A 5G campus network is a 5G network for the users affiliated with the campus organization, e.g., an industrial campus, covering a prescribed geographical area. A 5G campus network can operate as a so-called 5G non-standalone (NSA) network (which requires 4G Long-Term Evolution (LTE) spectrum access) or as a 5G standalone (SA) network (without 4G LTE spectrum access). 5G campus networks are envisioned to enable new use cases, which require cyclic delay-sensitive industrial communication, such as robot control. We design a rigorous testbed for measuring the one-way packet delays between a 5G end device via a radio access network (RAN) to a packet core with sub-microsecond precision as well as for measuring the packet core delay with nanosecond precision. With our testbed design, we conduct detailed measurements of the one-way download (downstream, i.e., core to end device) as well as one-way upload (upstream, i.e., end device to core) packet delays and losses for both 5G SA and 5G NSA hardware and network operation. We also measure the corresponding 5G SA and 5G NSA packet core processing delays for download and upload. We find that typically 95% of the SA download packet delays are in the range from 4-10 ms, indicating a fairly wide spread of the packet delays. Also, existing packet core implementations regularly incur packet processing latencies up to 0.4 ms, with outliers above one millisecond. Our measurement results inform the further development and refinement of 5G SA and 5G NSA campus networks for industrial use cases. We make the measurement data traces publicly available as the IEEE DataPort 5G Campus Networks: Measurement Traces dataset (DOI 10.21227/xe3c-e968).

Flow routing can achieve fine-grained network performance optimizations by routing distinct packet traffic flows over different network paths. While the centralized control of Software-Defined Networking (SDN) provides a control framework for implementing centralized network optimizations, e.g., optimized flow routing, the implementation of flow routing that is adaptive to varying traffic loads requires complex models. The goal of this study is to pursue a model-free approach that is based on reinforcement learning. We design and evaluate QR-SDN, a classical tabular reinforcement learning approach that directly represents the routing paths of individual flows in its state-action space. Due to the direct representation of flow routes in the QR-SDN state-action space, QR-SDN is the first reinforcement learning SDN routing approach to enable multiple routing paths between a given source (ingress) switch–destination (egress) switch pair while preserving the flow integrity. That is, in QR-SDN, packets of a given flow take the same routing path, while different flows with the same source-destination switch pair may take different routes (in contrast, the recent DRL-TE approach splits a given flow on a per-packet basis incurring high complexity and out-of-order packets). We implemented QR-SDN in a Software-Defined Network (SDN) emulation testbed. Our evaluations demonstrate that the flow-preserving multi-path routing of QR-SDN achieves substantially lower flow latencies than prior routing approaches that determine only a single source-destination route. A limitation of QR-SDN is that the state-action space grows exponentially with the number of network nodes. Addressing the scalability of direct flow routing, e.g., through routing only high-rate flows, is an important direction for future research. The QR-SDN code is made publicly available to support this future research.

In the early 2020s, the coronavirus pandemic brought the notion of remotely connected care to the general population across the globe. Oftentimes, the timely provisioning of access to and the implementation of affordable care are drivers behind tele-healthcare initiatives. Tele-healthcare has already garnered significant momentum in research and implementations in the years preceding the worldwide challenge of 2020, supported by the emerging capabilities of communication networks. The Tactile Internet (TI) with human-in-the-loop is one of those developments, leading to the democratization of skills and expertise that will significantly impact the long-term developments of the provisioning of care. However, significant challenges remain that require today’s communication networks to adapt to support the ultra-low latency required. The resulting latency challenge necessitates trans-disciplinary research efforts combining psychophysiological as well as technological solutions to achieve one millisecond and below round-trip times. The objective of this paper is to provide an overview of the benefits enabled by solving this network latency reduction challenge by employing state-of-the-art Time-Sensitive Networking (TSN) devices in a testbed, realizing the service differentiation required for the multi-modal human-machine interface. With completely new types of services and use cases resulting from the TI, we describe the potential impacts on remote surgery and remote rehabilitation as examples, with a focus on the future of tele-healthcare in rural settings.

