Impact of Real-World Energy Consumption Variance on Internet of Things Node Lifetime Predictions

Node lifetime predictions are a crucial design time tool when developing Internet of Things (IoT) solutions with constrained energy budgets. However, this analysis is typically based on simplistic analyses of current consumption values based on datasheets and static duty cycles. This leads to an optimistic prediction of the node lifetime. Real-world measurements show a variation in the energy consumption that can significantly reduce the predicted node lifetime. In this paper, we aim to analyze the impact of the experienced variation for a given IoT platform and typical sensing tasks. To do this, we present a design case study in smart agriculture, where we perform empirical measurements to analyze energy consumption variability and its effect on as well as challenges regarding different design decisions. In addition, we suggest an empirical modeling method to enhance the energy efficiency of IoT nodes. The results show that the variations have a significant impact on node lifetime and should be considered in estimations in the future, as they show the design space to be considered when building robust systems.