Papers

Cloud services rapidly shift towards independently-managed microservices architectures. This independence may lead to temporary microservices miscoordination, resulting in overloads and rejections of valuable transactions. This weakness may further be exploited by attackers to launch overload attacks inflicting performance and cost damages. Retry operations are a key machinery aiming at overcoming seldom rejections. However, during overload periods they: a) Fail protecting valuable transactions from rejections, and b) Trigger large-scale performance-degrading retry storms. We propose simple modifications in retry operations, overcoming these deficiencies by transforming retries into a rejection prioritization mechanism, DiffTry (Differential Retries). It is based on a simple, lightweight, distributed and stateless design and requires neither overload sensing nor microservices coordination. We develop an analytical model capturing DiffTry's effectiveness for two services in tandem, then generalize it to networks of services. Our theoretical framework lets practitioners quantify, for a given network, the resulting prioritization and rejection rates, and yields formal guarantees that DiffTry substantially reduces the impact of both incidental and adversarial orchestrated overloads. We validate these predictions in simulation and on a live Kubernetes deployment, where DiffTry is a simple drop-in service-mesh plugin requiring no application changes; the measured rejection closely matches the analysis.

Cellular broadband Internet of Things (IoT) applications are expected to keep growing year-by-year, generating demands from high throughput services. Since some of these applications are deployed over licensed mobile networks, as long term evolution (LTE), one already common problem is faced: the scarcity of licensed spectrum to cope with the increasing demand for data rate. The LTE-Unlicensed (LTE-U) forum, aiming to tackle this problem, proposed LTE-U to operate in the 5 GHz unlicensed spectrum. However, Wi-Fi is already the consolidated technology operating in this portion of the spectrum, besides the fact that new technologies for unlicensed band need mechanisms to promote fair coexistence with the legacy ones. In this work, we extend the literature by analyzing a multi-cell LTE-U/Wi-Fi coexistence scenario, with a high interference profile and data rates targeting a cellular broadband IoT deployment. Then, we propose a centralized, coordinated reinforcement learning framework to improve LTE-U/Wi-Fi aggregate data rates. The added value of the proposed solution is assessed by a ns-3 simulator, showing improvements not only in the overall system data rate but also in average user data rate, even with the high interference of a multi-cell environment.

Recent studies and literature reviews have shown promising results for 3GPP system solutions in unlicensed bands when coexisting with Wi-Fi, either by using the duty cycle (DC) approach or licensed-assisted access (LAA). However, it is widely known that general performance in these coexistence scenarios is dependent on traffic and how the duty cycle is adjusted. Most DC solutions configure their parameters statically, which can result in performance losses when the scenario experiences changes on the offered data. In our previous works, we demonstrated that reinforcement learning (RL) techniques can be used to adjust DC parameters. We showed that a Q-learning (QL) solution that adapts the LTE DC ratio to the transmitted data rate can maximize the Wi-Fi/LTE-Unlicensed (LTE-U) aggregated throughput. In this paper, we extend our previous solution by implementing a simpler and more efficient algorithm based on multiarmed bandit (MAB) theory. We evaluate its performance and compare it with the previous one in different traffic scenarios. The results demonstrate that our new solution offers improved balance in throughput, providing similar results for LTE and Wi-Fi, while still showing a substantial system gain. Moreover, in one of the scenarios, our solution outperforms the previous approach by 6% in system throughput. In terms of user throughput, it achieves more than 100% gain for the users at the 10th percentile of performance, while the old solution only achieves a 10% gain.

Smart healthcare IoT requires low-latency and reliable delivery of patient data, yet IP-based communication remains host-centric and offers limited support for mobility, caching, and request aggregation. This paper evaluates a smartphone-centric Named Data Networking (NDN) approach in which a patient's smartphone aggregates sensor streams and publishes a stable latest profile under a consistent name, enabling effective in-network caching. Using ndnSIM simulations on a realistic medical IoT topology, we compare NDN and IP under identical traffic and link conditions. Results show that per-sample naming provides little benefit, but smartphone aggregation with stable naming allows NDN caching to reduce delay after cache warm-up and achieve significantly higher delivery ratios under congestion compared to IP.

