## 7 Prospective Aspects of WebAssembly
### Prospective Alignments with Technologies  
As Wasm continues to evolve and mature, it is set to revolutionize various aspects of IoT applications and usher in a new era of innovation. The flexible, efficient, and secure characteristics of Wasm make it highly suitable for futuristic IoT use cases. This section delves deeper into the potential future applications of Wasm in the IoT landscape. * **Dew Computing** Dew computing, an emerging concept in the realm of IoT, envisions a paradigm in which computing and data processing capabilities are distributed across the network's edge, closer to the IoT devices and sensors themselves. In the context of futuristic use cases in IoT, Wasm can play a crucial role in enabling dew computing architectures. By leveraging Wasm, IoT devices can execute lightweight and efficient code at the edge, reducing reliance on cloud infrastructure and minimizing latency. Wasm's ability to run on resource-constrained devices makes it an ideal technology for enabling intelligent edge analytics and decision-making. With Wasm, IoT devices can perform real-time data processing, advanced machine learning tasks, and local decision-making all while maintaining low power consumption and ensuring fast response times. This empowers IoT deployments with the ability to derive valuable insights, make autonomous decisions, and respond to critical events in a timely manner even in scenarios with limited connectivity or stringent privacy requirements. Wasm's versatility and compatibility make it a promising technology for realizing the potential of dew computing in futuristic IoT use cases. * **WebAssembly and AI*
* AI has become a fundamental component in a variety of technology sectors, bringing automation and intelligence to tasks traditionally requiring human intervention. As we anticipate the future of Wasm, its role in enhancing and accelerating AI applications is an intriguing subject. This section delves into the potential intersections between Wasm and AI. * **Why Wasm for AI? *
* The use of Wasm in AI can be attributed to its core strengths of performance, portability, and security. These attributes can significantly influence the development, execution, and scalability of AI applications. * **Wasm and AI Models*
* Many AI models, especially those involving deep learning, require substantial computational power. Currently, these models are typically trained on powerful servers and executed on the edge, for instance, in a user's browser or on an IoT device. This execution often relies on JavaScript, which, while highly portable, does not deliver the performance of a compiled language. Wasm, with its near-native execution speed, offers a compelling alternative for executing AI models on the edge, potentially improving performance and responsiveness. * **Wasm and On-Device AI*
* Wasm can enhance on-device AI capabilities. In many scenarios, running AI models directly on end-user devices such as smartphones or IoT devices can provide benefits in terms of latency, privacy, and bandwidth. However, these devices often have diverse architectures and capabilities. Wasm's portability makes it an
excellent choice for running AI workloads in such a heterogeneous environment, allowing developers to write their code once and run it on various devices. * **Wasm and AI in the Browser** Wasm can accelerate AI in the browser context as well. It opens up possibilities for running complex AI models directly in the browser, allowing for real-time AI applications such as image recognition, natural language processing, and predictive modeling to run efficiently without the need for server-side computation. * **Decentralized Computing** With the advent of blockchain technologies and decentralized computing, there is a growing need for efficient, secure, and platform-independent computation models. Wasm's platform-agnostic nature in combination with its sandboxed execution environment make it an ideal candidate for this use case.