ALT The Invisible Internet Project (I2P) is a peer-to-peer anonymity network whose routing and peer-selection mechanics underlie a growing family of state-sponsored covert relay architectures. Studying these mechanics demands a controlled, reproducible environment. We present Garlic-in-a-Box, an installable emulator that deploys a fully isolated network of unmodified I2P Java routers on a single host or cluster of hosts. Each router runs in its own Linux network namespace. A filesystem bootstrap removes the dependency on signed reseed servers, and synthetic geographic diversity activates the same location-aware peer-selection code paths present on the live network. A fifty-router deployment across five geographic locations stabilises in about twenty-five minutes and uses roughly 21 GB of memory. To demonstrate the platform's value, we model I2P peer selection as a two-stage process and conduct a reputation attack. Across five independent trials, the attack promotes the adversary into the v

ALT This paper studies a reconfigurable intelligent surface (RIS)-assisted indoor architecture for extended reality (XR) applications, where multiple access points (APs) simultaneously serve multiple users over a shared frequency channel under a centralized AP controller. We formulate a non-convex sum-rate maximization problem with coupled AP precoders and RIS phase shifts, and develop a centralized alternating optimization (AO) framework for joint design. We rigorously prove that the proposed AO guarantees monotonic improvement of the sum rate that converges to a coordinate-wise stationary point, despite the non-convexity of the resulting subproblems. Although AO provides reliable performance, its iterative nature limits real-time applicability as the system dimension grows. To overcome this limitation, we propose a supervised learning-assisted approach that leverages AO-generated solutions to significantly accelerate convergence. Simulation results demonstrate that the proposed framework

ALT Accurate position and distance information provided by Ultra-Wideband (UWB) can enhance a wide range of applications. However, its receive-mode power consumption makes continuous channel monitoring impractical for energy-constrained devices. Consequently, UWB systems often rely on Bluetooth Low Energy (BLE) to discover peers and establish sessions before ranging begins. Although both technologies have been studied in isolation, an framework capturing the latency and energy consumption of the complete workflow is still lacking. To address this gap, we develop a unified analytical model for BLE-assisted UWB systems, from device-to-device ranging to infrastructure-based localization. The model includes closed-form expressions for discovery, parameter exchange, and ranging, and evaluates the resulting latency and energy consumption. Evaluation of three representative BLE configurations shows that the scanner consumes 28-57x more energy per discovery than the advertiser. In addition, the 99

ALT Effective collaboration among resource-constrained mobile intelligent machines, such as Unmanned Aerial Vehicles (UAVs), is essential for the completion of complex tasks. However, concurrent task demands, limited distributed resources, and dynamic collaborator availabilities make it challenging to satisfy task-specific completion goals in diverse collaborative systems. To address these challenges simultaneously, we propose a new framework of trusted collaborator group formation and the supporting resource allocation strategy for concurrent task orchestration. Specifically, a collaborative task is decomposed into subtasks subject to dynamic resource availability and inter-subtask dependencies. A task utility model is developed for collaborator group evaluation support subtask allocation according to task completion goals and unified value-risk analysis. Based on this model, concurrent task orchestration is formulated as a long-term utility maximization problem under α-fairness. To solve

ALT Today, data center networks run multiple congestion control algorithms (CCAs), each tailored to distinct traffic characteristics, from latency-sensitive intra-region RPCs to high-throughput cross-region AI workloads. The rapid evolution of AI training and inference further accelerates this trend by continually introducing new traffic characteristics that demand new CCAs. This paper introduces CCA-as-a-Service (CCAaaS), our eBPF-based ecosystem for the rapid design, deployment, and operation of production-grade CCAs at hyperscale. We present (1) a modular BPF-TCP framework and general lessons for designing modular CCAs; (2) a deployment and orchestration layer that provides disruption-free CCA updates, along with best practices for managing a zoo of CCAs at scale; and (3) a lightweight observability tool for CCAs and a blueprint for scaling CCA observability. CCAaaS manages over 97% of our server-to-server traffic using three active eBPF-based CCAs; two of the five CCAs we have built ha

