliant solutions across all system scales, while baseline techniques fail or show performance degradation.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

ReinFog: A Deep Reinforcement Learning Empowered Framework for Resource Management in Edge and Cloud Computing Environments

The growing IoT landscape requires effective server deployment strategies to meet demands including real-time processing and energy efficiency. This is complicated by heterogeneous, dynamic applications and servers. To address these challenges, we propose ReinFog, a modular distributed software empowered with Deep Reinforcement Learning (DRL) for adaptive resource management across edge/fog and cloud environments. ReinFog enables the practical development/deployment of various centralized and distributed DRL techniques for resource management in edge/fog and cloud computing environments. It also supports integrating native and library-based DRL techniques for diverse IoT application scheduling objectives. Additionally, ReinFog allows for customizing deployment configurations for different DRL techniques, including the number and placement of DRL Learners and DRL Workers in large-scale distributed systems. Besides, we propose a novel Memetic Algorithm for DRL Component (e.g., DRL Learners and DRL Workers) Placement in ReinFog named MADCP, which combines the strengths of Genetic Algorithm, Firefly Algorithm, and Particle Swarm Optimization. Experiments reveal that the DRL mechanisms developed within ReinFog have significantly enhanced both centralized and distributed DRL techniques implementation. These advancements have resulted in notable improvements in IoT application performance, reducing response time by 45%, energy consumption by 39%, and weighted cost by 37%, while maintaining minimal scheduling overhead. Additionally, ReinFog exhibits remarkable scalability, with a rise in DRL Workers from 1 to 30 causing only a 0.3-second increase in startup time and around 2 MB more RAM per Worker. The proposed MADCP for DRL component placement further accelerates the convergence rate of DRL techniques by up to 38%.

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preprint2026arXiv

Report of the 5th PVUW Challenge: Towards More Diverse Modalities in Pixel-Level Understanding

This report summarizes the objectives, datasets, and top-performing methodologies of the 2026 Pixel-level Video Understanding in the Wild (PVUW) Challenge, hosted at CVPR 2026, which evaluates state-of-the-art models under highly unconstrained conditions. To provide a comprehensive assessment, the 2026 edition features three specialized tracks: the MOSE track for tracking objects within densely cluttered and severely occluded scenarios; the MeViS-Text track for localizing targets via motion-focused linguistic expressions; and the newly inaugurated MeViS-Audio track, which pioneers acoustic-driven object segmentation. By introducing previously unreleased challenging data and analyzing the cutting-edge, multimodal solutions submitted by participants, this report highlights the community's latest technical advancements and charts promising future directions for robust video scene comprehension.

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preprint2022arXiv

Container Orchestration in Edge and Fog Computing Environments for Real-Time IoT Applications

Resource management is the principal factor to fully utilize the potential of Edge/Fog computing to execute real-time and critical IoT applications. Although some resource management frameworks exist, the majority are not designed based on distributed containerized components. Hence, they are not suitable for highly distributed and heterogeneous computing environments. Containerized resource management frameworks such as FogBus2 enable efficient distribution of framework's components alongside IoT applications' components. However, the management, deployment, health-check, and scalability of a large number of containers are challenging issues. To orchestrate a multitude of containers, several orchestration tools are developed. But, many of these orchestration tools are heavy-weight and have a high overhead, especially for resource-limited Edge/Fog nodes. Thus, for hybrid computing environments, consisting of heterogeneous Edge/Fog and/or Cloud nodes, lightweight container orchestration tools are required to support both resource-limited resources at the Edge/Fog and resource-rich resources at the Cloud. Thus, in this paper, we propose a feasible approach to build a hybrid and lightweight cluster based on K3s, for the FogBus2 framework that offers containerized resource management framework. This work addresses the challenge of creating lightweight computing clusters in hybrid computing environments. It also proposes three design patterns for the deployment of the FogBus2 framework in hybrid environments, including 1) Host Network, 2) Proxy Server, and 3) Environment Variable. The performance evaluation shows that the proposed approach improves the response time of real-time IoT applications up to 29% with acceptable and low overhead.

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preprint2020arXiv

Drosophila-Inspired 3D Moving Object Detection Based on Point Clouds

3D moving object detection is one of the most critical tasks in dynamic scene analysis. In this paper, we propose a novel Drosophila-inspired 3D moving object detection method using Lidar sensors. According to the theory of elementary motion detector, we have developed a motion detector based on the shallow visual neural pathway of Drosophila. This detector is sensitive to the movement of objects and can well suppress background noise. Designing neural circuits with different connection modes, the approach searches for motion areas in a coarse-to-fine fashion and extracts point clouds of each motion area to form moving object proposals. An improved 3D object detection network is then used to estimate the point clouds of each proposal and efficiently generates the 3D bounding boxes and the object categories. We evaluate the proposed approach on the widely-used KITTI benchmark, and state-of-the-art performance was obtained by using the proposed approach on the task of motion detection.

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preprint2020arXiv

On Hamiltonian Berge cycles in $3$-uniform hypergraphs

Given a set $R$, a hypergraph is $R$-uniform if the size of every hyperedge belongs to $R$. A hypergraph $\mathcal{H}$ is called \textit{covering} if every vertex pair is contained in some hyperedge in $\mathcal{H}$. In this note, we show that every covering $[3]$-uniform hypergraph on $n\geq 6$ vertices contains a Berge cycle $C_s$ for any $3\leq s\leq n$. As an application, we determine the maximum Lagrangian of $k$-uniform Berge-$C_{t}$-free hypergraphs and Berge-$P_{t}$-free hypergraphs.

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preprint2020arXiv

Saturation problems in the Ramsey theory of graphs, posets and point sets

In 1964, Erdős, Hajnal and Moon introduced a saturation version of Turán's classical theorem in extremal graph theory. In particular, they determined the minimum number of edges in a $K_r$-free, $n$-vertex graph with the property that the addition of any further edge yields a copy of $K_r$. We consider analogues of this problem in other settings. We prove a saturation version of the Erdős-Szekeres theorem about monotone subsequences and saturation versions of some Ramsey-type theorems on graphs and Dilworth-type theorems on posets. We also consider semisaturation problems, wherein we allow the family to have the forbidden configuration, but insist that any addition to the family yields a new copy of the forbidden configuration. In this setting, we prove a semisaturation version of the Erdős-Szekeres theorem on convex $k$-gons, as well as multiple semisaturation theorems for sequences and posets.

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preprint2018arXiv

On difference graphs and the local dimension of posets

The dimension of a partially-ordered set (poset), introduced by Dushnik and Miller (1941), has been studied extensively in the literature. Recently, Ueckerdt (2016) proposed a variation called local dimension which makes use of partial linear extensions. While local dimension is bounded above by dimension, they can be arbitrarily far apart as the dimension of the standard example is $n$ while its local dimension is only $3$. Hiraguchi (1955) proved that the maximum dimension of a poset of order $n$ is $n/2$. However, we find a very different result for local dimension, proving a bound of $Θ(n/\log n)$. This follows from connections with covering graphs using difference graphs which are bipartite graphs whose vertices in a single class have nested neighborhoods. We also prove that the local dimension of the $n$-dimensional Boolean lattice is $Ω(n/\log n)$ and make progress toward resolving a version of the removable pair conjecture for local dimension.

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