With the increasing focus on flexible automation, which emphasizes systems capable of adapting to varied tasks and conditions, exploring future deployments of cloud and edge-based network infrastructures in robotic systems becomes crucial. This work, examines how wireless solutions could support the shift from rigid, wired setups toward more adaptive, flexible automation in industrial environments. We provide a quality of control (QoC) based abstraction for robotic workloads, parameterized on loop latency and reliability, and jointly optimize system performance. The setup involves collaborative robots working on distributed tasks, underscoring how wireless communication can enable more dynamic coordination in flexible automation systems. We use our abstraction to optimally maximize the QoC ensuring efficient operation even under varying network conditions. Additionally, our solution allocates the communication resources in time slots, optimizing the balance between communication and control costs. Our simulation results highlight that minimizing the delay in the system may not always ensure the best QoC but can lead to substantial gains in QoC if delays are sometimes relaxed, allowing more packets to be delivered reliably.

The increasing demand for latency-sensitive applications has necessitated the development of sophisticated algorithms that efficiently manage packets with end-to-end delay targets traversing the networked infrastructure. Network components must consider minimizing the packets' end-to-end delay violation probabilities (DVP) as a guiding principle throughout the transmission path to ensure timely deliveries. Active queue management (AQM) schemes are commonly used to mitigate congestion by dropping packets and controlling queuing delay. Today's established AQM schemes are threshold-driven, identifying congestion and trigger packet dropping using a predefined criteria which is unaware of packets' DVPs. In this work, we propose a novel framework, Delta, that combines end-to-end delay characterization with AQM for minimizing DVP. In a queuing theoretic environment, we show that such a policy is feasible by utilizing a data-driven approach to predict the queued packets' DVPs. That enables Delta AQM to effectively handle links with arbitrary stationary service time processes. The implementation is described in detail, and its performance is evaluated and compared with state of the art AQM algorithms. Our results show the Delta outperforms current AQM schemes substantially, in particular in scenarios where high reliability, i.e. high quantiles of the tail latency distribution, are of interest.

This paper presents ExPECA, an edge computing and wireless communication research testbed designed to tackle two pressing challenges: comprehensive end-to-end experimentation and high levels of experimental reproducibility. Leveraging OpenStack-based Chameleon Infrastructure (CHI) framework for its proven flexibility and ease of operation, ExPECA is located in a unique, isolated underground facility, providing a highly controlled setting for wireless experiments. The testbed is engineered to facilitate integrated studies of both communication and computation, offering a diverse array of Software-Defined Radios (SDR) and Commercial Off-The-Shelf (COTS) wireless and wired links, as well as containerized computational environments. We exemplify the experimental possibilities of the testbed using OpenRTiST, a latency-sensitive, bandwidthintensive application, and analyze its performance. Lastly, we highlight an array of research domains and experimental setups that stand to gain from ExPECA's features, including closed-loop applications and time-sensitive networking.

End-to-end learning for wireless communications has recently attracted much interest in the community, owing to the emergence of deep learning-based architectures for the physical layer. Neural network-based autoencoders have been proposed as potential replacements of traditional model-based transmitter and receiver structures. Such a replacement primarily provides an unprecedented level of flexibility, allowing to tune such emerging physical layer network stacks in many different directions. The semantic relevance of the transmitted messages is one of those directions. In this paper, we leverage a specific semantic relationship between the occurrence of a message (the source), and the channel statistics. Such a scenario could be illustrated for instance, in vehicular communications where the distance is to be conveyed between a leader and a follower. We study two autoencoder approaches where these special circumstances are exploited. We then evaluate our autoencoders, showing through the simulations that the semantic optimization can achieve significant improvements in the BLERs (up till 93.6%) and RMSEs (up till 87.3%) for vehicular communications leading to considerably reduced risks and needs for message retransmissions.

As the number of cyber-physical systems rises, it becomes increasingly crucial to study Networked Control Systems (NCSs) combining control communication co-design. This nature of NCSs has led to task-specific approaches to research, creating a dearth of generalizable, repeatable, and scalable experimentation. Further, with the advent of edge computing solutions, it is of paramount importance to explore its relevance in such applications. In this work, we present CLEAVE, a novel, completely software-based framework for repeatable and scalable experimentation in edge native NCSs. Our approach is based on the emulation of physical plants communicating over a real network with software-based controllers. CLEAVE is designed and built for the edge, using Python3 and with full compatibility with industry-standard containerization solutions. Although designed for single-loop emulations, the flexibility afforded by the aforementioned characteristics allow our framework to be adapted to a multitude of complex scenarios.

We validate CLEAVE using an initial implementation of an inverted pendulum NCS. Our results showcase the utility of the tool as a repeatable, extensible, and scalable solution to NCS performance evaluation and benchmarking on the Edge.

This paper presents a wireless mesh network-based solution utilizing stationary and mobile ground/UAV nodes for setting up a post-disaster communication infrastructure. The network is ad hoc and establishes connectivity over a large area without the use of a cellular network or the internet. We propose a complete end-to-end framework, where we deploy an android application on smartphones at the user-end, the wireless mesh network as the backbone of communication, and a graphical user interface (GUI) at a base station for facilitating situational awareness. The software stack is based on the Robot Operating System for broadcasting user information to the base station, and also for controlling the UAVs remotely. The GUI also incorporates a node recommendation strategy for selecting the optimal nodes. The framework is evaluated using UAVs and a semi-autonomous car, and the experimental results show that the proposed system could be indispensable in providing large-scale connectivity in post-disaster management.

Person Re-Identification (PRID) has been one of the most challenging tasks in intelligent video surveillance. Most existing PRID based surveillance methods rely on a single camera mounted on emplacements. This approach assumes that any scrutinized person appears again in the field of view of that cam-era, which is unreliable in real-world settings. In this paper, we propose a system wherein we explore on-board re-identification of persons using cameras mounted on Unmanned Aerial Vehicles (UAVs). The captured images are dynamically shared between multiple UAVs by virtue of wireless mesh networking, also making use of the Robot Operating System (ROS) for on-ground control of the UAVs from a control station, facilitating the exchange of images between them. We demonstrate our approach on an institutional dataset we created, and our experimental results show that the system could be indispensable in aiding airborne surveillance operations.

Vehicle-to-Everything (V2X) using Visible Light Communication (VLC) channels can be seen as an economically viable option to replace the existing modes of vehicular communications in the near future. In this paper, we have analyzed the performance of VLC based V2X communication under various environmental deterrents viz. Light Fog, Dense Fog, Light Smoke and Dense Smoke using a proof-of-concept testbed. A series of experiments were conducted to investigate the effects of environmental deterrents over VLC based Line-of-Sight as well as non-Line-of Sight V2X transmission with respect to distance and angular variations. On-Off-keying (OOK) modulation has been selected as the modulation scheme, as defined in VLC standard (IEEE 802.15.7) for the transmission of information bits between a transmitter LED and a photo-diode receiver. The experimental results show the feasibility of VLC-based V2X systems with reliable data transmission under different environmental deterrents with a fairly good signal-to-noise ratio (SNR), even under dense-fog and smoke conditions where the attenuation in average optical power at the receiver, is quite high.