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.
Highlights
Sensing coupled with communication gave birth to the wireless sensor networks (WSNs)
It begins with the data collected from a WSN deployment, carries out statistical analysis to identify the relationships among the configurable parameters and quality of service (QoS) metrics
We presented the experiments conducted on a state-of-the-art real testbed (i.e., w-iLab.t) and dataset collected against a comprehensive set of parameters including inter-arrival time, packet size, maximum transmissions, number of nodes, network density/topology, MAC protocols, RPL objective functions, transmission power, and distance
Summary
Sensing coupled with communication gave birth to the wireless sensor networks (WSNs). In addition to the potential for sufficient accuracy, the solutions based on data-driven techniques and ML are self-learning and adaptive [8] that are amongst the most important traits in the modern system design These prospects motivate us to investigate the data-driven methods for a challenging problem like QoS in WSNs and IoT. Before delving into the prospects of data-driven models for QoS facilitation in WSNs and IoT, it is important to understand the legacy research cycle in the context. Different application-specific QoS requirements in unique physical settings can be met with particular configurations of parameters like MAC and routing protocols, modulation techniques, transmission power, traffic rates, packet sizes, etc. Discuss the research approaches and methods used in wireless communication and networking, highlight the limitations and constraints, and identify the potential on offer in the form of a data-driven approach.
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