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Network testing plays an important role in the iterative process of developing new communication protocols and algorithms.However, test environments have to keep up with the evolution of technology and require continuous update and redesign. In this paper, we propose COINS, a framework that can be used by wireless technology developers to enable continuous integration (CI) practices in their testbed infrastructure. As a proof-ofconcept, we provide a reference architecture and implementation of COINS for controlled testing of multi-technology 5G Machine Type Communication (MTC) networks. The implementation upgrades an existing wireless experimentation testbed with new software and hardware functionalities. It blends web service technology and operating system virtualization technologies with emerging Internet of Things technologies enabling CI for wireless networks. Moreover, we also extend an existing qualitative methodology for comparing similar frameworks and identify and discuss open challenges for wider use of CI practices in wireless technology development.

Abstract-TV White Spaces (TVWS) technology allows wireless devices to opportunistically use locally-available TV channels enabled by a geolocation database. The UK regulator Ofcom has initiated a pilot of TVWS technology in the UK. This paper concerns a large-scale series of trials under that pilot. The purposes are to test aspects of white space technology, including the white space device and geolocation database interactions, the validity of the channel availability/powers calculations by the database and associated interference effects on primary services, and the performances of the white space devices, among others. An additional key purpose is to perform research investigations such as on aggregation of TVWS resources with conventional resources and also aggregation solely within TVWS, secondary coexistence issues and means to mitigate such issues, and primary coexistence issues under challenging deployment geometries, among others. This paper provides an update on the trials, giving an overview of their objectives and characteristics, some aspects that have been covered, and some early results and observations.

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The current understanding of activity in the wireless spectrum is limited to mostly punctual studies of aggregated energy values. However, there is a need and increasing technological means for a better understanding of spectrum usage by automatically detecting and recognizing wireless transmissions in an unlicensed or shared frequency band. In this paper we propose, implement and evaluate a framework for automatic detection of wireless transmissions. Our framework includes a manual component as our assessment suggests manual labor has a paramount impact on tuning and maintaining good performance of an automatic transmission detection system. However, a considerable problem in this aspect is represented by the disagreement amongst human annotations which is a universally recognized issue. To this end, we discuss and evaluate challenges in generating labeled datasets that can then be used as ground truth for evaluating and possibly training automatic transmission detection systems. We also propose two methods for automatic transmission detection that are not based on machine learning and therefore do not need training data and evaluate their performance against each other and manually labeled data. Our results show that generating human-labeled ground truth data is an expensive and imperfect process. Humans on average require 90 minutes to label 56 minutes of unlicensed European narrowband spectrum. The experts that generate the ground truth sometimes only agree on as little as 40.18% of the labeled cases.