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.
|Titel||Proceedings - 2019 IEEE International Congress on Cybermatics : 12th IEEE International Conference on Internet of Things, 15th IEEE International Conference on Green Computing and Communications, 12th IEEE International Conference on Cyber, Physical and Social Computing and 5th IEEE International Conference on Smart Data, iThings/GreenCom/CPSCom/SmartData 2019|
|Status||Udgivet - 2019|
|Begivenhed||2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) - Atlanta, USA|
Varighed: 14 jul. 2019 → 17 jul. 2019
|Konference||2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)|
|Periode||14/07/2019 → 17/07/2019|