Indoor localization is a critical component of various applications, including assisted living, personnel monitoring, and asset tracking. Traditional localization methods relying on specialized sensors such as LiDAR, ultrasound, and 3D cameras offer high precision but suffer from high costs and limited interoperability. To address these challenges, this paper explores the Integrated Sensing and Communication (ISAC) paradigm, leveraging signal-based modalities focusing on received signal strength indication (RSSI). These measurements, inherently embedded in wireless communication packets, enable cost-effective and vendor-agnostic localization without the need for additional hardware. However, practical deployment remains challenging due to signal degradation from obstacles, multipath effects, and reflections. This paper presents an end-to-end localization framework utilizing COTS IoT devices and advanced RSSI processing techniques to enhance measurement reliability. By integrating filtering mechanisms and machine learning models, the proposed solution improves distance estimation, categorizes line-of-sight (LoS) and non-line-of-sight (NLoS) conditions, and enhances localization accuracy. Additionally, an open and extendable edge-to-cloud infrastructure supports scalability and real-time processing. Experimental evaluation demonstrate the effectiveness of this approach in various aspects such as increase of RSSI reliability (increase of up to 82 % regarding standard deviation, drastic reduction of outliers' detection and fluctuation of more than 90 % to distances up to 2 m), accurate LoS-NLoS classification up to 99 % and overall localization increased accuracy more than 83 %.

The safe and secure implementation of increasingly complex features is a major challenge in the development of autonomous and distributed embedded systems. Automated design-time procedures that guarantee the fulfillment of critical system properties are a promising approach to tackle this challenge. In the European project XANDAR, which took place from 2021 to 2023, eight partners developed an X-by-Construction (XbC) design framework to support developers in the creation of embedded software systems with certain safety, security, and real-time properties. The design framework combines a model-based toolchain with a hypervisor-based runtime architecture. It targets modern high-performance hardware, facilitates the integration of machine learning applications, and employs a library of trusted safety and security patterns to reduce the implementation and verification effort. This paper describes the concepts developed during the project, the prototypical implementation of the design framework, and its application in both an automotive and an avionics use case.

Realizing desired properties “by construction” is a highly appealing goal in the design of safety-critical embedded systems. As verification and validation tasks in this domain are often both challenging and time-consuming, the by-construction paradigm is a promising solution to increase design productivity and reduce design errors. In the XANDAR project, partners from industry and academia develop a toolchain that will advance current development processes by employing a modelbased X-by-Construction (XbC) approach. XANDAR defines a development process, metamodel extensions, a library of safety and security patterns, and investigates many further techniques for design automation, verification, and validation. The developed toolchain will use a hypervisor-based platform, targeting future centralized, AI-capable high-performance embedded processing systems. It is co-developed and validated in both an avionics use case for situation perception and pilot assistance as well as an automotive use case for autonomous driving.

The integration of connected and autonomous technologies in safety-critical and cyber-physical systems offers great potential in the vital application domains of transportation, manufacturing and aerospace. These technological advancements are necessary to meet the increasing demand for intelligent services, as they open doors to new business models by analysing and sharing the generated data. However, where this sharing of mix-critical data and broader connectivity brings opportunities, it simultaneously presents serious cybersecurity and safety risks due to the cyber-physical nature of these systems. Hence, delivering these intelligent services securely, safely, and reliably to its consumers is a complex engineering and design problem. One of the ways to approach this engineering problem is to consider both system functional and non-functional properties (safety, security, reliability) and systematically integrate them across system design and operational life cycle. The XANDAR project investigates this approach and aims to develop holistic software design methods and architectures for safety-critical and cyber-physical systems that guarantee functional and non-functional properties “byconstruction”. This paper focuses on the non-functional aspects of the project and discusses the preliminary work. by presenting the core cybersecurity principles and uses them as a baseline to propose a holistic cybersecurity engineering process. The tasks of the proposed cybersecurity engineering process are also map onto relevant clauses of ISO 21434. In future, proposed work will be integrated into the XANDAR software toolchain and validated for an avionics situation perception pilot assistance and automotive autonomous driving use cases.

