The paper introduces PLATOS, a scheduling strategy for Healthcare IoT in fog computing that balances power consumption and latency. PLATOS classifies tasks into priority, storage, and computational categories, then selects fog resources to minimize delay and energy use before allocating high‑performance nodes to high‑priority tasks. Simulations show an 18.72% reduction in energy use and an 8.65% decrease in latency compared to existing methods.
arXiv:2606. 24340v1 Announce Type: new Abstract: In recent years, the Internet of Things (IoT) paradigm has been shifting toward batteryless, energy-harvesting architectures.
arXiv:2511.11628v2 Announce Type: replace-cross Abstract: Modern operating system process schedulers typically enforce a static, ``one-policy-fits-all'' design, which inevitably compromises performan...
PPO-STGNN is a DAG task‑scheduling algorithm that combines proximal policy optimization with spatio‑temporal graph neural networks. It extracts features from both the task topology and the heterogeneous cloud‑edge‑end resource graph, then optimizes scheduling to reduce makespan and schedule length ratio while balancing CPU and memory loads. A multi‑teacher behavior‑cloning pretraining step accelerates convergence, and experiments show significant load‑balancing improvements with low completion times in dynamic, heterogeneous environments.
The paper introduces a SAREF-compliant ontology designed to represent distributed AI workflows across edge, fog, and cloud environments. It extends the SAREF4SYST ontology with concepts for AI pipelines, executable jobs, resources, deployment constraints, and communication links, creating a unified semantic model for both AI workflows and heterogeneous infrastructures. Evaluation through smart‑grid energy service scenarios and competency questions demonstrates successful deployment, reasoning, and workload adaptation, achieving 90‑100% deployment success and sub‑80 ms orchestration times.
In recent years, the Internet of Things (IoT) paradigm has been shifting toward batteryless, energy-harvesting architectures. Sustaining reliable operation in these systems requires intelligent management of highly volatile stored energy.
The paper introduces a dynamic framework for partitioning neural network layers across a heterogeneous edge‑cloud continuum, adapting to runtime changes in network conditions and device capabilities. It profiles models at startup, measures link quality, and periodically re‑evaluates the partitioning to optimize performance. Experiments on a Raspberry Pi, laptop, and desktop using VGG16, AlexNet, and MobileNetV2 demonstrate energy savings of 27.09–35.82% and latency reductions of 6.34–22.92% over static partitioning.
arXiv:2607. 26602v1 Announce Type: cross Abstract: The rapid development of the Internet of Everything (IoE) is accelerating the adoption of intelligent applications.
arXiv:2607. 20490v1 Announce Type: new Abstract: Edge Intelligence has emerged as a key paradigm for enabling real-time applications in smart cities by shifting computation from centralized cloud data centers to the network edge, thereby reducing latency and bandwidth consumption.
PPO-STGNN is a DAG task‑scheduling algorithm that combines proximal policy optimization with spatio‑temporal graph neural networks to address the NP‑hard scheduling problem in heterogeneous cloud‑edge‑end environments. It extracts features from both the DAG task topology and the physical resource graph, then optimizes the scheduling policy to minimize makespan and schedule length ratio while improving CPU and memory load balancing. A multi‑teacher behavior‑cloning pretraining step accelerates convergence, and experiments demonstrate significant load‑balancing gains with low completion times in dynamic, heterogeneous settings.
The paper introduces Workload Aware Column Imprint-Hash Join (WACI‑HJ), a new edge query processing technique that predicts upcoming workloads to pre‑compute hash bins, reducing unnecessary cacheline scans. Compared to the prior CI‑HJ method, WACI‑HJ cuts cacheline reads by 54% and improves query execution time by 10%, while also delivering measurable gains in CPU, RAM, and I/O energy efficiency. Experiments on benchmark and Smart Transportation datasets demonstrate its effectiveness for both scaled and skewed data, enabling faster real‑time traffic analysis and congestion management.
MANE is a distributed inference framework that uses a multi‑path tail architecture to allow dynamic accuracy–throughput trade‑offs during edge onloading of deep neural networks. It introduces a novel multi‑path model, a three‑stage training scheme with Joint Head Network Distillation loss, and a hysteresis‑based scheduler with an equitable device‑fallback policy. The system achieves over 80% SLO satisfaction and 6pp higher accuracy than on‑device alternatives while supporting up to 40 concurrent devices.
arXiv:2610.16730v1 Announce Type: new Abstract: The rapid advancement of foundation models and edge intelligence is transforming wireless networks from data-oriented communication systems into artifi...