ALT Coexistence between non-terrestrial networks (NTNs) and terrestrial networks (TNs) is essential for future wireless systems, but terrestrial downlink transmissions can generate harmful aggregate interference at low-Earth orbit (LEO) satellite receivers. We present DT-STABLE, an Open Radio Access Network (O-RAN)-enabled, digital twin (DT)-assisted framework for context-aware NTN-TN coexistence. Using site-specific information, the DT estimates interference at LEO satellites, enabling proactive management with reduced real-time measurement overhead. We formulate joint beam activation and user association under RF-chain limits, load balancing, and aggregate satellite interference constraints; prove NP-hardness; and develop an efficient DT-guided scheduling algorithm. DT-STABLE couples short-timescale radio resource adaptation with periodic mid- and long-timescale DT updates through standardized O-RAN interfaces. Site-specific 28 GHz simulations under worst-case satellite geometry and low

ALT This paper studies interactions between clean-label poisoning attack and defense in vehicular networks using federated learning (FL). Basic Safety Message (BSM) classification for anomaly detection is modeled as a distributed learning problem over a vehicular network with dynamic topology. As a critical threat to FL, a feature-space clean-label attack is introduced, wherein benign samples are perturbed toward malicious feature extremes, degrading class separability while preserving labels. To counter this, a feature-consistency relabeling defense is proposed to detect and relabel suspicious samples. A game-theoretic framework is developed to capture attack-defense interactions and characterize equilibrium strategies based on attack effectiveness and cost parameters. Results show that the attack scales effectively with compromised nodes, while the proposed defense keeps attack success low without degrading clean accuracy and shifts equilibrium regions toward attack-averse regimes.

ALT Many different programmable network devices and domain-specific languages (DSLs) for programming them have been developed in recent years. However, the affiliated compiler infrastructure for each DSL has not kept up, leading to unreliable compilation results and ultimately a poor experience for developers. In response to this fragmented ecosystem, we propose an intermediate representation, Peregrine, to consolidate the common compiler functionalities within this domain into a shared, open-source infrastructure. We designed Peregrine to provide formal guarantees from the beginning, rather than adding them in a later step. The language comes with a formal semantics, along with an equivalence checker written and proven correct in Rocq. Experimental results show that Peregrine is general enough to express multiple applications written in various state-of-the-art network programming languages. The equivalence checker designed in Peregrine can be used for validating the correctness of a rang

ALT Multiagent deterministic GLP (madGLP) runs Grassroots Logic Programs (GLP) as agents that communicate solely by asynchronous message passing, and assumes fair delivery: every message sent is eventually delivered. Each agent runs on a person's smartphone, named by the person's public key. A grassroots platform depends on no global resource but the network, so agents must reach one another over Bluetooth Low Energy, on a local network, and across the Internet from behind NAT, via rendezvous servers anyone can run. What madGLP requires of such a network, and what the network requires of the platform, has not been specified. We specify the networking layer beneath madGLP. Anything that can be done in GLP is done in GLP: the layer provides only what GLP cannot reach - the network stack, the location service, application attestation, and cryptographic primitives. Three transport underlays share one interface. Over PAN and LAN, an agent advertises a rotating recognition token derived from its

ALT We analyze HTML content and hyperlinks from an offline Tor crawl containing 383 onion hosts and 2,067 directed cross-host links. The study combines semantic analysis with degree-controlled quantum–classical link prediction. Two directory groups emit 94.8% of the observed host links. Directed preferential attachment reaches ROC-AUC 0.976 with random non-edges, but falls to 0.550±0.014 under source-conditioned target-degree matching. With disjoint training and test pairs, multimodal logistic regression reaches 0.672±0.021, GAE 0.899±0.016, and VGAE 0.906±0.016. A four-qubit variational quantum classifier (VQC) comparison tests two encodings across 20 disjoint-pair graph splits under exact and 1,024-shot simulation, with classical controls using the same inputs and training examples. Its best exact mean ROC-AUC is 0.549±0.023, compared with 0.551±0.016 for RBF SVC and 0.573±0.029 for a 16-parameter MLP on the same inputs and training examples. These results support predictive signal in co

