One common modern technique for power management is Dynamic Voltage and Frequency Scaling (DVFS), which saves energy by dynamically controlling a processor’s frequency. This paper addresses the lack of the capability of user-specific and context-aware DVFS designs that enable users to customize the CPU/GPU DVFS solution. To ensure a holistic performance experience for users, our power management method incorporates diverse QoE-defining metrics tailored to the expectations of each application type. The significance and novelty of our framework lie in its QoE-centric DVFS, which enables users to custom-define the target metric range for each application, set the power threshold, and assign weights to prioritize a metric or application over others, thereby shaping a personalized power-performance model tailored to their unique usage patterns. Our reinforcement learning framework delivers CPU and GPU frequency-scaling decisions in accordance with QoE-metric targets and processor power thresholds. Experimental evaluations have demonstrated that the QoE-driven DVFS governor achieves notable power conservation compared to the baseline CPU/GPU governors and state-of-the-art zTT framework, while also outperforming the Linux baseline governors. We have further evaluated the proposed framework on an Odroid M1S device running Android and observed substantial power savings compared to the baseline Android DVFS governors.
The sustainable deployment of edge intelligence for cable partial discharge (PD) identification is challenged by non-independent and identically distributed (non-IID) data and limited communication and energy resources. This article proposes a dynamic event-triggered personalized federated aggregation (DET-PFA) approach for accurate and energy-efficient PD identification on edge devices. DET-PFA decouples the PD identification model into a globally aggregated feature extractor and locally retained personalized classifiers, thereby enhancing generalization across clients, preserving adaptability to local data characteristics, and reducing parameter transmission. A dynamic event-triggered mechanism further suppresses unnecessary model uploads. By coordinating parameter decoupling with a dynamic event-triggered mechanism, DET-PFA reduces communication costs in both the parameter-space and temporal dimensions. Experiments on the collected PD dataset show that DET-PFA achieves an identification accuracy of 99.87%. Raspberry Pi 4 Model B deployment experiments further demonstrate reductions of 99.23% in total communication cost and 87.81% in energy consumption compared with conventional federated learning. These results demonstrate the practical feasibility and resource efficiency of DET-PFA and highlight its sustainable computing benefits for edge-based power equipment monitoring.
Intrusion Detection System (IDS) is a vital solution in the cyber defense architecture, and its implementation should guarantee secure data transactions. This role is becoming increasingly critical as ever-increasing technological democratization has transformed the digital field into a high-risk environment for cyberattacks, leading to an increasing volume of network traffic. Under these circumstances, IDSs will be in higher demand and consume more energy. To address this challenge, the objective of this study is to introduce new sustainability metrics, including Green Feature Extraction (GFE), Redundancy Efficient Rule (RER), Waste Rate (WR), and Path Selection Energy Consumption (PSEC), to quantify the environmental impact of IDS operations. The core of our study examines data flows, algorithms, deployment architecture, configuration, and feature engineering techniques to assess their impact on IDS sustainability. The experimental validation confirms the feasibility and effectiveness of the proposed metrics: the GFE analysis shows that using only 5 features achieves 99.58% detection accuracy with optimal energy efficiency, the WR analysis reveals that Random Forest achieves the lowest waste (0.72% vs. 15.48% for Logistic Regression), and the PSEC analysis demonstrates that Gain Ratio consumes 69% less energy than Chi-Square (29.5 kW vs. 95.4 kW). These findings demonstrate substantial potential for sustainable IDS design that balances robust threat detection with environmental responsibility.
This work provides a general formulation of the task scheduling problem in HCSs by further considering the thermal issue along with the objectives of schedule length, energy consumption, and reliability with fault-tolerance. Then, it provides a novel solution approach for the general task scheduling problem in HCSs. The proposed algorithm, which is an efficient estimation of distribution algorithm-based hybrid meta-heuristic approach, is designed by employing Bayesian optimization algorithm (BOA) as a global optimizer, binary search as a local optimizer, and adaptive archive radix strategy as a parameter learner of Bayesian network (BN). Our proposed BN-based task scheduling approach aims to optimize the objectives of HCSs simultaneously. After optimization, sampling, and mutation phase, the objectives of the solutions are calculated. The solutions appropriately assigned tasks to the cores, considering the characteristics of the system. Our proposed scheduling approach exploits complex dynamic voltage and frequency scaling levels by suitably exploiting the benefits of the heterogeneity of the processing elements. We evaluate our proposed method alongside six other multi objective evolutionary algorithms (MOEAs) using a sample task graph and real-world application benchmarks, such as MiBench, modeled as directed acyclic graphs on simulated heterogeneous multiprocessor systems on chip. The performance comparison is conducted using hyper-volume and inverted generalized distance metrics. Additionally, the multi-criterion decision making method (MCDM) is employed to demonstrate the overall superiority of our proposed approach. The results of all the considered performance metrics as well as the MCDM-based comparison indicate that our proposed method outperforms all the competing MOEAs.
Accurate wind power forecasting (WPF) is critical to power system planning and operational management, thereby enhancing grid reliability under large-scale wind power integration. However, most existing methods remain confined to a one-dimensional formulation, which inadequately captures turbine-level spatial heterogeneity and consequently limits the joint modeling of positional structure, multivariate interactions, and temporal dynamics. To overcome this limitation, we reformulate WPF as a three-dimensional spatiotemporal forecasting problem by reorganizing each time step into a multi-channel spatial frame according to turbine locations. On this basis, we propose CViTRNN, a multidimensional collaborative learning framework that combines a channel-aware vision transformer with a lightweight recurrent unit to model multivariate interactions and temporal dependencies under turbine-level spatial priors. Furthermore, a two-phase autoregressive workflow is devised to extend forecasting from single-turbine settings to cross-turbine scenarios. Extensive experiments demonstrate the superior accuracy and adaptability of the proposed model, while further confirming the soundness of its design and the interpretability of its forecasting behavior.
