Ultra-wideband (UWB) localization systems commonly rely on Two-Way Ranging (TWR) or Time Difference of Arrival (TDoA) techniques, each associated with its strengths and limitations. While TWR offers accurate ranging without clock synchronization, it requires frequent packet exchanges. In contrast, TDoA enables scalable, low-latency localization but suffers from synchronization and multipath-induced errors. Although many error correction approaches have been proposed for TWR, scientific work on error correction for TDoA is lacking. In this work, we propose a hybrid learning-based approach that leverages data collected from TWR systems to enhance time of arrival (ToA) error correction in TDoA-based localization. The motivation behind this strategy is: (1) labeled TWR datasets are more readily available in the scientific community, and (2) obtaining accurate error labels in real-life conditions for TWR is significantly easier than for TDoA. To this end, we train a convolutional neural network (CNN) using channel impulse response (CIR)s and their corresponding ranging error labels from a TWR system. Once trained, the model is applied directly, without retraining or fine-tuning to CIRs captured in a TDoA setup. Experimental results demonstrate that existing error correction approaches, such as anchor node selection, fail when encountering strong multipath environments, with 11.6% of positions not being calculated due to the removal of too many CIRs. In contrast, our approach enables significant gains in positioning accuracy in dense multipath environments, where only a limited number of signals are received.

The contemporary AI landscape demands a holistic framework to ensure security across the entire AI supply chain and lifecycle. Despite the availability of existing adversarial attack techniques, an end-to-end solution for identifying threats, vulnerabilities, and risks is still lacking. Despite EU initiatives like the AI Act promoting safety and trustworthiness in AI, it lacks a system for managing weaknesses within a networked AI supply chain. This paper introduces CoEvolution, which aspires to address this gap by implementing a complete Security, Trust, and Robustness (STR) assessment solution, capable of addressing evolving AI cybersecurity threats. CoEvolution proposes a universal hub for STR risk assessment and security assurance, aligned with MLDevOps practices and EU AI regulatory frameworks. It introduces innovative AI model descriptions, including an AI Model Bill of Materials, coupled with security monitoring and context awareness. CoEvolution seeks to ensure compliance with EU directives on trust, fairness, data governance, and GDPR guidelines.

The rapid evolution of cellular networks, driven by the proliferation of mobile devices and the exponential growth of the Internet of Things (IoT), has significantly advanced wireless communication technologies. Fifth generation of wireless communications technology (5G) enhanced data rates, latency, and network capacity, resulting in the emergence of new applications. However, the sixth generation (6G) is foreseen to support a new set of use cases with diverse requirements. This paper explores the critical role of artificial intelligence (AI) in shaping the trajectory from 5G to 6G. We discuss AI applications in 5G for network planning, resource allocation, traffic management, and security, as well as propose infrastructure upgrades, like edge servers and enhanced network topologies, to support AI in 6G. Additionally, we outline a visionary perspective on AI’s potential contributions to 6G, highlighting its role in enabling innovative services and applications. By providing this forward-looking perspective, this paper aims to stimulate discussion and guide the development of intelligent and autonomous 6G networks.

Indoor positioning systems based on Ultra-wideband (UWB) technology are gaining recognition for their ability to provide cm-level localization accuracy. However, these systems often encounter challenges caused by dense multi-path fading, leading to positioning errors. To address this issue, in this letter, we propose a novel methodology for unsupervised anchor node selection using deep embedded clustering (DEC). Our approach uses an Auto Encoder (AE) before clustering, thereby better separating UWB features into separable clusters of UWB input signals. We furthermore investigate how to rank these clusters based on their cluster quality, allowing us to remove untrustworthy signals. Experimental results show the efficiency of our proposed method, demonstrating a significant 23.1% reduction in mean absolute error (MAE) compared to without anchor exclusion. Especially in the dense multi-path area, our algorithm achieves even more significant enhancements, reducing the MAE by 26.6% and the 95th percentile error by 49.3% compared to without anchor exclusion.

Traditional IoT setups are cloud-centric and typically focused around a centralized IoT platform to which data is uploaded for further processing. Next generation IoT applications are incorporating technologies such as artificial intelligence, augmented reality, and distributed ledgers to realize semi-autonomous behaviour of vehicles, guidance for human users, and machine-to-machine interactions in a trustworthy manner. Such applications require more dynamic IoT environments, which can operate locally without the necessity to communicate with the Cloud. In this paper, we describe three use cases of next generation IoT applications and highlight associated challenges for future research. We further present the IntellIoT framework that comprises the required components to address the identified challenges.

