2,088 research outputs found

    Edge/Fog Computing Technologies for IoT Infrastructure

    Get PDF
    The prevalence of smart devices and cloud computing has led to an explosion in the amount of data generated by IoT devices. Moreover, emerging IoT applications, such as augmented and virtual reality (AR/VR), intelligent transportation systems, and smart factories require ultra-low latency for data communication and processing. Fog/edge computing is a new computing paradigm where fully distributed fog/edge nodes located nearby end devices provide computing resources. By analyzing, filtering, and processing at local fog/edge resources instead of transferring tremendous data to the centralized cloud servers, fog/edge computing can reduce the processing delay and network traffic significantly. With these advantages, fog/edge computing is expected to be one of the key enabling technologies for building the IoT infrastructure. Aiming to explore the recent research and development on fog/edge computing technologies for building an IoT infrastructure, this book collected 10 articles. The selected articles cover diverse topics such as resource management, service provisioning, task offloading and scheduling, container orchestration, and security on edge/fog computing infrastructure, which can help to grasp recent trends, as well as state-of-the-art algorithms of fog/edge computing technologies

    A fuzzy logic recommendation system to support the design of cloud-edge data analysis in cyber-physical systems

    Get PDF
    The ongoing 4th industrial revolution is characterized by the digitization of industrial environments, mainly based on the use of Internet of Things, Cloud Computing and Artificial Intelligence (AI). Regarding AI, although data analysis has shown to be a key enabler of industrial Cyber-Physical Systems (CPS) in the development of smart machines and products, the traditional Cloud-centric solutions are not suitable to attend the data and time-sensitive requirements. Complementary to Cloud, Edge Computing has been adopted to enable the data processing capabilities at or close to the physical components. However, defining which data analysis tasks should be deployed on Cloud and Edge computational layers is not straightforward. This work proposes a framework to guide engineers during the design phase to determine the best way to distribute the data analysis capabilities among computational layers, contributing for a lesser ad-hoc design of distributed data analysis in industrial CPS. Besides defining the guidelines to identify the data analysis requirements, the core contribution relies on a Fuzzy Logic recommendation system for suggesting the most suitable layer to deploy a given data analysis task. The proposed approach is validated in a smart machine testbed that requires the implementation of different data analysis tasks for its operation.This work was supported by FCT - Fundação para a Ciência e Tecnologia within the Project Scope: UIDB/05757/2020.info:eu-repo/semantics/publishedVersio

    TrustE-VC: Trustworthy Evaluation Framework for Industrial Connected Vehicles in the Cloud

    Get PDF
    The integration between cloud computing and vehicular ad hoc networks, namely, vehicular clouds (VCs), has become a significant research area. This integration was proposed to accelerate the adoption of intelligent transportation systems. The trustworthiness in VCs is expected to carry more computing capabilities that manage large-scale collected data. This trend requires a security evaluation framework that ensures data privacy protection, integrity of information, and availability of resources. To the best of our knowledge, this is the first study that proposes a robust trustworthiness evaluation of vehicular cloud for security criteria evaluation and selection. This article proposes three-level security features in order to develop effectiveness and trustworthiness in VCs. To assess and evaluate these security features, our evaluation framework consists of three main interconnected components: 1) an aggregation of the security evaluation values of the security criteria for each level; 2) a fuzzy multicriteria decision-making algorithm; and 3) a simple additive weight associated with the importance-performance analysis and performance rate to visualize the framework findings. The evaluation results of the security criteria based on the average performance rate and global weight suggest that data residency, data privacy, and data ownership are the most pressing challenges in assessing data protection in a VC environment. Overall, this article paves the way for a secure VC using an evaluation of effective security features and underscores directions and challenges facing the VC community. This article sheds light on the importance of security by design, emphasizing multiple layers of security when implementing industrial VCsThis work was supported in part by the Ministry of Education, Culture, and Sport, Government of Spain under Grant TIN2016-76373-P, in part by the Xunta de Galicia Accreditation 2016–2019 under Grant ED431G/08 and Grant ED431C 2018/2019, and in part by the European Union under the European Regional Development FundS

    Trust-empowered, IoT-driven legitimate data offloading

    Get PDF
    In an IoT environment deployed on top of fog and/or cloud nodes, offloading data between nodes is a common practice that aims at lessening the burden on these nodes and hence, meeting some real-time processing requirements. Existing initiatives put emphasis on “when to offload” and “where to offload” using criteria like resource constraint, load balancing, and data safety during transfer. However, there is limited emphasis on the trustworthiness of those nodes that will accept the offloaded data putting these data at risk of misuse. To address this limited emphasis, this paper advocates for trust as a decision criterion for identifying the appropriate nodes for hosting the offloaded data. A trust model is designed and then, developed considering factors like legitimacy, quality-of-service, and quality-of-experience. A system demonstrating the technical doability of the trust model is presented in the paper, as well
    corecore