Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges

Machine learning (ML) is widely used for key tasks in Connected and Automated Vehicles (CAV), including perception, planning, and control. However, its reliance on vehicular data for model training presents significant challenges related to in-vehicle user privacy and communication overhead generated by massive data volumes. Federated learning (FL) is a decentralized ML approach that enables multiple vehicles to collaboratively develop models, broadening learning from various driving environments, enhancing overall performance, and simultaneously securing local vehicle data privacy and security. This survey paper presents a review of the advancements made in the application of FL for CAV (FL4CAV). First, centralized and decentralized frameworks of FL are analyzed, highlighting their key characteristics and methodologies. Second, diverse data sources, models, and data security techniques relevant to FL in CAVs are reviewed, emphasizing their significance in ensuring privacy and confidentiality. Third, specific and important applications of FL are explored, providing insight into the base models and datasets employed for each application. Finally, existing challenges for FL4CAV are listed and potential directions for future work are discussed to further enhance the effectiveness and efficiency of FL in the context of CAV

Hybrid clouds for data-Intensive, 5G-Enabled IoT applications: an overview, key issues and relevant architecture

Hybrid cloud multi-access edge computing (MEC) deployments have been proposed as efficient means to support Internet of Things (IoT) applications, relying on a plethora of nodes and data. In this paper, an overview on the area of hybrid clouds considering relevant research areas is given, providing technologies and mechanisms for the formation of such MEC deployments, as well as emphasizing several key issues that should be tackled by novel approaches, especially under the 5G paradigm. Furthermore, a decentralized hybrid cloud MEC architecture, resulting in a Platform-as-a-Service (PaaS) is proposed and its main building blocks and layers are thoroughly described. Aiming to offer a broad perspective on the business potential of such a platform, the stakeholder ecosystem is also analyzed. Finally, two use cases in the context of smart cities and mobile health are presented, aimed at showing how the proposed PaaS enables the development of respective IoT applications.Peer ReviewedPostprint (published version

Artificial Intelligence: A Promised Land for Web Services

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An Overview about Emerging Technologies of Autonomous Driving

Since DARPA started Grand Challenges in 2004 and Urban Challenges in 2007, autonomous driving has been the most active field of AI applications. This paper gives an overview about technical aspects of autonomous driving technologies and open problems. We investigate the major fields of self-driving systems, such as perception, mapping and localization, prediction, planning and control, simulation, V2X and safety etc. Especially we elaborate on all these issues in a framework of data closed loop, a popular platform to solve the long tailed autonomous driving problems

Integration of ontologies with decentralized autonomous organizations development: A systematic literature review

This paper presents a systematic literature review of the integration of ontologies into the Decentralized Autonomous Organization (DAO) development process. The review extracted data from 34 primary studies dealing with ontologies in the blockchain domain. DAO has become a key concept for the development of blockchain-based decentralized software systems. DAOs are seen as a positive alternative for organizations interested in the adoption of decentralized, reliable and transparent governance, as well as attracting the interest of academic research. However, there is no common understanding or generally accepted formal definition of a DAO, and the guidelines that provide support for the adoption and development of DAOs are limited to a few key references that lack the computational semantics needed to enable their automated validation, simulation or execution. Thus, the objective of this paper is to provide an unbiased and up-to-date review related to the integration of ontologies within DAOs which helps to identify new research opportunities and take advantage of this integration from a blockchain-based decentralized perspective

IT Infrastructures in Manufacturing: Insights from Seven Case Studies

IT solutions in manufacturing support the execution as well as the monitoring of production operations. Fast reaction to exceptions, detailed documentation of operations, and the detection of inefficiencies in the production are among the benefits of a tight IT integration of shop-floor processes. Several dedicated software solutions and standards exist for the manufacturing domain. However, each manufacturer must tailor the IT to the special requirements of its processes and infrastructure. We found that real-world installations show considerable variations. In this paper we present the results of seven case studies on IT infrastructures in manufacturing. For each case we portray the employed architecture and the main factor that influenced the design. From this analysis we derive reoccurring patterns on the structure of IT solutions in manufacturing and relate them to existing standards. Our results provide system architects with guidance for picking the right architectural choices in different manufacturing environments

AI-Based Edge Acquisition, Processing and Analytics for Industrial Food Production

This article presents a novel approach to the acquisition, processing, and analytics of industrial food production by employing state-of-the-art artificial intelligence (AI) at the edge. Intelligent Industrial Internet of Things (IIoT) devices are used to gather relevant production parameters of industrial equipment and motors, such as vibration, temperature and current using built-in and external sensors. Machine learning (ML) is applied to measurements of the key parameters of motors and equipment. It runs on edge devices that aggregate sensor data using Bluetooth, LoRaWAN, and Wi-Fi communication protocols. ML is embedded across the edge continuum, powering IIoT devices with anomaly detectors, classifiers, predictors, and neural networks. The ML workflows are automated, allowing them to be easily integrated with more complex production flows for predictive maintenance (PdM). The approach proposes a decentralized ML solution for industrial applications, reducing bandwidth consumption and latency while increasing privacy and data security. The system allows for the continuous monitoring of parameters and is designed to identify potential breakdown situations and alert users to prevent damage, reduce maintenance costs and increase productivity.publishedVersio