Web of Things (WoT) is an emerging concept to connect IoT devices to the web using standard interfaces. This provides interoperability between different IoT platforms and enables seamless integration with web and cloud services. However, running sophisticated web services directly on resource-constrained IoT devices is challenging due to limitations in memory, computation, and energy. This paper proposesWaWoT, aWasm-based framework for flexible and efficientWebofThings services.WaWoTallows flexible WoT service development using annotations and automatic partitioning. It also enables dynamic service migration using WebAssembly modules to adapt placement between IoT devices and web clients. We also introduce an ahead-of-time compiler optimized for low memory usage through techniques like streamed compilation and trimming. For energy efficiency, we use optimizations like bulk instruction writing and direct I/O accessing. Safety is ensured through compile-time and run-time analyses to guarantee sandboxed execution. Evaluations demonstrateWaWoTexhibits better flexibility than existing WoT development approaches. Furthermore,WaWoTcan also reduce RAM usage by 84.9x and energy consumption by 1.9x-4.9x over existing WebAssembly runtimes. Overall, it enables efficient, safe, and flexible WoT services on constrained IoT devices.

Customizing and deploying an edge system are time-consuming and complex tasks because of hardware heterogeneity, third-party software compatibility, diverse performance requirements, and so on. In this article, we present TinyEdge, a holistic framework for the low-code development of edge systems. The key idea of TinyEdge is to use a top-down approach for designing edge systems. Developers select and configure TinyEdge modules to specify their interaction logic without dealing with the specific hardware or software. Taking the configuration as input, TinyEdge automatically generates the deployment package and estimates the performance with sufficient profiling. TinyEdge provides a unified development toolkit to specify module dependencies, functionalities, interactions, and configurations. We implement TinyEdge and evaluate its performance using real-world edge systems. Results show that: (1) TinyEdge achieves rapid customization of edge systems, reducing 44.15% of development time and 67.79% of lines of code on average compared with the state-of-the-art edge computing platforms; (2) TinyEdge builds compact modules and optimizes the latent circular dependency detection and message routing efficiency; (3) TinyEdge performance estimation has low absolute errors in various settings.

Recent years have witnessed the progressive integration between IoT (Internet of Things) devices and the cloud server, which promotes the efficiency and interoperability of IoT applications. WebAssembly, known for its performance and portability, is considered a promising technology to bridge the heterogeneity between devices and the server. Nevertheless, resource-constrained devices, which are commonly deployed in the wild, have difficulty participating in this device-cloud integration because they can hardly run WebAssembly efficiently.

With the fast growth of the Internet of Things, people now are surrounded by plenty of devices. To achieve efficient interaction with these devices, human-device interaction technologies are evolving. Because existing methods (mobile App) require users to remember the mapping between the real-world device and the digital one, an important point is to break such a gap. In this paper, we propose VSLink, which offers human-device interaction in an Augmented-Reality-like manner. VSLink achieves fast object identification and pervasive interaction for fusing the physical and cyberspace. To improve processing speed and accuracy, VSLink adopts a two-step object identification method to locate the interaction targets. In VSLink, visual SLAM and object detection neural networks detect stable/-movable objects separately, and detection prior from SLAM is sent to neural networks which enables sparse-convolution-based inference acceleration. VSLink offers a platform where the user could customize the interaction target, function, and interface. We evaluated VSLink in an environment containing multiple objects to interact with. The results showed that it achieves a 33% network inference acceleration on state-of-the-art networks, and enables object identification with 30FPS video input.

We advocate ThingSpire OS, a new IoT operating system based on WebAssembly for cloud-edge integration. By design, WebAssembly is considered as the first-class citizen in ThingSpire OS to achieve coherent execution among IoT device, edge and cloud. Furthermore, ThingSpire OS enables efficient execution of WebAssembly on resource-constrained devices by implementing a WebAssembly runtime based on Ahead-of-Time (AoT) compilation with a small footprint, achieves seamless inter-module communication wherever the modules locate, and leverages several optimizations such as lightweight preemptible invocation for memory isolation and control-flow integrity. We implement a prototype of ThingSpire OS and conduct preliminary evaluations on its inter-module communication performance.

Customizing and deploying an edge system is a time-consuming and complex task, considering the hardware heterogeneity, third-party software compatibility, diverse performance requirements, etc. In this paper, we present TinyEdge, a holistic system for the rapid customization of edge systems. The key idea of TinyEdge is to use a top-down approach for designing the software and estimating the performance of the customized edge systems under different hardware specifications. Developers select and conFigure modules to specify the critical logic of their interactions, without dealing with the specific hardware or software. Taking the configuration as input, TinyEdge automatically generates the deployment package and estimate the performance after sufficient profiling. TinyEdge provides a unified customization framework for modules to specify their dependencies, functionalities, interactions, and configurations. We implement TinyEdge and evaluate its performance using real-world edge systems. Results show that: 1) TinyEdge achieves rapid customization of edge systems, reducing 44.15% of customization time and 67.79% lines of code on average compared with the state-of-the-art edge platforms; 2) TinyEdge builds compact modules and optimizes the latent circular dependency detection and message queuing efficiency; 3) TinyEdge performance estimation has low average absolute error in various settings.