With the rapid advancement of Internet of Things and artificial intelligence, device automation systems have become increasingly integrated with physical environments, introducing new security challenges for Trigger-Action Programming. An improper configuration of TAP rules may lead to severe cascading threats. However, existing methods typically rely on predefined safety properties and fail to capture the underlying semantic dependencies and interactions among rules. To address these limitations, we propose TAPGuard, a semantics-enhanced framework for TAP rule linkage modeling and cascading threat detection. Specifically, we identify two types of cascading threats: explicit threats, which arise from direct device interactions, and implicit threats, which are induced by shared environmental variables and may propagate across semantically related but structurally disconnected rules. TAPGuard leverages large language models to extract structured semantic elements from natural language rule descriptions and incorporates a semantic alignment module to assess the functional similarity between rules. Building on this, we propose a dual-relation context encoder incorporating node-level and semantic-level attention to model heterogeneous dependencies and enable multi-hop relational reasoning in the heterogeneous TAP rule graph. We evaluate TAPGuard on a real-world smart home dataset and demonstrate its effectiveness in detecting cascading threats. Experimental results show that TAPGuard significantly outperforms state-of-the-art graph-based baselines.

Traffic congestion is becoming an increasingly prominent problem, and intelligent traffic signal control methods can effectively alleviate it. Recently, there has been a growing trend of applying reinforcement learning to traffic signal control for adaptive signal scheduling. However, most existing methods focus on improving traffic performance while neglecting the issue of scheduling fairness, resulting in long waiting time for some vehicles. Some works attempt to address fairness issues but often sacrifice transport performance. Furthermore, existing methods overlook the challenge of sample efficiency, especially when dealing with diversity-limited traffic data. Therefore, we propose a Fairess-aware and sample-Efficient traffic signal control method called FELight. Specifically, we first design a novel fairness metric and integrate it into decision process to penalize cases with high latency by setting a threshold for activating the fairness mechanism. Theoretical comparison with other fairness works proves why and when our fairness could bring advantages. Moreover, counterfactual data augmentation is employed to enrich interaction data, enhancing the sample efficiency of FELight. Self-supervised state representation is introduced to extract informative features from raw states, further improving sample efficiency. Experiments on real traffic datasets demonstrate that FELight provides relatively fairer traffic signal control without compromising performance compared to state-of-the-art approaches.

Modular Self-Reconfigurable Robot (MSRR) systems are a class of robots capable of forming higher-level robotic systems by altering the topological relationships between modules, offering enhanced adaptability and robustness in various environments. This paper presents a novel MSRR called MODUR, featuring dual-level reconfiguration capabilities designed to integrate reconfigurable mechanisms into MSRR. Specifically, MODUR can perform high-level self-reconfiguration among modules to create different configurations, while each module is also able to change its shape to execute basic motions. The design of MODUR primarily includes a compact connector and scissor linkage groups that provide actuation, forming a parallel mechanism capable of achieving both connector motion decoupling and adjacent position migration capabilities. Furthermore, the workspace, considering the interdependent connectors, is comprehensively analyzed, laying a theoretical foundation for the design of the module’s basic motion. Finally, the motion of MODUR is validated through a series of experiments.

The increased utilization of Trigger-Action Programming (TAP) rules in smart homes has raised concerns regarding potential security threats in the interactions between smart digital devices/online services (DD/OS) and the physical environment. To ensure the secure use of intelligent and convenient infrastructure for users, we introduce an approach aimed at detecting potential security threats. In this paper, we propose IoT security threat models and categorize the threats into Risky DD/OS with Physical Security, Contradictory Operation of DD/OS and Environmental Impact Conflict. To effectively detect security threats, we construct an Internet of Things-Heterogeneous Information Networks (IoT-HIN) and enhance it with a knowledge base tool, transforming it into a knowledge-based IoT-HIN. We establish meta-paths to conduct analysis of events triggered by rules, and a threat detection algorithm is proposed to identify potential security threats and determine the rules leading to these threats. The proposed approach is validated using a real-world dataset, and the experimental results demonstrate its efficiency and practicality. Furthermore, a comparative analysis with similar works is conducted to highlight the superiority of our proposed approach.

