ALT IP geolocation supports many Internet services and operations, including but not limited to content delivery, fraud detection, content localization, and regulatory compliance. However, assessing the reliability and accuracy of IP geolocation providers is challenging and only partially explored. In this paper, we present a longitudinal study evaluating the consistency between RIPE Atlas latency measurements and location estimates from four popular geoproviders as a proxy for geolocation quality. Analyzing six months of data we observe mostly stable consistency levels through time, with fluctuations largely related to changes to the mix of targets being measured by RIPE Atlas each day. We also observe that consistency levels can differ greatly between geoproviders, with IPinfo leading DB-IP (the next best) by 5.4 percentage points for IPv4 and 11.5 percentage points for IPv6. Finally, the analysis reveals distinct degradation patterns across providers for different subsets of IP addresse

ALT Internet path diversity is usually verified at the logical layer: two alternatives may share no AS or IP link yet traverse the same facility, conduit, or submarine landing zone. We call this gap illusory resilience and define the Illusory Resilience Index (IRI) as the latent hazard-weighted physical overlap of a logically disjoint path pair. To detect such hidden exposure without carrier underlay maps, we audit three public signals: correlated BGP withdrawals during documented physical incidents, operator interconnection-footprint overlap, and shared submarine landing zones. Two signals survive calibration; the 60-second BGP signal does not. Across 47 operators in ten regions, same-region pairs have substantially greater listed facility overlap than cross-region pairs, and one Marseille campus colocates 153 operator pairs. Across 68 cable systems in twelve corridors, 31-100% of candidate within-corridor pairs share a 25 km coastal hazard zone; two corridors are fully saturated, and the

ALT Space-based artificial intelligence (AI) is moving beyond isolated onboard processing toward constellation-scale computing over optical inter-satellite links (OISLs). Yet current infrastructures remain fragmented, making end-to-end (E2E) coordination across heterogeneous and multi-vendor systems difficult. This article examines the StarMind vision and develops a reference framework for distributed orbital computing. It organizes the system into five layers and defines six standardizable interfaces that link mission intent to resource conditions and trustworthy result delivery. The framework also clarifies the operational constraints that determine whether orbital cooperation is worthwhile. In a representative synthetic aperture radar/interferometric SAR (SAR/InSAR) case study, the framework reduces E2E latency to 41-min, compared with 61-min for localized onboard computing and 146-min for ground-based processing. This gain does not rely on faster processors in orbit. Instead, it comes

ALT Mobile networks are evolving from human-centered, data-oriented communication infrastructures toward machine- and/or agent-dominant, task-oriented platforms that are to serve the rapidly increasing networked machines and embodied agents. In order to fulfill the newly arose complex task objectives, the framework of future mobile network is to be innovated to reflect and facilitate the intrinsic interplay and integration of the underlying processes of perception, communication, computation, decision, action, and feedback, etc. This survey establishes the mobile embodied agent network (MEAN) as a task-oriented networking paradigm in which networking and distributed intelligence are jointly organized around closed-loop embodied task execution. We first establish a unified MEAN framework that explains how network resources and intelligent functions jointly support embodied task execution, and extend its performance indicators from conventional communication metrics to embodied task outcomes

ALT We study power allocation for a multiuser integrated sensing and communication (ISAC) downlink with bursty queues and imperfect channel state information (CSI). Starting from a long-run utility–backlog objective, we derive its common task reward and online score. Integer departures are represented by threshold events of indefinite Gaussian quadratic forms, accounting for estimation error in both signal and interference. Their moments give the conditional one-slot task reward and characterize the error of an efficient plug-in score. An anchored quadratic Lyapunov look-ahead corrects its reward-greedy action with a bounded nominal loss. Across five CSI-error levels, the rule reduces mean queue exceedance by 27.4–43.9% (0.49–0.99 percentage points) relative to the plug-in maximizer, with mean reward loss below 0.01 per slot. The rule achieves higher reward and lower exceedance than DPP, SAC and PPO; its advantage over freshly retrained learning baselines extends to five configurations wit

