Towards Data-Driven Control of QoS in IoT: Unleashing the Potential of Diversified Datasets

Abstract

Cognition is of paramount importance in modern communication systems for this brings the potential for adaptiveness and self-fine-tuning for dynamic reconfigurability. To achieve this feat, two primary tasks are to identify the influential configurable parameters and availability of comprehensive datasets representative of the real-world scenarios rather than simulated ones. For this article, an extensive dataset covering diverse settings of wireless sensor networks (WSNs) driven internet of things (IoT) is collected. It covers broad variations of 10 pre-configured communication parameters as well as some runtime information. In addition to legacy parameters (e.g., transmission power, and packet size, etc.), we also used two different medium access control protocols (i.e., carrier sense multiple access (CSMA) and time-slotted channel hopping (TSCH)), and routing metrics (i.e., objective function 0 (OF0), minimum rank with hysteresis (MRH), MRH with expected transmission count (ETX2)). Important quality of service (QoS) metrics like packet delivery ratio, throughput, and energy consumption against all combinations of the communication parameters are measured and recorded. A statistical analysis is carried out to identify the correlations among the communication parameters and QoS metrics. The results lay the foundation for the design of a data-driven framework for predictive QoS control in the IoT.