Web of Things (WoT) technology standardizes the integration of various IoT devices deployed in daily environments, promoting the capability of applications to automatically sense and regulate the physical environment.Meanwhile, the complex nature of such a ubiquitous software system, where heterogeneous applications, user activities, and environment states collectively influence device behaviors, poses risks of unexpected or even hazardous safety and security violations caused by improper device operations.Existing works on WoT violation identification primarily focus on the sole analysis of software applications, however, lacking consideration of the multi-source violations stemming from the human-cyberphysical ternary spaces, as well as the intricate interplay between environment and devices.Furthermore, the investigation into users' preferences for violation resolution remains unexplored.To address these limitations, we introduce EnvGuard, an environment-centric approach for customizing safety and security properties, identifying violations, and executing resolutions in the WoT environment.Our evaluation in two real-world WoT systems shows that Env-Guard outperforms previous state-of-the-art works, and confirms its usability, effectiveness, and runtime efficiency. CCS Concepts•

Large-scale scenarios, such as drone-based search and rescue, often require seamless access to devices distributed across hierarchical edge clusters. However, existing multi-cluster communication solutions designed for cloud environments cannot be directly applied to edge environments due to resource constraints, network limitations, and privacy concerns. To address these challenges, this paper introduces EdgeConnector, a lightweight middleware specifically designed to enable seamless and efficient cross-cluster device access in edge environments. EdgeConnector consists of components deployed across superior and subordinate clusters and employs a compact mechanism leveraging eXpress Data Path (XDP) for highly efficient device communication between clusters. The middleware was evaluated in both real-world and simulated environments. A real-world case study demonstrates its practicality and usability, while experimental results from the simulated environment highlight its superior performance. Specifically, EdgeConnector achieves an 80% reduction in latency and a 90% reduction in CPU usage under high-load conditions compared to the leading existing solution for cross-cluster service access.

Trigger-Action Programming (TAP) is a popular end-user programming paradigm for constructing automation applications to orchestrate smart device collaboration. Existing TAP platforms employ a device-centric approach to programming and executing TAP rules, which suffers limited flexibility and reusability when a same automation requirement is effective in different location. To this end, we develop a tool named laTAPE to support location-aware trigger-action programming and executing. laTAPE supports users to specify triggers, condition states and actions involving locations which refer to either runtime user location or a predefined location. During runtime, laTAPE achieves the rule execution by leveraging corresponding environment devices determined by the user location obtained from smartphone. Our evaluation on real-world case study demonstrates usability and feasibility of laTAPE in rule programming and executing.

The physical world we live in is accelerating digitalization with the vigorous development of Internet of Things (IoT). Following this trend, Web of Things (WoT) further enables fast and efficient creation of various applications that perceive and act on the physical world using standard Web technologies. A popular way for creating WoT applications is Trigger-Action Programming (TAP), which allows users to orchestrate the capabilities of IoT devices in the form of “if trigger, then action”. However, existing TAP approaches don’t support scenario-centric WoT applications which involve abstract modeling of physical environments and complex spatio-temporal dependencies between events and actions. In this paper, we propose an approach called SCTAP which supports Scenario-Centric Trigger-Action Programming based on software-defined physical environments. SCTAP defines a structured and conceptual representation for physical environments, which provides the required programming abstractions for WoT applications. Based on the representation, SCTAP defines a grammar for specifying scenario-centric WoT applications with spatio-temporal dependencies. Furthermore, we design a service-based architecture for SCTAP which supports the integration of device access, event perception, environment representation, and rule execution in a loosely-coupled and extensible way. We implement SCTAP as a WoT infrastructure and evaluate it with two case studies including a smart laboratory and a smart coffee house. The results confirm the usability, feasibility and efficiency of SCTAP and its implementation.

Adversarial examples have emerged as increasingly severe threats for deep neural networks. Recent works have revealed that these malicious samples can transfer across different neural networks, and effectively attack other models. The state-of-the-art methodologies leverage Fast Gradient Sign Method to generate obstructing textures, which can cause neural networks to make incorrect inferences. However, the over-reliance on task-specific loss functions makes the adversarial examples less transferable across networks. Moreover, recent de-noising based adaptive defences provide promising performance against aforementioned attacks. Therefore, to achieve better transferability and attack effectiveness, we propose a novel attack, referred to as the Fabricate-Vanish (FV) attack, which is able to erase benign representations and generate obstruction textures simultaneously. The proposed FV attack treats the adversarial example transferability as latent contribution for each layer of deep neural networks, and maximizes the attack performance by balancing transferability and task specific loss function. Our experimental results on ImageNet show that the proposed FV attack achieves the best attack performance and better transferability by degrading the accuracy of classifiers 3.8% more on average compared to the state-of-the-art attacks.