ALT Classifying disaster-related social-media posts into humanitarian information labels is challenging because individual messages are typically short and context-limited, humanitarian categories can overlap semantically, and the CrisisBench label distribution is strongly imbalanced. This study investigates whether lightweight lexical machine-learning methods can provide effective and computationally efficient classification under these conditions. Using the CrisisBench v1.0 English humanitarian task, comprising 87,455 records across predefined Train, Development and Test splits, we conduct a controlled comparison of TF-IDF representations and lightweight classifiers, with Macro-F1 as the primary model-selection metric. Beyond aggregate predictive performance, the study jointly examines per-label behaviour, directional error patterns, relative decision margins, computational requirements, and the effect of domain-specific training through a tropical-cyclone specialist model. The selected

ALT The deployment of Active Queue Management (AQM) policies is known to be a promising solution to bufferbloat, the problem of persistently-full large buffers in Internet routers. However, most AQMs involve tunable parameters, and some require awareness of the characteristics of the transport-layer protocol implemented at the end systems, thereby rendering their deployment infeasible. We consider one such AQM– a threshold-based queue policy which requires the packet-dropping threshold to be co-designed with relevant TCP parameters. We propose a mechanism that detects network congestion, estimates the congestion window evolution in the end hosts, estimates the required TCP parameter, and produces an optimal value of the packet-dropping threshold. Arguments based on analysis of the TCP fluid model are presented to substantiate the proposed mechanism. A simulation-based performance evaluation reveals the effectiveness of the proposed mechanism, compared to some widely implemented queue polic

ALT Future networks will seamlessly integrate computing and communication resources within a unified ecosystem of IoT devices, edge nodes, and cloud servers, forming an IoT-edge-cloud continuum. Within such an ecosystem, tasks can be dynamically offloaded to leverage resources across the continuum and efficiently scale with demand. Task offloading has traditionally focused on minimizing task execution latency or energy consumption. In contrast, we propose a deterministic task offloading and resource allocation scheme for the joint management of communication and computing resources to support services with deterministic service-level requirements. The proposed scheme aims to maximize the number of tasks completed on time, rather than minimizing the latency of individual tasks. To do so, it exploits variable task latency requirements to distribute the workload across the continuum. It can also prioritize high-priority tasks under resource scarcity. Numerical evaluations demonstrate that a d

ALT Intent-Based Networking (IBN) is a key enabler of autonomous network management in next-generation networks, as it allows operators to drive the network through high-level intents instead of low-level configuration commands. In this paper, we propose a semantic routing framework for telecom intent classification and translation across the radio access networks (RANs), transport, 5G core, and cloud/edge domains. This framework operates locally to preserve intent privacy and avoid the latency and cost of cloud-based Large Language Model (LLM) solutions. The proposed framework is composed of intent classification and translation. To accurately classify the intent, we propose fine-tuning sentence-transformer models using contrastive learning over a 3GPP TS 28.312-aligned taxonomy of five top-level routes and 22 branches, supported by a telecom-specific codebook of 1,100 intents. In addition, we develop a branchaware, template-driven rule-based module for intent translation that enforces de

ALT LLMs are becoming an indispensable part of network control systems due to their impressive reasoning. Before such LLM-enabled systems can be deployed, it is imperative to develop tools that can audit their performance on previously unseen inputs. However, auditing these systems is challenging due to the inherent opacity of LLMs. LENS is the first step towards black-box performance analysis of LLM-enabled network control systems. It uses novel methods to extend deep reinforcement learning to guide the search for performance gaps and inputs that make LLMs struggle. LENS finds large gaps for LLM-based traffic engineering and GPU allocation systems, and even matches existing white-box analyzers designed for hand-crafted TE heuristics.

ALT Reliable indoor positioning remains a central unsolved problem in spatial computing. Existing systems depend on fixed, provider-owned infrastructure (WiFi access points, Bluetooth beacons, UWB anchors) whose deployment, calibration, and ownership models limit their scalability. Meanwhile, personal trackers, wearables, smart luggage tags, and tagged industrial assets are becoming densely embedded in the very indoor environments where positioning infrastructure is weakest. This paper argues that these devices point to a new class of spatial infrastructure: non-stationary, user-owned, and semantically interpretable. We present TrackerNet, a vision for transforming personal trackers from private object-finders into a decentralized substrate for ubiquitous positioning. The key shift is to stop treating anchor mobility as noise: TrackerNet models mobile anchors as probabilistic references whose stability, trust, and semantic role are inferred over time, reframing indoor localization as non-s