Rethinking IoT Network Reliability in the Era of Machine Learning

In the Internet of Things (IoT), wireless sensor networks are often paired with machine learning frameworks to deliver applications of high societal impact and support
critical infrastructures. In this context, this paper investigates the relationship between network reliability and the reliability of the machine learning framework in terms of prediction accuracy. Our experimental analysis leverages six data sets of various degrees of information redundancy and considers four machine learning algorithms that are commonly used for classification. In turn, packet loss is inserted in the raw input data, emulating various networking loss patterns in terms of burstiness. The experimental results consistently demonstrate a non-linear relationship between the reliability of the network and the accuracy of the machine learning classifier, indicating that not all data packets are equally valuable to the application performance. We conclude with recommendations for IoT practitioners and IoT system designers.