ALT Open Radio Access Network (O-RAN) enables mobility-management xApps, but a learned controller is not necessarily the most effective or efficient handover policy. This paper compares standard Event A3, a resource- and mobility-aware heuristic, and a Double Deep Q-Network (DDQN) through a common xApp-oriented interface and paired system-level simulation. The heuristic combines radio quality, target-cell resources, UE mobility, and service class, with weights optimized offline using NSGA-II, while DDQN-Multi uses temporal radio and resource observations. All controllers are evaluated on identical UE trajectories, traffic assignments, and ray-traced radio inputs under Base, High-Mobility, and High-UE scenarios. Performance is assessed using service, handover, resource, energy, and fairness KPIs, complemented by CRITIC–TOPSIS ranking and complexity analysis. Results show that the heuristic achieves the lowest outage in all three scenarios and ranks first in the CRITIC–TOPSIS comparison, whi

ALT Concurrent applications (xApps) in the Near-Real-Time RAN Intelligent Controller (Near-RT RIC) of the Open Radio Access Network (O-RAN) can issue control intents that are jointly infeasible when they act on hierarchically coupled radio resources. This paper experimentally studies an interaction between slice-level physical resource block (PRB) allocation and per-user-equipment (UE) scheduling. We implement a utilisation-aware slicing control (the spectral-efficiency xApp) that reclaims a fixed fraction of the baseline allocation from under-utilised slices and redistributes pooled capacity to saturated slices. The control logic is exercised in simulation and on a 5G testbed comprising an OCUDU-based next-generation NodeB (gNB), an Open5GS core, a Universal Software Radio Peripheral (USRP) X310, and five commercial off-the-shelf (COTS) UEs. Because the gNB counters available in this testbed are UE-keyed but not slice-labelled, the slicing control derives a per-slice throughput proxy from

ALT AI-RAN platforms are expected to share CPU and GPU resources between radio workloads and AI services. Aggregate telemetry reports operating conditions, but timing changes may reflect different workloads, processors, or measurement boundaries. We propose workload-aware monitoring that associates radio-work descriptors with processing paths, timing boundaries, and operating context before comparing like with like. In 16 over-the-air downlink experiments, hybrid dispatch (CPU for transport blocks (TBs) below 4,096 bytes and GPU otherwise) has 29.66% and 22.93% lower mean host-callback durations than direct GPU dispatch, without and with a synthetic GPU co-runner, respectively. Grouping locates the separation in small TBs, while large-TB callback-duration ranges overlap. These observations identify a candidate for complementary CPU/GPU use.

ALT Radio access network (RAN) telemetry reveals radio conditions, protocol behavior, and their consequences for applications. Evaluating its usefulness requires controlled impairments and observations of the same execution. We present C and Python channel brokers and a fault injection workflow for software 5G testbeds connecting an OCUDU base station and a software UE through ZeroMQ. The brokers apply scheduled, directional transformations to I/Q samples and restore the link, while the workflow jointly records applied faults, native and programmable (codelet) telemetry, and application delay. We demonstrate this capability through 45 comparative trials of brief uplink interruptions and stepped additive noise. These use cases distinguish recognizing a radio change from measuring its service consequence: native missing-feedback events retrospectively identify interruptions, whereas noise changes decoding-error measurements without producing those events. Median on-time round-trip times rema