The 6th generation of wireless mobile networks is emerging as a paradigm shifting successor to unifying the experience across the physical, digital, and human worlds, pushing boundaries on performance in capacity, throughput, latency, scalability, flexibility, and reliability, while prominently addressing new major factors, including sustainability, security and privacy, as well as digital inclusion. Many research institutions and initiatives worldwide have started investigations to make 6G a reality by approximately 2030. In Germany, federal funding from the German Ministry of Education and Research (BMBF) supports a large-scale 6G initiative, with its lighthouse project, called 6G-ANNA. The core aim of this project is to develop the key aspects of a holistic, sustainable, secure, and resilient 6G system design that will simplify and improve the interaction between humans, digital assets, and the physical environment. This paper shares the vision of the project’s main technical working areas and advances, spanning topics from radio access, integration of multiple networks, as well as automation and simplification in networking to new applications and testbed scenarios, including real-time digital twins and extended reality. The industrial impact and relevance of standardization makes 6G-ANNA uniquely positioned to lead and realize the vision of next-generation wireless mobile network technologies, systems, and applications.

In recent years, industrial manufacturing has undergone massive technological changes that embrace digitalization and automation towards the vision of intelligent manufacturing plants. With the aim of maximizing efficiency and profitability in production, an important goal is to enable flexible manufacturing, both, for the customer (desiring more individualized products) and for the manufacturer (to adjust to market demands). Manufacturing-as-a-service can support this through manufacturing plants that are used by different tenants who utilize the machines in the plant, which are offered by different providers. To enable such pay-per-use business models, Distributed Ledger Technology (DLT) is a viable option to establish decentralized trust and traceability. Thus, in this paper, we study potential DLT technologies for efficient and intelligent integration of DLT-based solutions in manufacturing environments. We propose a general framework to adapt DLT in manufacturing, and then we introduce the use case of shared manufacturing, which we utilize to study the communication and computation efficiency of selected DLTs in resource-constrained wireless IoT networks.

The Internet of Things (IoT) concept is evolving rapidly and influencing new developments in various application domains, such as the Internet of Mobile Things (IoMT), Autonomous Internet of Things (A-IoT), Autonomous System of Things (ASoT), Internet of Autonomous Things (IoAT), Internet of Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc. are progressing/advancing by using IoT technology. The IoT influence represents new development and deployment challenges in different areas such as seamless platform integration, context based cognitive network integration, new mobile sensor/actuator network paradigms, things identification (addressing, naming in IoT) and dynamic things discoverability and many others. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication of multiple heterogeneous mobile/autonomous/robotic things for cooperating, coordination, configuration, exchange of information, security, safety and protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrency that require new ideas for integrating the intelligent "devices", collaborative robots (COBOTS), into IoT applications. Dynamic maintainability, self-healing, self-repair of resources, changing resource state, (re-) configuration and context based IoT systems for service implementation and integration with IoT network service composition are of paramount importance when new "cognitive devices" are becoming active participants in IoT applications. The chapter aims to be an overview of the IoRT concept, technologies, architectures and applications and to provide a comprehensive coverage of future challenges, developments and applications. This chapter aims to be an overview of the IoRT concept, technologies, architectures and applications and to provide a comprehensive coverage of trends and future challenges, developments and applications. 2

The chapter presents an overview of the eight that are part of the European IoT Security and Privacy Projects initiative (IoT-ESP) addressing advanced concepts for end-to-end security in highly distributed, heterogeneous and dynamic IoT environments. The approaches presented are holistic and include identification and authentication, data protection and prevention against cyber-attacks at the device and system levels. The projects present architectures, concepts, methods and tools for open IoT platforms integrating evolving sensing, actuating, energy harvesting, networking and interface technologies. Platforms should provide connectivity and intelligence, actuation and control features, linkage to modular and ad-hoc cloud services, The IoT platforms used are compatible with existing international developments addressing object identity management, discovery services, virtualisation of objects, devices and infrastructures and trusted IoT approaches.

