Greedy geometric routing enables scalable, stateless communication in IoT and edge networks by relying solely on local coordinate information. However, this efficiency is underpinned by a structural backbone—the Hyperbolic Minimum Spanning Tree (HMST)—which introduces critical implications for performance and resilience. We analyze the HMST as a locally inferable routing scaffold emerging from hyperbolic embeddings, using Edge Load Centrality (ELC) to assess its importance. We show that HMST edges carry a disproportionate routing load and that removing them causes up to $50 \%$ failure in greedy routing—far exceeding the impact of random removals. While this reveals a vulnerability, the HMST’s local reconstructability also enables autonomous recovery of the network. We discuss the implications for resilient, self-organizing network management and coordinate-aware security.

Hyperbolically embedded networks have emerged as powerful structures for efficient routing and management in diverse fields, including internet infrastructure, edge computing, and IoT environments. Leveraging the rich geometric information inherent in hyperbolic coordinates-such as hierarchy, popularity, and node similarity-these networks achieve scalability and optimal performance. However, the detailed structural information encoded by these coordinates also exposes such networks to novel vulnerabilities. This study introduces a distributed, selforganizing attack model specifically designed to exploit hyperbolic embeddings through strategic link removals. Nodes independently identify inter-sector connections based solely on local geometric coordinates, progressively dismantling the network's global connectivity. Our comparative evaluation demonstrates that the proposed sector-based attack efficiently fragments the network while removing fewer links compared to traditional partitioning methods. Analysis of edge removal distribution reveals insights into node vulnerability patterns and network resilience. This work highlights the necessity of addressing such security threats proactively and discusses potential countermeasures, underscoring the inherent challenges due to the global nature of geometric embeddings.

The growing scale and complexity of Internet of Things (IoT) networks present critical challenges for maintaining and recovering connectivity, especially in dynamic environments or after large-scale fragmentation caused by disasters. Traditional centralized approaches often face scalability, resilience, and energy efficiency issues. Hyperbolic geometry, which has successfully modeled hierarchical and scale-free structures in large-scale systems such as the Internet's AS topology, offers a promising foundation for addressing these challenges. This paper introduces hyperbolic trees as a novel framework for IoT connectivity management. Nodes are embedded in a hyperbolic plane, and connectivity is maintained or restored through a simple local rule: each node connects to the nearest node with a smaller radial coordinate. This distributed, asynchronous approach allows nodes to autonomously reorganize and recover connectivity without centralized coordination, ensuring scalability and adaptability. We demonstrate that hyperbolic trees provide a robust, energy-efficient solution for both maintaining and recovering IoT network connectivity. Numerical results validate the framework's scalability and effectiveness, highlighting its applicability in scenarios ranging from routine management to disaster recovery. These findings establish hyperbolic trees as a practical and scalable tool for resilient IoT connectivity management in next-generation networks.

Future services and applications, such as Tactile Internet, coordinated remote driving or wireless controlled exoskeletons, pose serious challenges on the underlying networks and IT platforms in terms of reliability, latency, or capacity, just to mention a few. Towards those services, virtualization is a key enabler from both technological and economic aspects which significantly reshaped the IT and networking ecosystem. On the one hand, cloud computing and the services based on that are evident results of last years' efforts; on the other hand, networking is in the middle of a momentous revolution and important changes mainly driven by Network Function Virtualization (NFV) and Software Defined Networking (SDN). In order to enable carrier grade network services with strict QoS requirements, we need a novel data plane supporting high performance and flexible, fine granular programmability and control. As the network functions (implemented by virtual machines or containers) use the same hardware resources (cpu, memory) as the components responsible for networking, we need a low-level resource orchestrator which is capable of jointly controlling these resources. In this paper, we propose a novel resource orchestrator (RO) for a data plane making use of open source components such as, Docker, DPDK and OVS. Our goal is threefold. First, we propose a novel data plane resource model which is capable of abstracting several hardware architectures. Second, we provide an adapter module which can automatically discover the underlying hardware and build the model on-the-fly. Third, we design and implement a novel RO building on the aforementioned components and a publicly available Service Graph embedding engine. As a proof of the concept, two software switches (OVS, ERFS) are adapted and different hardware platforms are evaluated

Lately, there has been an upsurge of interest in compressed data structures, aiming to pack ever larger quantities of information into constrained memory without sacrificing the efficiency of standard operations, like random access, search, or update. The main goal of this paper is to demonstrate how data compression can benefit the networking community by showing how to squeeze the IP Forwarding Information Base (FIB), the giant table consulted by IP routers to make forwarding decisions, into information-theoretical entropy bounds, with essentially zero cost on longest prefix match and FIB update. First, we adopt the state of the art in compressed data structures, yielding a static entropy-compressed FIB representation with asymptotically optimal lookup. Then, we redesign the venerable prefix tree, used commonly for IP lookup for at least 20 years in IP routers, to also admit entropy bounds and support lookup in optimal time and update in nearly optimal time. Evaluations on a Linux kernel prototype indicate that our compressors encode an FIB comprising more than 440 K prefixes to just about 100-400 kB of memory, with a threefold increase in lookup throughput and no penalty on FIB updates.

Lately, there has been an upsurge of interest in compressed data structures, aiming to pack ever larger quantities of information into constrained memory without sacrificing the efficiency of standard operations, like random access, search, or update. The main goal of this paper is to demonstrate how data compression can benefit the networking community, by showing how to squeeze the IP Forwarding Information Base (FIB), the giant table consulted by IP routers to make forwarding decisions, into information-theoretical entropy bounds, with essentially zero cost on longest prefix match and FIB update. First, we adopt the state-of-the-art in compressed data structures, yielding a static entropy-compressed FIB representation with asymptotically optimal lookup. Then, we re-design the venerable prefix tree, used commonly for IP lookup for at least 20 years in IP routers, to also admit entropy bounds and support lookup in optimal time and update in nearly optimal time. Evaluations on a Linux kernel prototype indicate that our compressors encode a FIB comprising more than 440K prefixes to just about 100--400 KBytes of memory, with a threefold increase in lookup throughput and no penalty on FIB updates.