The rapid expansion of the Internet of Things (IoT) has led to the development of smart homes and automation systems. Trigger-action programming (TAP) has emerged as a prevalent paradigm used in IoT, facilitating the creation of automation rules. However, with the proliferation of TAP rules, the potential for conflicts between them grows significantly, which results in undesired outcomes or even safety risks. In this article, we propose a context-aware conflict detection framework for TAP rules, called CCDF-TAP, to identify the potential rule conflicts. Specifically, we incorporate external knowledge and context information during the TAP data preprocessing stage, which is conducive to accurately defining the rule conflicts. Then, based on the above information, the conflict types are defined and a conflict graph is constructed, which establishes a unified format for the rule conflict detection task. Finally, we propose a novel algorithm called dual-channel graph attention auto-encoders (DualGAAs) for efficient conflict detection, which takes the conflict graph as the input and excels in accurately identifying conflicts. Extensive experiments conducted on a comprehensive IFTTT data set demonstrate the superiority of DualGAA in detecting conflicts, achieving an exceptional accuracy of 98.85% and an F1 score of 98.91%. The contributions of our study offer a comprehensive end-to-end solution for context-aware conflict detection in TAP rules, thereby significantly enhancing the security and dependability of IoT smart home systems.

Traffic congestion is becoming an increasingly prominent problem, and intelligent traffic signal control methods can effectively alleviate it. Recently, there has been a growing trend of applying reinforcement learning to traffic signal control for adaptive signal scheduling. However, most existing methods focus on improving traffic performance while neglecting the issue of scheduling fairness, resulting in long waiting time for some vehicles. Some works attempt to address fairness issues but often sacrifice transport performance. Furthermore, existing methods overlook the challenge of sample efficiency, especially when dealing with diversity-limited traffic data. Therefore, we propose aFairness-aware and sample-Efficient traffic signal control method called FELight. Specifically, we first design a novel fairness metric and integrate it into decision process to penalize cases with high latency by setting a threshold for activating the fairness mechanism. Theoretical comparison with other fairness works proves why and when our fairness could bring advantages. Moreover, counterfactual data augmentation is employed to enrich interaction data, enhancing the sample efficiency of FELight. Self-supervised state representation is introduced to extract informative features from raw states, further improving sample efficiency. Experiments on real traffic datasets demonstrate that FELight provides relatively fairer traffic signal control without compromising performance compared to state-of-the-art approaches. Our codes are available athttps://github.com/dxnbbsw/FELight.

Nowadays, users can personalize Internet of Things (IoT) devices/web services via trigger-action programming (TAP). As the number of connected entities grows, the relations of triggers and actions become progressively complex (i.e., the heterogeneity of TAP), which becomes a challenge for existing models to completely preserve the heterogeneous data and semantic information in trigger and action. To address this issue, in this article, we propose IoT nonnegative matrix factorization (IoT-NMF), a NMF-based heterogeneous graph embedding method for TAP. Prior to using IoT-NMF, we map triggers and actions to an IoT heterogeneous information network, from which we can extract three structures that preserve heterogeneous relations in triggers and actions. IoT-NMF can factorize the structures simultaneously for getting low-dimensional representation vectors of the triggers and actions, which can be further utilized in Artificial Intelligence of Things applications (e.g., TAP rule recommendation). Finally, we demonstrate the proposed approach using an if this then that (IFTTT) dataset. The result shows that IoT-NMF outperforms the state-of-the-art approaches.

The advent of fifth generation (5G) enables the Internet of Things (IoT) to connect a massive number of things. The interaction and communication among these things generate an enormous amount of context-aware data that is semantically diverse. Traditional data representation approaches, such as semantic annotation, ontology, and semantic Web technology are rule based, which lack flexibility and adaptability when applied to IoT. To address the challenge, this article mainly focuses on the problem of semantic representation, which is essential for processing and fusion of IoT data. To serve as a bridge, we propose a high-level framework, namely, Things2Vec, which aims to produce the latent semantic representations from the interaction of things through the graph embedding technique. These semantic representations benefit various IoT semantic analysis tasks, such as the IoT service recommendation and automation of things. In Things2Vec, we utilize the graph to model the function sequence relationships that are generated by the interaction of things, which is called the IoT context graph. Since these function sequence relationships are heterogeneous in terms of semantics, it causes general graph embeddings to fail to learn complete information. Thus, we propose a biased random walk procedure, which is tailored to capture the neighborhoods of nodes with different types of semantic relationships. Extensive experiments are carried out, and our results show that the proposed method can effectively capture the semantic relationships among context-aware data in IoT.