To avoid the disadvantages of a cloud-centric infrastructure, next-generation industrial scenarios focus on using distributed edge networks. Task allocation in distributed edge networks with regards to minimizing the energy consumption is NP-hard and requires considerable computational effort to obtain optimal results with conventional algorithms like Integer Linear Programming (ILP). We extend an existing ILP problem including an ILP heuristic for multi-workflow allocation and propose a Particle Swarm Optimization (PSO) and a Deep Reinforcement Learning (DRL) algorithm. PSO and DRL outperform the ILP heuristic with a median optimality gap of and against . DRL has the lowest upper bound for the optimality gap. It performs better than PSO for problem sizes of more than 25 tasks and PSO fails to find a feasible solution for more than 60 tasks. The execution time of DRL is significantly faster with a maximum of 1 s in comparison to PSO with a maximum of 361 s. In conclusion, our experiments indicate that PSO is more suitable for smaller and DRL for larger sized task allocation problems.

An Intelligent IoT Environment (iIoTe) is comprised of heterogeneous devices that can collaboratively execute semi-autonomous IoT applications, examples of which include highly automated manufacturing cells or autonomously interacting harvesting machines. Energy efficiency is key in such edge environments, since they are often based on an infrastructure that consists of wireless and battery-run devices, e.g., e-tractors, drones, Automated Guided Vehicle (AGV)s and robots. The total energy consumption draws contributions from multipleiIoTe technologies that enable edge computing and communication, distributed learning, as well as distributed ledgers and smart contracts. This paper provides a state-of-the-art overview of these technologies and illustrates their functionality and performance, with special attention to the tradeoff among resources, latency, privacy and energy consumption. Finally, the paper provides a vision for integrating these enabling technologies in energy-efficient iIoTe and a roadmap to address the open research challenges

As Machine Learning (ML) models are becoming increasingly complex, one of the central challenges is their deployment at scale, such that companies and organizations can create value through Artificial Intelligence (AI). An emerging paradigm in ML is a federated approach where the learning model is delivered to a group of heterogeneous agents partially, allowing agents to train the model locally with their own data. However, the problem of valuation of models, as well the questions of incentives for collaborative training and trading of data/models, have received limited treatment in the literature. In this paper, a new ecosystem of ML model trading over a trusted Blockchain-based network is proposed. The buyer can acquire the model of interest from the ML market, and interested sellers spend local computations on their data to enhance that model's quality. In doing so, the proportional relation between the local data and the quality of trained models is considered, and the valuations of seller's data in training the models are estimated through the distributed Data Shapley Value (DSV). At the same time, the trustworthiness of the entire trading process is provided by the distributed Ledger Technology (DLT). Extensive experimental evaluation of the proposed approach shows a competitive run-time performance, with a 15\% drop in the cost of execution, and fairness in terms of incentives for the participants.

Embodied intelligence will unleash the potential of artificial intelligence by providing it with a body and impact every area of life. The success of the adoption of the embodied intelligence market depends on solving the technical, legal, economic, and social issues it brings with it.

The name of one of the volume editors, Simon Mayer, was erroneously misspelt in an earlier version of the cover and inside cover of this volume. This has now been corrected.

The ESWC Satellite events 2021 cover a wide range of topics such as visual intelligent agents and multi-agent systems, and much more

Various tools support developers in the creation of IoT applications. In general, such tools focus on the business logic, which is important for application development, however, for IoT applications in particular, it is crucial to consider the network, as they are intrinsically based on interconnected devices and services. IoT application developers do not have in depth expertise in configuring networks and physical connections between devices. Hence, approaches are required that automatically deduct these configurations. We address this challenge in this work with an architecture and associated data models that enable networking-aware IoT application development. We evaluate our approach in the context of an application for oil leakage detection in wind turbines.

In the industrial Internet of Things domain, applications are moving from the Cloud into the Edge, closer to the devices producing and consuming data. This means that applications move from the scalable and homogeneous Cloud environment into a potentially constrained heterogeneous Edge network. Making Edge applications reliable enough to fulfill Industry 4.0 use cases remains an open research challenge. Maintaining operation of an Edge system requires advanced management techniques to mitigate the failure of devices. This article tackles this challenge with a twofold approach: (1) a policy-enabled failure detector that enables adaptable failure detection and (2) an allocation component for the efficient selection of failure mitigation actions. The parameters and performance of the failure detection approach are evaluated, and the performance of an energy-efficient allocation technique is measured. Finally, a vision for a complete system and an example use case are presented.