Complex and scalable IoT services increasingly rely on collaborative agreements across multi-cloud and edge-cloud environments. Such cooperation enables the dynamic composition of service workflows by leveraging both local and external resources, thus addressing the variability and surge in data traffic and computational demand typical of IoT scenarios. In this paper, we propose a trust-based model for dynamic IoT service composition that integrates system-level Quality of Service (QoS) metrics, operational costs, and user-generated feedback. The approach combines graph-based QoS prediction with trust-driven feedback aggregation and cost-aware heuristics, enabling more informed and adaptive service selection. A qualitative comparison with existing approaches highlights how the proposed model addresses key limitations in current solutions by jointly considering trust, QoS, and cost in a unified framework. Simulation-based experiments demonstrate that the proposed approach improves the reliability and responsiveness of service orchestration in heterogeneous IoT environments.
Metacognition, the capacity of an intelligent system to reason about its own processes, is widely regarded as absent from current artificial intelligence. This special issue examines how metacognitive principles can be modeled, measured, and applied to predict and improve AI behavior. Drawing on cognitive psychology, we frame metacognition through two related functions. Metacognitive monitoring gathers cues that reveal the state of a problem. Metacognitive control uses those cues to guide action. The six contributions gathered here leverage these ideas and span several branches of artificial intelligence examining metacognitive research relating to language models, uncertainty quantification, robotics, vision, federated learning, and reinforcement learning.
Frontier agentic systems powered by large language models (LLMs) exhibit human-like patterns of cognition. As these systems become deeply integrated across different domains, their cognitive engagement raises critical concerns for human society that remain insufficiently studied. To address this gap, we systematically analyze risks induced by expanding cognitive capabilities, following a three-level framework defined by their cognitive scope, from physical cognition to social cognition, and finally to self-referential cognition. We study their potential risks to human agency, autonomy, and control capability, corresponding to each cognitive level. We finally propose strategies to mitigate these risks and enhance the controllability of agentic AI systems, ensuring their long-term safe development.
As artificial intelligence (AI) continues to evolve, it has transitioned from a research-focused discipline to a widely adopted technology, enabling intelligent solutions across various sectors. In security, AI's role in strengthening organizational resilience has been studied for more than two decades. Although much attention has focused on AI's constructive applications, the increasing maturity and integration of AI have also exposed its darker potentials. This article explores two emerging AI-related threats and the interplay between them: AI as a target of attacks ("adversarial AI") and AI as a means to launch attacks on any target ("offensive AI")-potentially even on another AI. By cutting through the confusion and explaining these threats in plain terms, we introduce the complex and often misunderstood interplay between adversarial AI and offensive AI, offering a clear and accessible introduction to the challenges posed by these threats.
With artificial intelligence- (AI) and machine learning (ML)-based tools being discussed nowadays in academia, industry, and in the media, interesting questions also arise regarding their applications. The title of this article expresses that question. One reason for writing this forward-looking perspective/position article is because this author served in various editorial roles in certain publications, and knowing the challenges in finding reviewer support, this author believes that it will be highly beneficial if an AI/ML-based tool could be utilized for such review support. The scope of this article does not intend to provide demonstration of such a tool, since the development of such a tool will need significant collaboration between multiple entities involving academia, libraries, publishers, and industry. The article also discusses the huge energy consumption of AI/ML-based tools.
