Joint Resource Scheduling for AMR Navigation Over Wireless Edge Networks

The future of autonomous systems will rely on the usage of wireless time-sensitive networks to connect mobile cyberphysical systems, such as Autonomous Mobile Robots (AMRs), to Edge compute platforms to offload computationally intensive workloads necessary to complete tasks. In the case of AMRs, due to their mobility, the offloading of expensive processes such as localization and tracking methods to the Edge computing infrastructure must also be done over dynamic wireless networks. In larger scale systems, the network and compute resource requirements can quickly become prohibitively large due to network traffic and heavy workloads and tight deadline requirements for proper execution of time-critical tasks. In this paper, we formulate the problem of jointly allocating network and compute resources for time sensitive systems as the state of the wireless channel changes over time. By characterizing a compute model for AMR workloads, we further demonstrate how the network and compute scheduling decisions can be serialized, thus making the optimal scheduling problem significantly more tractable, via the incorporation of a compute-utility aware network cost function. Simulation results of AMR systems in a Wi-Fi network demonstrate substantial gains over baseline scheduling methods in total resource efficiency

Communication-Control Co-design for Robotic Manipulation in 5G Industrial IoT

<p>Industrial IoT use cases have stringent reliability and latency requirements to enable real-time wireless control systems, which is supported by 5G ultra-reliable low-latency communications (URLLC) over cellular networks. However, extremely high quality-of-service (QoS) requirements in 5G URLLC causes huge radio resource consumption and low spectral efficiency limiting network capacity in terms of the number of supported devices. Industrial control applications typically incorporate redundancy in their design and may not always require extreme QoS to achieve the expected control performance. Therefore, we propose communication-control co-design and dynamic QoS to address the capacity issue for robotic manipulation use-cases in 5G-based industrial IoT. We have developed an advanced co-simulation framework that includes a network simulator, physics simulator, and compute emulator, for realistic performance evaluation of the proposed methods. Through simulations, we show significant improvements in network capacity (i.e., the number of supported URLLC devices), about 2x gain for the robotic manipulation use-case.</p&gt

Evaluating the Integration of Wireless Time-Sensitive Networking with Software-Defined Networking for Dynamic Network Configuration

<p>The introduction of Time-Sensitive Networking (TSN) is revolutionising real-time networks and time-critical applications. Recent advancements in this field extended the TSN capabilities to wireless technologies, giving rise to the concept of wireless TSN (WTSN). This paper focuses on the integration of wireless TSN with Software-Defined Networking (SDN) to enable dynamic network configuration and improve the performance of time-sensitive applications. We present a practical test environment that uses a hybrid network configuration consisting of wireless and wired TSN links. The primary objective is to evaluate the effectiveness of combining a TSN-capable network with an SDN controller. This setup enables dynamic configuration and routing within the system, allowing for prompt actions to address network issues, such as seamlessly re-routing data paths between the two links due to an increase in latency or packet loss. A measurement setup using OpenVSwitch in the wireless TSN domain is presented, along with the evaluation of time synchronization and dynamic route selection capabilities.</p&gt