Passive probing

Abstract: In autonomous driving, several applications like teleoperated driving, back-end status verification, or online gaming for customer infotainment rely on low-latency communication. Ideally, we can select a route that best supports the applications’ requirements before the journey. Therefore, route selection for autonomous vehicles might require in-advance latency predictions. End-to-end (E2E) latency prediction is a difficult task, especially when considering that it needs to be achieved with limited active probing due to cost constraints. We study continuous latency prediction and application feasibility assessment (in terms of meeting the applications’ E2E latency requirements), using a custom-designed deep learning model that leverages feature engineering for prediction error reduction. We provide insights into the model behavior utilizing recent advances in explainable artificial intelligence. Moreover, we present a novel model-agnostic approach based on active learning to leverage passive probing data. A pre-trained model performs certainty sampling, predicts artificial labels to enlarge the training dataset, and trains iteratively on the augmented set. The results show a 5 % reduction in mean average error for continuous latency prediction and an increase of up to 2.8 % in macro F1 score due to the use of passive probing data.

Abstract: In one embodiment, during network operation, it is dynamically determined whether to change from passive probing of communication path metrics to active probing of communication path metrics.

Abstract: Estimating available network resources is fundamental when adapting the sending rate both at the application and transport layer. Traditional approaches either rely on active probing techniques or iteratively adapting the average sending rate, as is the case for modern TCP congestion control algorithms. In this paper, we propose a statistical method based on the inter-packet arrival time analysis of TCP acknowledgments to estimate a path available bandwidth. SABES first estimates the bottleneck link capacity exploiting the TCP flow slow start traffic patterns. Then, an heuristic based on the capacity estimation, provides an approximation of the end-to-end available bandwidth. Exhaustive experimentation on both simulations and real-world scenarios were conducted to validate our technique, and our results are promising. Furthermore, we train an artificial neural network to improve the estimation accuracy.

Abstract: The complexity within a Wireless Personal Area Network (WPAN) increases. Several technologies have to share the same radio spectrum. In this paper we take a look at the 2.4 GHz Industrial Scientific and Medical (ISM)-band. This paper discusses a method of selecting the best wireless channel within Wireless Local Area Network (WLAN) when several technologies could be used in the same WPAN range of the needed access point. The issue is to keep away from already occupied channels. The method is divided into four steps: the passive probing of the power level detecting IEEE 802.15.4 (ZigBee) channels using a new and affordable hardware, the transformation of the probed data to a linguistic level using Fuzzy Set Theory (FST), the classification of the data, and finally the sorting and selection of channels based on whose power levels.