Congestion Control (CC) limits the transmission rate of data packets in the transport layer protocol, e.g., in TCP and QUIC, of a sending endpoint so as to mitigate the congestion in packet-switched communication networks. Numerous studies have developed and evaluated Congestion Control Algorithms (CCAs) for a wide range of network types. Prior surveys have covered the functioning of the existing CCAs and their performance characteristics. In contrast, this survey focuses on the quantitative evaluation of CCAs; specifically, we comprehensively survey the CCA evaluation methodologies, i.e., the why and how of CCA evaluations.We define a CCA evaluation as a combination of a prescribed evaluation scenario with prescribed evaluation metrics. In turn, we define an evaluation scenario as a combination of a prescribed network (e.g., path) model and a particular traffic model. We present comprehensive sets of network and traffic model characteristics (i.e., network scenario characteristics) that can influence the performance of CCAs, as well as a comprehensive set of evaluation metrics for measuring the performance of CCAs. We taxonomize CCA evaluations into the main categories: Steady-state, transient-state, network variation, dynamic (e.g., wireless) network, and competition evaluations. We place and survey the existing CCA evaluation studies in this taxonomy to uncover the main trends and shortcomings of the CCA evaluation field to date. To facilitate the comprehensive rigorous evaluation of CCAs, we have developed the publicly available CCA evaluation framework ccperf, which incorporates the insights from our survey. We also outline future research directions to further advance the comprehensive rigorous evaluation of CCAs.

Existing Visual Simultaneous Localization And Mapping (vSLAM) approaches that offload the complex self-localization computations from mobile robots over a wireless network to edge computing are limited to static offloading, i.e., the offloaded computation tasks are offloaded permanently. However, wireless networks are inherently dynamic and may excessively delay the transmissions between a mobile device and the edge during periods of poor wireless network quality, e.g., from fading or temporary obstructions. We propose and evaluate Dynamic Visual SLAM Network Offloading (DynNetSLAM) to dynamically adapt the vSLAM computation offloading according to the measured wireless network latency. As groundwork towards developing DynNetSLAM, we first enhance the existing state-of-the-art vSLAM approaches through judicious parameter settings and parallel map updates to enable the tracking of common fast vSLAM data sets. We introduce an offloading latency threshold along with a safe zone and a hysteresis around the threshold to control the dynamic offloading. Our extensive evaluations with public vSLAM data sets indicate that DynNetSLAM with the hysteresis substantially reduces the probability of track loss events compared to the state-of-the-art ORB-SLAM2 approach for processing statically on the mobile device and the enhanced static Edge SLAM. Also, DynNetSLAM nearly attains the low absolute position error and only slightly increases the CPU utilization compared to the enhanced static Edge SLAM.

In edge-assisted visual Simultaneous Localization and Mapping (SLAM), mobile devices offload computationally intensive tasks to an edge cloud. The mobile device should transfer its input data to the edge cloud in a resource-efficient way, especially in visual SLAM systems with their very large image frame input data. Most state-of-the-art systems use a conventional Analyze-Then-Compress (ATC) approach that pre-analyzes the captured frames on the mobile device (incurring substantial processing latencies), and transmits only the (several times smaller) pre-analysis results, namely only the feature representation of so-called key frames (and the corresponding tracking results), to the edge. We examine two novel transmission methods (ATC workflows) for "functional-split" edge-assisted visual SLAM: Feature-Representation-Every-Frame (FREF) transmits the feature representation for every captured frame to the edge, and the edge performs the key frame creation processing (including the tracking); Feature-Representation-Only Key Frames (FROKF) transmits only the feature representation of key frames without the tracking results to the edge, and the edge performs tracking. We evaluate these ATC methods in terms of the required network throughput (bandwidth), as well as the latencies for transmission and processing, as well as the resulting end-to-end latency (from frame capture at mobile device to key frame and tracking results becoming available at the edge cloud) via testbed measurements for various computing hardware platforms. Compared to the existing conventional approach, our newly introduced FREF method reduces the end-to-end latency down to one quarter; while our FROKF method can reduce the required throughput down to one sixth. Additionally, feature compression can reduce the required throughput; within the FROKF method, down to one twentieth compared to the conventional method without feature compression, albeit at the expense of significantly increased processing latency for the compression.