1,721,518 research outputs found
Energy efficiency of TCP: An analytical model and its application to reduce energy consumption of the most diffused transport protocol
No full textEnergy efficient communications become a challenge for both industries and researchers. Incorporating energy efficiency into the design of network protocols and architectures represents a relevant issue in networking research. Currently, very few works address energy efficiency as a fundamental feature of network protocols. This paper benchmarks energy efficiency of TCP to understand the parameters and operational mechanics that determine and contribute to energy consumption. We propose an analytical model with energy consumption to protocol operation cycles and novel optimization techniques for reducing energy consumption of TCP. The evaluation results, obtained from NS2 simulations, demonstrate that even minor modifications of the protocol behavior can bring significant savings of energy. Copyright © 2015 John Wiley & Sons, Ltd
Profiling Performance of Application Partitioning for Wearable Devices in Mobile Cloud and Fog Computing
Full text linkWearable devices have become essential in our daily activities. Due to battery constrains the use of computing, communication, and storage resources is limited. Mobile Cloud Computing (MCC) and the recently emerged Fog Computing (FC) paradigms unleash unprecedented opportunities to augment capabilities of wearables devices. Partitioning mobile applications and offloading computationally heavy tasks for execution to the cloud or edge of the network is the key. Offloading prolongs lifetime of the batteries and allows wearable devices to gain access to the rich and powerful set of computing and storage resources of the cloud/edge. In this paper, we experimentally evaluate and discuss rationale of application partitioning for MCC and FC. To experiment, we develop an Android-based application and benchmark energy and execution time performance of multiple partitioning scenarios. The results unveil architectural trade-offs that exist between the paradigms and devise guidelines for proper power management of service-centric Internet of Things (IoT) applications
A Cost-Effective Distributed Framework for Data Collection in Cloud-based Mobile Crowd Sensing Architectures
Full text linkpeer reviewedMobile crowd sensing received significant attention in the recent years and has become a popular paradigm for sensing. It operates relying on the rich set of built-in sensors equipped in mobile devices, such as smartphones, tablets and wearable devices. To be effective, mobile crowd sensing systems require a large number of users to contribute data. While several studies focus on developing efficient incentive mechanisms to foster user participation, data collection policies still require investigation. In this paper, we propose a novel distributed and sustainable framework for gathering information in cloud-based mobile crowd sensing systems with opportunistic reporting. The proposed framework minimizes cost of both sensing and reporting, while maximizing the utility of data collection and, as a result, the quality of contributed information. Analytical and simulation results provide performance evaluation for the proposed framework by providing a fine-grained analysis of the energy consumed. The simulations, performed in a real urban environment and with a large number of participants, aim at verifying the performance and scalability of the proposed approach on a large scale under different user arrival patterns
On capturing vital properties of denial of service attacks using metaheuristic approaches
No full text
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Transformer-based Time Series Forecasting
Full text linkTime series forecasting (TSF) is indispensable for decision-making under uncertainty. This thesis systematically explores Transformer-based TSF across four key dimensions: Data, Model, Application, and Evaluation, to facilitate research in this field.
The evolution of artificial intelligence (AI) has been shaped by advances in both model sophistication and the emergence of the data-centric paradigm, which highlights the critical role of high-quality data in the machine learning (ML) pipeline. Among recent innovations, the Transformer architecture has shown remarkable performance across domains such as natural language processing (NLP), computer vision (CV), and time series forecasting (TSF). Chapter 3 (Data) of this thesis bridges the gap between Transformer-based TSF and data-centric AI through a structured literature review and taxonomy, highlighting recent research works with various solutions at the intersection of Transformers and data-centric AI, with the aim of laying a foundation for future research at this intersection.
Previous research showes that in multivariate time-series forecasting, Transformers handle complex, noisy datasets well but often suffer from redundant features and heavy computational demands. To address these challenges, we introduce a novel framework that integrates Principal Component Analysis (PCA) to streamline inputs, enhancing both accuracy and efficiency. Evaluated across five state-of-the-art (SOTA) models and four real-world datasets, the PCA+Crossformer variant reduces mean square error (MSE) by 33.3% and runtime by 49.2%. Notably, the framework achieves up to 86.9% runtime reduction on the Traffic dataset, demonstrating its practical value in real-world applications. The Chapter 4 (Model) details this PCA-enhanced Transformer framework, establishing a foundation for subsequent architectural innovations.
Beyond standard forecasting tasks, we extend our analysis to credit default swaps (CDS). While sophisticated models like Transformers, gradient-boosted machines (GBM), and extreme gradient boosted (XGBoost) offer strong performance, interpretability remains vital for high-stakes financial decisions. Leveraging explainability tools and hyperparameter optimization via high-performance computing (HPC), our experiments show that fine-tuned XGBoost offers the best balance between accuracy and interpretability. To further enhance trust in AI-driven decisions, we also include a Trustworthy AI (TAI) framework. The Chapter 5 (Application) presents both quantitative metrics and qualitative insights as well as transformer-based TSF in the CDS context.
Finally, to address the current lack of a unified hyperparameter optimization (HPO) pipeline for Transformer-based TSF, we present a generalizable HPO framework. Validated on benchmark datasets and extended to recent models like Mamba and TimeMixer, this pipeline offers practical guidance for efficient model tuning. All code and results are publicly released to promote transparency and further innovation. The Chapter 6 (Evaluation) provides open-source HPO tools and best practices for model tuning and assessment, aiming to facilitate model selection and usage in practice
Heterogeneous Job Consolidation for Power Aware Scheduling with Quality of Service
No full textIn this paper, we present an energy optimization model of Cloud computing, and formulate novel energy-aware resource allocation problem that provides energy-efficiency by heterogeneous job consolidation taking into account types of applications. Data centers process heterogeneous workloads that include CPU intensive, disk I/O intensive, memory intensive, network I/O intensive and other types of applications. When one type of applications creates a bottleneck and resource contention either in CPU, disk or network, it may result in degradation of the system performance and increasing energy consumption. We discuss energy characteristics of applications, and how an awareness of their types can help in intelligent allocation strategy to improve energy consumption
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Collaborative Data Delivery for Smart City-oriented Mobile Crowdsensing Systems
Full text linkpeer reviewedThe huge increase of population living in cities calls for a sustainable urban development. Mobile crowdsensing (MCS) leverages participation of active citizens to improve performance of existing sensing infrastructures. In typical MCS systems, sensing tasks are allocated and reported on individual-basis. In this paper, we investigate on collaboration among users for data delivery as it brings a number of benefits for both users and sensing campaign organizers and leads to better coordination and use of resources. By taking advantage from proximity, users can employ device-to-device (D2D) communications like Wi-Fi Direct that are more energy efficient than 3G/4G technology. In such scenario, once a group is set, one of its member is elected to be the owner and perform data forwarding to the collector. The efficiency of forming groups and electing suitable owners defines the efficiency of the whole collaborative-based system. This paper proposes three policies optimized for MCS that are compliant with current Android implementation of Wi-Fi Direct. The evaluation results, obtained using CrowdSenSim simulator, demonstrate that collaborative-based approaches outperform
significantly individual-based approaches
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