Wireless Mesh Networks can be a valuable enhancement to network infrastructures in many contexts. Mesh nodes can extend the range of WiFi networks by acting as relays between source and destination nodes, which can be crucial for contexts where cabling is something to be avoided. Due to unforeseen degradations in the environment, however, certain links within the mesh may fail, leading to a lack of end-to-end connectivity, at which point the nodes must form new routes based on the employed protocol. Better Approach To Mobile Ad-Hoc Networking (BATMAN) is a very commonly employed mesh routing protocol that handles routing within mesh networks. This paper evaluates the end-to-end reconnection establishment performance of the protocol utilizing a hardware testbed, and discusses potential enhancements to improve reconnection latency. We also open source the software developed to automate our operations and measurements done on the testbed, for public reference and usage.

5G Non-Public Networks (NPNs) for industrial networking focus in particular on reliable low-latency communication to enable new use cases. Traditionally, the network itself as well as the end devices are black-box, closed-source platforms. This prevents a fine-grained analysis of the influence of the different components, when evaluating the end-to-end One-Way Delay (OWD). Recently, open-source 5G Radio Access Network (RAN) implementations reached a certain level of maturity allowing a higher separation of the components. In this work, we perform a comprehensive delay analysis with a focus on the User Equipment (UE) induced impact. Our results show that devices containing the widely used Qualcomm X55 chipset introduce an additional delay of up to 6ms, compared to the successive Qualcomm X62. This puts a lot of previous 5G related measurement campaigns into a new perspective, as the ${X55 }$was the first chipset allowing the Stand Alone (SA) operation.

Visual Simultaneous Localization and Mapping (SLAM) systems have their numerous applications in robotics and autonomous driving. Computational offloading enables the computationally demanding Visual SLAM systems to run on hardware-constrained mobile devices, such as Unmanned Aerial Vehicles (UAVs) and Automated Guided Vehicles (AGVs). The offloading of SLAM modules to a central server also enables cooperative collaboration between multiple mobile devices. In this paper, we investigate the process of map synchronization between the edge and mobile devices in edge assisted SLAM systems. Due to the condition of uninterrupted execution, it is necessary that the map synchronization can be executed in the running process without losing the localization on the mobile device. Using a state-of-the-art edge assisted SLAM system, we investigate the influences of the movement speed as well as the network latency on the map synchronization and the resulting success ratio of the tracking continuation. By introducing a frame buffer on the mobile device, we have managed to compensate for the negative effect of the synchronization delay and thus increase reliability by up to 37%.

New generations of mobile networks are opening up novel possibilities for controlling robots remotely in real-time. With 5G’s requirement to support use cases that demand low latencies at a high reliability from the communication network, wireless control applications become feasible. A remote operator typically uses a handheld device with buttons or joysticks to control a mobile robot. Joysticks are widely used today. The limitations of two-dimensional controlling and displaying of camera data can cause difficulties. Augmented Reality (AR)-based control provides the ability to control in three dimensional space. Therefore, new user-friendly Human Machine Interfaces (HMIs) can improve the interaction with these robots. In this demonstration, we present a human-in-the-loop application with a novel HMI. With our HMI a remote operator controls a four-legged Boston Dynamics Spot robot with gestures while wearing the AR-Headset Microsoft HoloLens 2. The remote operator receives feedback from the robot in the form of live camera streams visualised on holographic screens.

Simultaneous Localization and Mapping (SLAM), among other critical functions of mobile robots, such as navigation, are computationally expensive. When deployed at the robot, those functions demand high energy consumption and result in shorter operation time. Offloading SLAM to an Edge Cloud (EC) can significantly reduce the robot’s computing demand and resources, subsequently reducing energy consumption. We offload intelligence of mobile robot control functionality, i.e., navigation, localization, and control to an EC. The EC processes sensor data and sends the robot the directional velocities. Meanwhile, a 5G wireless connection ensures the necessary low latencies and high throughputs. We demonstrate the feasibility of offloading SLAM and navigation in an EC based on a use case in automotive production. Additionally, we developed a digital twin of the robot and visualized its current sensor data.

Edge Simultaneous Localization and Mapping (SLAM) retains only the tracking on the mobile device, while offloading the compute-intensive local mapping and loop close to edge computing. Existing Edge SLAM approaches incur relatively high delays for offloading, resulting in high failure probabilities, i.e., low reliability, for commonly used public SLAM datasets. We discovered that two parameters which had not previously been studied in detail, namely the number of features and the number of keyframes that are bundled for a local map update, play a critical role in the offloading delay. Also, previous approaches updated the local map in the mobile device in a serial manner, incurring map update latencies. We study the numbers of features and bundled keyframes in detail and we parallelize the local map update. We find that judicious parameter settings, namely relatively small numbers of features (750 per frame) and bundled keyframes (1, i.e., effectively no bundling), reduce the map update latency to less than half compared to the previously common settings (1000 features per frame and 6 keyframes used for a map update). For a low network latency of 20ms, these judicious parameter settings in conjunction with our parallelized local map updating, reduce the 79% failure rate of the previous Edge SLAM systems down to 2%.