ALT Researchers need blockchain experimental environments with configurable workloads, controllable network conditions, and observable outcomes. Public blockchains and testnets limit experimental control, model-based simulators omit real client implementations, and real-client environments face challenges in combining network control with resource-efficient deployment. We present ChainSEED, a scalable, high-fidelity blockchain emulator built on the SEED Internet Emulator. It separates blockchain configuration from client placement on a configurable routed network. For Ethereum PoS, it organizes separately configured execution, consensus, and validator clients and provides APIs for customizing genesis state, peer connections, and client software. A transaction generator informed by Ethereum mainnet activity, application interfaces, and chain- and node-level monitoring support experiments. ChainSEED also supports Monero and Solana. We examine beacon-node memory reduction, execution-client me

ALT Unmanned Aerial Vehicles (UAVs) in Flying Networks (FNs) increasingly require mechanisms to translate high-level natural language objectives into executable missions. Although Large Language Models (LLMs) have demonstrated the feasibility of language-driven UAV control, existing approaches typically rely on static model configurations and overlook mission-aware performance variability. This paper presents AERIAL, a benchmark-driven framework for adaptive LLM selection in hybrid edge-cloud UAV control. AERIAL separates systematic model benchmarking from mission execution, deriving empirical scenario-model mappings and applying them at runtime for mission-aware selection. Evaluation across 720 automated simulation runs spanning navigation, spatial reasoning, and algorithm invocation revealed significant performance variability depending on scenario-model pairing. A case study integrating AERIAL with a FNs placement algorithm further validated the framework's ability to interface with dom

ALT Ambient Internet of Things (A-IoT) devices operate under stringent energy constraints, relying on harvested power in the μW-to-mW range and limited energy storage. The A-IoT air interface for active devices is still under development; we therefore use Narrowband Internet of Things (NB-IoT) as an implementable reference for evaluating autonomous Uplink (UL) access under harvested energy. Under these conditions, the standard NB-IoT contention-based Random Access (RA) procedure incurs substantial per-packet overhead, making UL access in dense A-IoT networks increasingly energy-expensive. Context-retaining UL access schemes reduce this overhead, but their energy-latency trade-offs are insufficiently studied in literature under realistic harvesting and protocol constraints. To address this gap, we extend the LENA-NB ns-3 module with solar energy harvesting, supercapacitor storage, and a hysteretic brown-out mechanism, and propose Hybrid Scheduling Request (HSR), a hybrid UL access scheme co

ALT Network failures degrade Multipath Reliable Connection (MRC) in direct-connect optical datacenters. We propose Optical MRC (OMRC), which remaps collectives and circuits, preserving 95.7% of training throughput, restoring stalled inference, and achieving 4.7x MRC's simulated bandwidth.

ALT Distributed quantum computing (DQC) is a promising near-term approach for executing circuits that exceed the capacity of a single quantum processing unit (QPU). Prior work has made significant progress on circuit partitioning and reconstruction, but practical DQC requires more than cutting a circuit into smaller pieces. A runtime system must determine whether to execute the circuit intact, cut it for sequential execution on one QPU, or distribute it across multiple QPUs, while accounting for queueing, communication, reconstruction, and hardware constraints. We present a resource-aware scheduler for DQC that compares these execution alternatives at runtime using explicit models of QPU execution, communication, and host-side reconstruction, together with hardware-informed timing derived from simulated IBM QPUs. By treating communication and reconstruction as first-class scheduling costs, the scheduler selects execution plans using a more realistic view of end-to-end overhead than cutting

ALT Antenna diversity in Internet of Things (IoT) devices is explored to address challenges associated with fixed device orientation and interference from nearby metallic objects, which can degrade wireless network performance. Integrating multiple antennas with different polarizations can significantly enhance signal quality and network reliability. Experiments demonstrated significant improvements in link quality, with gains exceeding 30 dB in received signal strength for specific scenarios. Already the addition of a second PCB-based antenna alone, resulted in a 17 dB improvement. These findings suggest that the added complexity in hardware and software to incorporate antenna diversity could well be justified by the resulting enhancements in network stability for real-world applications.