## 5 State-of-the-Art
### Edge-Cloud Integration  
Studies have discussed the increasing integration of IoT devices and cloud servers to enhance the efficiency of IoT applications [111, 112]. While Wasm is considered a potential solution to overcome the heterogeneity between devices and servers, it presents challenges for resource-constrained devices. The authors of the aforementioned studies proposed WAIT, a lightweight Wasm runtime for such devices, which optimizes memory usage and ensures safe sandbox execution while reducing energy consumption. Several articles have discussed the challenges and potential of harnessing data from previously untapped sources in the manufacturing industry [113, 114], including a concept and prototype for retrofitting aged machines using Wasm on edge devices. This concept provides a uniform development environment from the cloud to the edge, achieving near-native performance with modularity similar to container-based service architectures. Another work looked into the interest in edge computing and computational offloading due to latency and privacy concerns along with the spread of IoT devices. The authors assessed how popular technologies achieve portability and migratability, and highlighted the benefits of Wasm as a platform for femtocloud offloading. The same paper introduced Nomad, a Wasm environment capable of live-migrating across different systems and hardware architectures [115]. A thesis [116] investigated the use of Wasm for computational offloading at the edge in the context of edge computing and 5G. The aim was to improve program performance by reducing the execution time and energy consumption on the end device. A proof-of-concept offloading system was presented and evaluated through several use cases. Another work discussed the high demand for computing power due to the rapid growth of IoT and AI and how edge computing can meet these demands [117]. Existing offloading approaches based on virtual machines and containers are criticized for their slow operation and large memory footprint. This work proposes LAWOW, a lightweight and high-performance framework that utilizes the Wasm runtime wasmedge for task offloading, providing a cross-platform solution to reduce computation costs and memory footprint. Identified Gaps:
* Analyzing the provided articles, the key research gaps in the field of Wasm, especially when applied to edge computing, IoT devices, and serverless architectures, appear to be as follows:
* **Efficient Execution on Resource-Constrained Devices:**[111] discusses the problem of executing Wasm on resource-constrained IoT devices. It appears that further research is needed to optimize the execution of Wasm applications on devices with limited memory, processing power, or energy resources. * **Migration and Portability:**[108, 109, 115] highlight the need for more research into the migration and portability of Wasm applications. There is a need for frameworks or solutions that allow Wasm applications to be dynamically deployed, migrated, or partitioned without changing their form while working across different operating systems and hardware architectures. * **Performance Analysis and Optimization:** while many of the discussed works [111, 113, 114, 116] involve efforts to optimize the performance of Wasm applications, it is clear that more research is needed in order to understand the performance implications of Wasm in different contexts, e.g., edge computing, IoT, offloading computational tasks, etc., and to further improve it. * **Secure and Multi-Tenancy Executions:**[110] hints at the challenge of ensuring secure and isolated execution environments for Wasm in serverless computing contexts. Further research into ways of ensuring security and facilitating multi-tenancy executions using Wasm might be needed. * **Applications in Industrial Retrofitting:** two articles [113, 114] indicate a gap in the application of Wasm in retrofitted industrial equipment. More studies are needed in order to test and evaluate how Wasm can be implemented in real-world industrial settings.