Modeling the performance of IoT networks

Internet of Things (IoTs) is gaining increasing significance due to real-time communication and decision making capabilities of sensors integrated into everyday objects. Predicting performance in IoTs is critical for detecting performance bottlenecks, designing optimal sleep/wake-up schedules and application-aware performance tuning. However, performance prediction becomes a significant challenge in IoTs due to varying needs of applications coupled with the resource constrained nature of sensors. In this work, we analyze the impact of factors affecting performance in IoT networks using simulation based models. Further, an analytical framework is developed to model the impact of individual node behavior on overall performance using Markov chains. In particular, we derive steady state transition probabilities of transmit and receive states using protocol execution traces and further utilize them towards predicting per-flow throughput. Our proposed model is generic in that it can be applied across domains. Accuracy of the model is evaluated by comparing the predictions with the actual estimates obtained using simulations.