Mobile robots are an essential part of the vision of flexible production in a smart factory. To monitor and connect the robots, reliable and low latency communication is necessary. In this work, we conduct packet-based active measurements to evaluate the performance of a state-of-the-art 5G standalone system in a production environment. The focus is on whether 5G connections can meet the requirements specified by 3G PP in terms of delay and reliability. The results indicate that without cross-traffic, the requirement of a delay of less than 10 ms for 99.9 % of the packets can be met for the remote control and fleet management of mobile robots. However, as soon as cross-traffic is injected, especially in the uplink, the upper percentiles of the delay increase significantly, thus failing to hold the reliability requirements.

5G campus networks, whose advantages include flexible deployment, can be a promising candidate for production plants to complement existing Wifi-based networks. Toward that goal, 5G has to satisfy strict requirements about real-time communication to facilitate novel use cases. However, the realtime-capability of 5G is not well understood yet. In this work, we deliver insights into the functioning of 5G NR RAN Release 15, which includes actual one-way delay and Round-Trip Time (RTT) measurements for Downlink and Uplink in a private 5G Standalone campus network. The extensive measurement results reveal that these delays are correlated, and the corresponding RTT, i.e. the sum of Downlink and Uplink delays, is discreetly clustered, ranging between 12ms and 40ms. The measurements also show that the distribution of RTTs is mainly dependent on the packet rates and their inter-arrival times. Our study helps expand the current understanding of 5G used for latency-critical applications. We make the code and the measurement data traces publicly available as the IEEE DataPort 5G Campus Networks: Measurement Traces dataset (DOI 10.21227/xe3c-e968).

The Simultaneous Localization and Mapping (SLAM) method is becoming more and more established for the localization of mobile robots in indoor environments. Due to the high complexity of SLAM, high computing resources are necessary, which leads to a shorter runtime. By using edge computing and higher bandwidths of new wireless technologies, the computing can be outsourced. In this work, the SLAM process is offloaded from a mobile robot to an edge cloud and the impact of more computing power is investigated. We show that outsourcing has performance advantages in terms of the update rate of the map generation as well as the localization.

The amount of consumer data transmitted over the internet will increase in the future. At the moment, this traffic is mainly handled by Content Delivery Networks (CDN) via Hypertext Transfer Protocol (HTTP). However, this server-based approach has the disadvantage that the servers themselves and their connections to the Internet are under a high load, which makes them a bottleneck. Therefore, new approaches for efficient content distribution are sought. One outstanding approach is the Interplanetary File System (IPFS), the synthesis of various successful Peer-to-Peer (P2P) approaches. Theoretically, the load on the server can be reduced by distributing the data to several nodes. To transmit data optimally, however, a high degree of coordination between nodes is necessary. Research on other content distribution schemes has shown that the cooperation effort can be avoided by using Random Linear Network Coding (RLNC). This has not been used in IPFS so far. In this work we present Multi Access Recoding System (MARS), a protocol which combines IPFS with RLNC. Simulations on the Interplanetary Testbed (IPTB) have shown that MARS is able to reduce the server load and download time by up to 50% compared to a transmission using only a single server and up to 45% compared to the same setup without RLNC. Even without coordination, this only causes an additional network load of 30%. In addition, MARS improves the download time by at least 15% even for very few peer nodes or peers with little storage.

A wide variety of applications ranging from distributed storage to Forward Error Correction (FEC) benefit from Random Linear Network Coding (RLNC). Recent research activities exploited Network Coding as a Service (NCaaS) as FEC for Software-Defined Networking (SDN). Based on the aforementioned research, in this paper we propose a more efficient approach to use RLNC in a reliability-centric network. By leveraging both, the knowledge possessed by the SDN switches on channel conditions and the dynamic flexibility provided by the virtualized coding instances, we developed a novel method to adapt the code rate of the underlying FEC, to support two different transport protocols with varying reliability and throughput requirements. We have implemented our approach in a realistic SDN emulator to evaluate it in a communication network with time-variant links for TCP and UDP flows comparing against existing fixed-code rate approaches. The results show that our method adapts the flows respectively to the channel conditions and thus delivers a good trade-off between reliability, bandwidth, and overhead. Specifically, our loss estimation algorithm can precisely estimate future losses with a deviation of up to 3%. Our approach is also able to precisely determine the delivery probability with a maximum deviation of 1.5%. In addition, we show that our adaptive redundancy enables TCP to achieve a stable throughput despite losses.

Systems for assisted and autonomous driving increasingly depend on information received and updated through wireless communication. But wireless communication often faces performance degradation because of its dynamic nature. Using multiple available communication channels, such as WiFi, LTE or 5G New Radio, simultaneously can increase the throughput and reliability, but also increases the dynamics of the system. MPTCP estimates the channel capacity and latency and schedule packets accordingly. However, in conditions with unstable channels MPTCP fails to fully utilize the available capacity. In this paper, we propose the use of Network Coding to efficiently utilize the available resources. We use a channel agnostic, random scheduler to maximize the utilization of all available channels. This prevents underestimations, but also produces a high number of packet loss and duplicate transmissions. We use Network Coding to repair the losses and reduce the overhead of redundant data. Our implementation of this protocol is evaluated against MPTCP in an emulated multipath network with time-varying path properties. The evaluation shows, that the proposed protocol utilizes the channels efficiently even in unstable conditions. In the evaluated dynamic network, the proposed protocol efficiently utilizes 94% of the available capacity, while MPTCP is below 80% due to underestimation. While our protocol is not suitable for general purpose traffic, it provides good performance for large file transfers in unstable wireless multipath networks.