Explainable AI over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions

Explainable Artificial Intelligence (XAI) is transforming the field of Artificial Intelligence (AI) by enhancing the trust of end-users in machines. As the number of connected devices keeps on growing, the Internet of Things (IoT) market needs to be trustworthy for the end-users. However, existing literature still lacks a systematic and comprehensive survey work on the use of XAI for IoT. To bridge this lacking, in this paper, we address the XAI frameworks with a focus on their characteristics and support for IoT. We illustrate the widely-used XAI services for IoT applications, such as security enhancement, Internet of Medical Things (IoMT), Industrial IoT (IIoT), and Internet of City Things (IoCT). We also suggest the implementation choice of XAI models over IoT systems in these applications with appropriate examples and summarize the key inferences for future works. Moreover, we present the cutting-edge development in edge XAI structures and the support of sixth-generation (6G) communication services for IoT applications, along with key inferences. In a nutshell, this paper constitutes the first holistic compilation on the development of XAI-based frameworks tailored for the demands of future IoT use cases.Comment: 29 pages, 7 figures, 2 tables. IEEE Open Journal of the Communications Society (2022

Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS - a collection of Technical Notes Part 1

This report provides an introduction and overview of the Technical Topic Notes (TTNs) produced in the Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS (Tigars) project. These notes aim to support the development and evaluation of autonomous vehicles. Part 1 addresses: Assurance-overview and issues, Resilience and Safety Requirements, Open Systems Perspective and Formal Verification and Static Analysis of ML Systems. Part 2: Simulation and Dynamic Testing, Defence in Depth and Diversity, Security-Informed Safety Analysis, Standards and Guidelines

A Survey on Trust Metrics for Autonomous Robotic Systems

This paper surveys the area of Trust Metrics related to security for autonomous robotic systems. As the robotics industry undergoes a transformation from programmed, task oriented, systems to Artificial Intelligence-enabled learning, these autonomous systems become vulnerable to several security risks, making a security assessment of these systems of critical importance. Therefore, our focus is on a holistic approach for assessing system trust which requires incorporating system, hardware, software, cognitive robustness, and supplier level trust metrics into a unified model of trust. We set out to determine if there were already trust metrics that defined such a holistic system approach. While there are extensive writings related to various aspects of robotic systems such as, risk management, safety, security assurance and so on, each source only covered subsets of an overall system and did not consistently incorporate the relevant costs in their metrics. This paper attempts to put this prior work into perspective, and to show how it might be extended to develop useful system-level trust metrics for evaluating complex robotic (and other) systems