The next generation of networked embedded systems (ES) necessitates rapid prototyping and high performance while maintaining key qualities like trustworthiness and safety. However, development of safety-critical ES suffers from complex software (SW) toolchains and engineering processes. Moreover, the current trend in autonomous systems, which relies on Machine Learning (ML) and AI applications when combined with fail-operational requirements renders the Verification and Validation (V&V) of these new systems a challenging endeavor. Prime examples are Advanced Driver-Assistance Systems (ADAS) that are prone to various safety/security vulnerabilities. The XANDAR project aims at developing a mature SW toolchain (from requirements analysis to the actual code integration on target including V&V) fulfilling the needs of industry for rapid prototyping of interoperable and autonomous ES. Starting from a model-based system architecture, XANDAR will leverage automatic model synthesis and software parallelization techniques to achieve specific non-functional requirements setting the foundation for a novel (real-time, safety-, and security)-by-Construction paradigm.

This work presents the concept and methodology as well as the architecture and physical implementation of an integrated node for smart-city applications. The presented integrated node lies on active RFID technology whereas the use case illustrated, with results from a small-scale verification of the presented node, refers to common-type waste-bins. The sensing units deployed for the use case are ultrasonic sensors that provide ranging information which is translated to fill-level estimations; however the use of a versatile active RFID tag within the node is able to afford multiple sensors for a variety of smart-city applications. The most important benefits of the presented node are power minimization, utilization of low-cost components and accurate fill-level estimation with a tiny data-load fingerprint, regarding the specific use case on waste-bins, whereas the node has to be deployed on public means of transportation or similar standard route vehicles within an urban or suburban context.

This work presents the architecture, modelling, simulation, and physical implementation of a versatile, scalable system for use in common-type waste-bins that can perform and transmit accurate fill-level estimates while consuming minimal power and consisting of low-cost embedded components. The sensing units are based on ultrasonic sensors that provide ranging information which is translated to fill-level estimations based on extensive simulations in MATLAB and physical experiments. At the heart of the proposed implementation lies RFID technology with active RFID tags retrieving information and controlling the sensors and RFID readers receiving and interpreting information. Statistical processing of the simulation in combination with physical experiments and field tests verified that the system works accurately and efficiently with a tiny data-load fingerprint.

Logistics or supply-chain services provide enterprises and organizations with the necessary level of flexibility and efficiency in order to retain competitiveness under the increasingly turbulent e-business area.Web-Services are utilized by organizations in order to integrate high and low level applications, thus providing a collaborative environment without affecting inter-and intra-enterprise processes.Nevertheless, the above context should be enhanced in order to comply with the Web-of-Things concept.This paper describes a sustainable approach towards the above requirement by employing ONS based services able to provide targeted information regarding RFID-enabled physical objects that are handled in an organization agnostic collaborative environment.

Efficient collection of urban solid waste is a growing problem for cities and urban areas. Optimizing service routes based on actual waste bin fill level information can result in significant cost savings, reduced emissions, and improved city image. Fill level sensor installation and networking at an urban scale poses unique challenges. We describe Dynacargo, an innovative smart city application that leverages RFID technology in the waste bins and delay-tolerant networking (DTN) in roaming vehicles acting as mobile sinks. Dynacargo collects and transmits waste bin status information to the backend systems utilizing already existing network infrastructures and single-hop communications thus, reducing associated costs and time for installation, operation, and management of the necessary networks and ICT systems.

This paper describes an Internet-based laboratory, named Remote Monitored and Controlled Laboratory (RMCLab) developed at University of Patras for electrical engineering laboratory education. The key feature of this remote laboratory is the utilization of real experiments, in terms of instrumentation and lab circuits, rather than simulation or virtual reality environment. RMCLab is able to provide affordably and simultaneously its services to many users through the Internet. A wide variety of lab experiments are supported, including not only fixed circuits but also students' custom circuit designs; thus contributing to students' authentic understanding of theory subjects. RMCLab can be accessed via the Web through http://www.apel.ee.upatras.gr/rmclab

A phase frequency detector (PFD) with wide linear operating range and the ability to saturate beyond that range is presented in this paper. Its use in phase locked loops (PLLs) implies elimination of cycle slipping, which results in an analytically predictable transient behavior and a possible improvement in acquisition speed, depending on the PLL design. Simulation results confirm these advantages. Moreover, a detailed study on the applicability of this PFD is conducted in this paper