Edge computing is projected to have profound implications in the coming decades, proposed to provide solutions for applications such as augmented reality, predictive functionalities, and collaborative Cyber-Physical Systems (CPS). For such applications, edge computing addresses the new computational needs, as well as privacy, availability, and real-time constraints, by providing local high-performance computing capabilities to deal with the limitations and constraints of cloud and embedded systems. Edge computing is today driven by strong market forces stemming from IT/cloud, telecom, and networking—with corresponding multiple interpretations of “edge computing” (e.g., device edge, network edge, distributed cloud). Considering the strong drivers for edge computing and the relative novelty of the field, it becomes important to understand the specific requirements and characteristics of edge-based CPS, and to ensure that research is guided adequately, e.g., avoiding specific gaps. Our interests lie in the applications of edge computing as part of CPS, where several properties (or attributes) of trustworthiness, including safety, security, and predictability/availability, are of particular concern, each facing challenges for the introduction of edge-based CPS. We present the results of a systematic mapping study, a kind of systematic literature survey, investigating the use of edge computing for CPS with a special emphasis on trustworthiness. The main contributions of this study are a detailed description of the current research efforts in edge-based CPS and the identification and discussion of trends and research gaps. The results show that the main body of research in edge-based CPS only to a very limited extent consider key attributes of system trustworthiness, despite many efforts referring to critical CPS and applications like intelligent transportation. More research and industrial efforts will be needed on aspects of trustworthiness of future edge-based CPS including their experimental evaluation. Such research needs to consider the multiple interrelated attributes of trustworthiness including safety, security, and predictability, and new methodologies and architectures to address them. It is further important to provide bridges and collaboration between edge computing and CPS disciplines.

Cyber Physical Systems (CPS) bridge the cyber-world of computing and communications with the physical world and require development of secure and reliable software. It asserts a big challenge not only on testing and verifying the correctness of all physical and cyber components of such big systems, but also on integration of these components. This paper develops a categorization of multiple levels of testing required to test CPS and makes a comparison of these levels with the levels of software testing based on the V-model. It presents a detailed state-of-the-art survey on the testing approaches performed on the CPS. Further, it provides challenges in CPS testing.

This paper presents the implementation of a Hierarchical Scheduling Framework (HSF) on an open source real-time operating system (FreeRTOS) to support the temporal isolation between a number of applications, on a single processor. The goal is to achieve predictable integration and reusability of independently developed components or applications. We present the initial results of the HSF implementation by running it on an AVR 32-bit board EVK1100. The paper addresses the fixed-priority preemptive scheduling at both global and local scheduling levels. It describes the detailed design of HSF with the emphasis of doing minimal changes to the underlying FreeRTOS kernel and keeping its API intact. Finally it provides (and compares) the results for the performance measures of idling and deferrable servers with respect to the overhead of the implementation.

The use of AI-based risk mitigation is increasing to provide safety in the areas of smart manufacturing, automated logistics etc, where the human-robot collaboration operations are in use. This paper presents our work on implementation of fuzzy logic system (FLS) and reinforcement learning (RL) to build risk mitigation modules for human-robot collaboration scenarios. Risk mitigation using FLS strategy is developed by manually defining the linguistic values, tuning the membership functions and generating the rules based on ISO/TS15066:2016. RL-based risk mitigation modules are developed using three different Qnetworks to estimate the Q-value function. Our purpose is twofold: to perform a comparative analysis of FLS and RL in terms of safety perspectives and further to evaluate the efficiency to accomplish the task. Our results present that all the proposed risk mitigation strategies improve the safety aspect by up to 26% as compared to a default setup where the robot is just relying on a navigation module without risk mitigation. The efficiency of using FLS model is maintained to the default setup, while the efficiency of using RL model is reduced by 26% from the default setup. We also compare the computation performance of risk mitigation between centralized and edge execution where the edge execution is 27.5 times faster than the centralized one.

The Internet of Things (IoT) is a vision of a future society where an ever-increasing number of heterogeneous physical devices ("things") obtain Internet connectivity, thus enabling a large number of applications for a broad range of industries and society at large. Mobile network operators, expected to provide the network infrastructure for many of these applications, face an unprecedented level of complexity. This complexity not only relates to the number of applications that share the network infrastructure, but also to the different network Quality of Service (QoS) requirements these applications have. To achieve economies of scale, automation in management of those applications throughout their lifecycle is essential. In this paper, we propose an architecture that automates allocation, monitoring and deallocation of both cloud and cellular network resources to ensure QoS for applications that involve connected devices communicating with cloud-hosted software. We describe an implementation of this architecture using a combination of open source, commercial, and custom components and evaluate it through a series of measurements. Results show that our implementation can simultaneously support mobile broadband and low latency, high availability mission-critical applications.

The recent advancements and emergence of rapidly evolving AI models, such as large language models (LLMs), have sparked interest among researchers and professionals. These models are ubiquitously being fine-tuned and applied across various fields such as healthcare, customer service and support, education, automated driving, and smart factories. This often leads to an increased level of complexity and challenges concerning the trustworthiness of these models, such as the generation of toxic content and hallucinations with high confidence leading to serious consequences. The European Union Artificial Intelligence Act (AI Act) is a regulation concerning artificial intelligence. The EU AI Act has proposed a comprehensive set of guidelines to ensure the responsible usage and development of general-purpose AI systems (such as LLMs) that may pose potential risks. The need arises for strengthened efforts to ensure that these high-performing LLMs adhere to the seven trustworthiness aspects (data governance, record-keeping, transparency, human-oversight, accuracy, robustness, and cybersecurity) recommended by the AI Act. Our study systematically maps research, focusing on identifying the key trends in developing LLMs across different application domains to address the aspects of AI Act-based trustworthiness. Our study reveals the recent trends that indicate a growing interest in emerging models such as LLaMa and BARD, reflecting a shift in research priorities. GPT and BERT remain the most studied models, and newer alternatives like Mistral and Claude remain underexplored. Trustworthiness aspects like accuracy and transparency dominate the research landscape, while cybersecurity and record-keeping remain significantly underexamined. Our findings highlight the urgent need for a more balanced, interdisciplinary research approach to ensure LLM trustworthiness across diverse applications. Expanding studies into underexplored, high-risk domains and fostering cross-sector collaboration can bridge existing gaps. Furthermore, this study also reveals domains (like telecommunication) which are underrepresented, presenting considerable research gaps and indicating a potential direction for the way forward.