The Internet of Things (IoT) is a growing network, supporting a wide variety of service types with specific network requirements that differ from traditional human type communications. This has led to emergence of dedicated IoT network standards. To optimize investments for dedicated network infrastructures, we’re investigating a dynamic approach in network capacity planning to accommodate multiple IoT traffic types over a cellular network, while maintaining their specific requirements.
We studied models of IoT traffic and used machine learning in prediction and scheduling of future workload under heterogeneous and variable traffic conditions when human-type and machine-type communications are mixed.
An integrated analytics framework including Hadoop and Spark were deployed for experimentation and a number of capacity planning use cases were implemented to verify the accuracy of the method.