Compared to testbeds, the efficiency and accuracy of wireless networking simulations are constantly questioned by the network community. It is widely accepted today that the current network simulators are not able to fully represent the real wireless characteristics, especially at the physical (PHY) layer. This affects the trustability of simulation-based performance evaluations. On the other hand, testbed experiments require taking a tedious and time-consuming implementation path. This path could be significantly reduced by using realistic network simulators as a first step to test novel algorithms or protocols. Therefore, we took on the challenge of representing the link characteristics of the indoor testbed of the Berlin Open Wireless Lab (BOWL) project in the ns-3 simulator. Our extensive measurements study of the link characteristics, namely received signal strength (RSS), frame detection ratio (FDR) and frame error ratio (FER), produced several guidelines for modeling our testbed with satisfying accuracy in the simulator. More importantly, the proposed empirical models take into account several crucial properties related to the radio hardware and the environment, which are shown to have a significant impact on the simulation accuracy. We validate our model against testbed results and show that, unlike the existing models in ns-3, our model shows high agreement with the measurement results for any pair of nodes in the testbed.
The wireless networking community continuously questions the accuracy and validity of simulation-based performance evaluations. The main reason is the lack of widely-accepted models that represent the real wireless characteristics, especially at the physical (PHY) layer. Hence, the trend in wireless networking is to rely more and more on testbeds, which on one hand bring more realism to network and protocol evaluation, but on the other hand present a high implementation barrier before an idea is ready to be tested. Therefore, realistic network simulators are still very much needed to reduce the time and effort for "concept testing" of novel ideas. In this case, the main question is how detailed should wireless simulators be to evaluate network and protocol performance. In this paper, we attempt a first answer to this question by using the Berlin Open Wireless Lab (BOWL) indoor model (BIM) in the ns-3 simulator. BIM includes several measurement-based models to characterize wireless communication such as frame detection ratio (FDR), frame error ratio (FER), capture and interference models. Through extensive measurements, we analyze the accuracy that we obtain with these PHY-layer models. Our experiments also show whether the detailed models at the PHY layer play an important role to represent transport layer performance in simulations.
In order to compete for a prominent market share, network operators and service providers should retain and increase the verticals' subscription, catering to their needs in order to differentiate themselves from competitors. In this scenario, verticals' satisfaction arises of paramount importance. As such, user experience is becoming a reliable indicator for service providers and telecommunication operators to convey overall end-to-end system functioning. To properly estimate end user satisfaction, operators and service providers require efficient means for quality monitoring and estimation at all layers, in conjunction with mechanisms able to maintain said quality at optimum levels. Given these factors, this paper proposes a mechanism for Quality of Perception (QoP) estimation in e-Health services, enabling the QoP-aware management of network slices fulfilling the requirements of supported services. To this end, the paper proposes a cognitive-based architecture which allows for the collection and monitoring of verticals' data to estimate QoP and provides mechanisms to re-configure the underlying network slices according to the monitored quality levels. A machine learning (ML) model is introduced that aims to forecast any future degradation in the quality perceived by vertical users. In case of a predicted degradation, the proposed architecture reacts and triggers the necessary remedial actions, referred as actuations. In order to evaluate the developed ML model and to showcase the
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