In recent years, significant progress has been made towards scalable network control-plane verification. Yet, operators are still hesitant to deploy such systems. We argue that this reluctance is in part due to a semantic gap between operators reasoning about routing states and verifiers exploring the space of environments. Indeed, operators express the specification in terms of behavior of routing states, while verifiers usually rely on solvers to find specific environments that violate the specification. This semantic gap prevents users from guiding these solvers to directly explore routing states that violate the specification, or to search for states that are most relevant or likely.In this paper, we present a new approach for flexible control-plane verification. Instead of relying on rigid off-the-shelf solvers, we design a novel backtracking algorithm to directly explore the space of routing states. This enables users to guide the exploration according to the specification and domain-specific knowledge from operators. This algorithm paves the way for novel use cases, ranging from finding relevant (e.g., likely) counterexamples to performing verification of probabilistic specifications.

BGP reconfigurations are a daily occurrence for most network operators, especially in large networks. Yet, performing safe and robust BGP reconfiguration changes is still an open problem. Few BGP reconfiguration techniques exist, and they are either (i) unsafe, because they ignore transient states, which can easily lead to invariant violations; or (ii) impractical, as they duplicate the entire routing and forwarding states, and require special hardware.

Configuration Synthesis promises to increase automation in network hardware configuration but is generally assumed to constitute a computationally hard problem. We conduct a formal analysis of the computational complexity of network-wide Configuration Synthesis to establish this claim formally. To that end, we consider Configuration Synthesis as a decision problem, whether or not the selected routing protocol(s) can implement a given set of forwarding properties. We find the complexity of Configuration Synthesis heavily depends on the combination of the forwarding properties that need to be implemented in the network, as well as the employed routing protocol(s). Our analysis encompasses different forwarding properties that can be encoded as path constraints, and any combination of distributed destination-based hop-by-hop routing protocols. Many of these combinations yield NP-hard Configuration Synthesis problems; in particular, we show that the satisfiability of a set of arbitrary waypoints for any hop-by-hop routing protocol is NP-complete. Other combinations, however, show potential for efficient, scalable Configuration Synthesis.

With Motor-Imagery (MI) Brain-Machine Interfaces (BMIs) we may control machines by merely thinking of performing a motor action. Practical use cases require a wearable solution where the classification of the brain signals is done locally near the sensor using machine learning models embedded on energy-efficient microcontroller units (MCUs), for assured privacy, user comfort, and long-term usage. In this work, we provide practical insights on the accuracy-cost tradeoff for embedded BMI solutions. Our proposed Multispectral Riemannian Classifier reaches 75.1% accuracy on 4-class MI task. We further scale down the model by quantizing it to mixed-precision representations with a minimal accuracy loss of 1%, which is still 3.2% more accurate than the state-of-the- art embedded convolutional neural network. We implement the model on a low-power MCU with parallel processing units taking only 33.39 ms and consuming 1.304 mJ per classification.

Large-scale reconfiguration campaigns tend to be nerve-racking for network operators as they can lead to significant network downtimes, decreased performance, and policy violations. Unfortunately, existing reconfiguration frameworks often fall short in practice as they either only support a small set of reconfiguration scenarios or simply do not scale.

Motor-Imagery Brain--Machine Interfaces (MI-BMIs)promise direct and accessible communication between human brains and machines by analyzing brain activities recorded with Electroencephalography (EEG). Latency, reliability, and privacy constraints make it unsuitable to offload the computation to the cloud. Practical use cases demand a wearable, battery-operated device with low average power consumption for long-term use. Recently, sophisticated algorithms, in particular deep learning models, have emerged for classifying EEG signals. While reaching outstanding accuracy, these models often exceed the limitations of edge devices due to their memory and computational requirements. In this paper, we demonstrate algorithmic and implementation optimizations for EEGNET, a compact Convolutional Neural Network (CNN) suitable for many BMI paradigms. We quantize weights and activations to 8-bit fixed-point with a negligible accuracy loss of 0.4% on 4-class MI, and present an energy-efficient hardware-aware implementation on the Mr.Wolf parallel ultra-low power (PULP) System-on-Chip (SoC) by utilizing its custom RISC-V ISA extensions and 8-core compute cluster. With our proposed optimization steps, we can obtain an overall speedup of 64x and a reduction of up to 85% in memory footprint with respect to a single-core layer-wise baseline implementation. Our implementation takes only 5.82 ms and consumes 0.627 mJ per inference. With 21.0GMAC/s/W, it is 256x more energy-efficient than an EEGNET implementation on an ARM Cortex-M7 (0.082GMAC/s/W).

Providing sufficient mobile coverage during mass public events or critical situations is a highly challenging task for the network operators. To fulfill the extreme capacity and coverage demands within a limited area, several augmenting solutions might be used. Among them, novel technologies like a fleet of compact base stations mounted on Unmanned Aerial Vehicles (UAVs) are gaining momentum because of their time- and cost- efficient deployment. Despite the fact that the concept of aerial wireless access networks has been investigated recently in many research studies, there are still numerous practical aspects that require further understanding and extensive evaluation. Taking this as a motivation, in this paper, we develop the concept of continuous wireless coverage provisioning by the means of UAVs and assess its usability in mass scenarios with thousands of users. With our system-level simulations as well as a measurement campaign, we take into account a set of important parameters including weather conditions, UAV speed, weight, power consumption, and millimeter- wave (mmWave) antenna configuration. As a result, we provide more realistic data about the performance of the access and backhaul links together with the practical lessons learned about the design and real-world applicability of the UAV-enabled wireless access networks.