Cost-Effective Processing of IoT Data in the Computing Continuum

Processing real-time data in the cloud has become the basis for various IoT applications. This processing typically incurs a high pricing cost, due to the use of paid messaging services (e.g., AWS SQS, or GCP Pub/Sub) that send real-time data from IoT devices at the edge to IoT applications in the cloud. Alternatively, costly AI-enabled IoT devices are employed for federated learning at the edge, which lowers the amount of data sent to the cloud. To reduce such costs, we propose an approach that sends real-time data from the IoT device to the cloud with reduced sampling frequency. In the cloud, we use AI to predict the missing data, and send both real and predicted data to the IoT application. This way, the IoT application receives the same amount of data, although less data is sent from the edge to the cloud (using messaging services). In addition, we measure the prediction error in the cloud, and adjust the sampling frequency at the edge, so that the error remains low. Thus, this approach lowers the cost of IoT applications by reducing the use of paid messaging services, and avoiding costly AI-enabled IoT devices, while also ensuring a low prediction error. For the evaluation, we build a prototype, and we experiment with real-world IoT data. Our experiments show promising results, including a reduction in costs by about 70% with a prediction error of around 5%.