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.
Because of the ageing population, the demand for assisted living solutions that can help prolonging independent living of elderly at their homes with reduced interaction with caregivers is rapidly increasing. One of the most important indicators of the users’ well-being is their motion and mobility inside their homes, used either on its own or as contextual information for other more complex activities such as cooking, housekeeping or maintaining personal hygiene. In monitoring users’ mobility, radio frequency (RF) communication technologies have an advantage over optical motion detectors because of their penetrability through the obstacles, thus covering greater areas with fewer devices. However, as we show in this paper, RF links exhibit large variations depending on channel conditions in operating environment as well as the level and intensity of motion, limiting the performance of the fixed motion detection threshold determined on offline or batch measurement data. Thus, we propose a new algorithm with an online adaptive motion detection threshold that makes use of channel impulse response (CIR) information of the IEEE 802.15.4 ultra-wideband (UWB) radio, which comprises an easy-to-install robust motion detection system. The online adaptive motion detection (OAMD) algorithm uses a sliding window on the last 100 derivatives of power delay profile (PDP) differences and their statistics to set the threshold for motion detection. It takes into account the empirically confirmed observation that motion manifests itself in long-tail samples or outliers of PDP differences’ probability density function. The algorithm determines the online threshold by calculating the statistics on the derivatives of the 100 most recent PDP differences in a sliding window and scales them up in the suitable range for PDP differences with multiplication factors defined by a data-driven process using measurements from representative operating environments. The OAMD algorithm demonstrates great adaptability to various environmental conditions and exceptional performance compared to the offline batch algorithm. A motion detection solution incorporating the proposed highly reliable algorithm can complement and enhance various assisted living technologies to assess user’s well-being over long periods of time, detect critical events and issue warnings or alarms to caregivers.
Precise location information will play an important role in 5G networks, their applications and services, especially in indoor environments. Ultra-wideband (UWB) technology offers exceptional temporal resolution enabling the emergence of high accuracy ranging-based indoor localization systems. In order to reduce time to market, developers need a reliable, fast and efficient method to evaluate localization and tracking system designs in the selected high-dimensional parameter space. Purely measurement-based performance evaluation of such systems is costly and cumbersome, so we propose the use of a radio frequency (RF) ray-tracing simulator augmented with a noise model based on measurements with localization equipment in real environment. We demonstrate the proposed approach by evaluating the UWB-based two-way-ranging (TWR) localization with the least squares (LS) and with the extended Kalman filter (EKF) tracking algorithms. We analyze the degradation of tracking performance due to time spreading of range measurements. Moreover, on a set of fixed locations we also show that the proposed approach is sufficiently representative for the pure measurement-based evaluation. The results obtained in a reference office environment show that EKF algorithm is twice as efficient and more resilient to the effects of time spreading of range measurements as LS algorithm.
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