Low-Power Wide Area Networks (LP-WANs) are emerging as a promising solution for connecting Internet of Things and Machine Type Communication devices. If ultra-narrowband (UNB) networks, a subclass of LP-WANs, reach predicted deployment numbers and densities, they will face two challenges: inter-technology and intra-technology interference. This paper proposes the first experimental architecture designed for the optimization of UNB networks. We illustrate its implementation on a case study of a SIGFOX network and the resulting extension of the existing LOG-a-TEC testbed. The proposed architecture enables context data collection, context model development, optimization and transmission control using rapid experimentation cycle approach enabled by flow based programming using Node-RED.

General representation, abstraction and exchange definitions are crucial for dynamically configurable context recognition. However, to evaluate potential definitions, suitable standard datasets are needed. This paper presents our effort to create and maintain large scale, multimodal standard datasets for context recognition research. We ourselves used these datasets in previous research to deal with placement effects and presented low-level sensor abstractions in motion based on-body sensing. Researchers, conducting novel data collections, can rely on the toolchain and the the low-level sensor abstractions summarized in this paper. Additionally, they can draw from our experiences developing and conducting context recognition experiments. Our toolchain is already a valuable rapid prototyping tool. Still, we plan to extend it to crowd-based sensing, enabling the general public to gather context data, learn more about their lives and contribute to context recognition research. Applying higher level context reasoning on the gathered context data is a obvious extension to our work.