An Edge-Based Architecture for Pay-Per-Use IoT Systems Integrated with the Lightning Network
Internet of Things; Micropayments; Lightning Network; Edge Computing; Pay-per-use.
The adoption of Internet of Things (IoT) systems has increased significantly in recent years, with many systems requiring payment mechanisms to support service provision and maintenance. Blockchain-based and cryptocurrency-based solutions have been investigated and have also attracted increasing interest as a means to support automated micropayments in IoT systems, since they can be accessed programmatically and generally rely on open protocols. However, several challenges remain when integrating these solutions with IoT architectures, such as hardware requirements, scalability, security, interoperability, as well as cryptocurrency transaction fees and latency. The Lightning Network (LN) addresses some of the challenges of cryptocurrencybased solutions, such as high transaction fees, transaction latency, and scalability limitations. However, the protocol was not originally designed for IoT devices, requiring additional work to enable its use in such environments. This dissertation proposes an architecture, named the Lightning Integrated Node Architecture (LINA), that enables pay-per-use operation in IoT systems by integrating the LN with an edge node that offloads payment processing and interacts with IoT devices. The architecture is designed to be lightweight and capable of running on constrained edge hardware, while maintaining compatibility with existing LN infrastructure. A functional prototype was implemented and evaluated on a Raspberry Pi 4 Model B. The results demonstrate that the LINA architecture is viable and operates on constrained edge hardware while sustaining approximately 110 concurrent devices under stable conditions without exhausting CPU, memory, disk, or network resources. These f indings confirm that the system is not fundamentally constrained by hardware limitations and is sufficiently lightweight for deployment on constrained edge platforms, leaving room for architectural and implementation optimizations to further increase scalability.