Internet Exchange Points (IXPs) operate from colocation facilities that draw power from regional grids and store limited backup fuel. In April 2025, the Iberian blackout kept the Spanish grid down for 16 hours, beyond the 12 hours of fuel required by the industry standard, while traffic at major Iberian IXPs fell by up to 70%. Yet operators cannot tell which facilities a grid event cuts off, whether it outlasts their backup, or where traffic from a failed IXP goes. We present SPARC, which maps power-grid and Internet dependencies from substations to IXPs and their traffic. Using open data validated against transmission operators, SPARC links 552 facilities hosting 275 IXPs to the substations that likely feed them. We spread grid events over this map and measure failover through controlled BGP withdrawals on the PEERING testbed. Disabling the most-shared 16% of facility-feeding substations cuts power to half of all IXP presences (an IXP at one facility), under half of what randomized maps require. Losing the 5% most central substations cuts power to 0.1% of IXP presences directly but to 9-52% once the grid splits into disconnected islands. Traffic often fails over to nearby sites that share the failing grid: a cascade affecting four metropolitan areas sends up to 40% of one area's monitored addresses to another of the four. We introduce SPARC-Shield to select substations to harden or facilities whose backup to extend. Against an attacker disabling ten substations, hardening the ten selected by SPARC-Shield reduces offline IXP presence by 72%, compared with 52% for the best standard ranking.

Motion capture is increasingly important in healthcare, rehabilitation, and sports applications. However, conventional vision-based systems require camera infrastructure and line-of-sight conditions, limiting their use in daily environments. This paper proposes a location-independent platform for motion capture, based on an array of IMU (inertial measurement unit) sensor nodes without individual wiring or onboard batteries, enabled by two-dimensional communication and power delivery over conductive fabric. Pipeline processing in each sensor node, combined with a token-passing protocol, enables collision-free and stable data acquisition at 50 Hz. Real-time three-dimensional reconstruction of flexible objects is further demonstrated by applying Hermite spline interpolation to orientation data from grid-aligned IMUs. Against a cylinder of known radius, the reconstructed surface deviates by 2.60 mm RMS over the 225 mm span of the array. The reconstruction is formulated for the quasi-static regime, and this letter empirically validates it in that regime. The hardware platform and communication protocol implemented in this work enable further investigation toward dynamic motion capture textiles.

This paper presents a preliminary study on the efficacy of network device configuration produced by Large Language Models (LLMs). Currently, the study focuses on routing protocol configurations in a predefined small internetwork with Cisco-based routing devices. The capability of three freely available LLMs with standard capabilities (i.e., with no enhanced features) to produce effective setups is investigated. Prompt engineering methods are excluded in order to assess the "first-response" configurations of the models under consideration. The assessment method is based on the contents of the resulting routing table, aiming at measuring the device functional correctness. For that purpose, the Jaccard index is used to indicate the percentage of correct network prefixes contained in the routing table constructed by the model-based configuration commands. The work provides insights on the maturity and limitations of modern LLMs on aiding network administrators with network device configurations in real deployments.

Accurate path loss (PL) estimation is essential for wireless network planning and optimization. This paper proposes HankelKANet, a lightweight physics-inspired network for PL estimation that structurally embeds electromagnetic propagation characteristics into the neural architecture. Built on a single residual UNet backbone, the proposed model integrates urban layout features with a convolutional Kolmogorov-Arnold network(KAN). Specifically, the convolutional kernels are parameterized by the radial amplitudes of the zeroth- and first-order Hankel functions. This formulation acts as a physics-inspired nonlinear distance encoding, enabling the network to capture dominant attenuation characteristics directly without requiring additional physics-based loss terms. Experimental results demonstrate that HankelKANet achieves state-of-the-art accuracy and efficiency on both the simulated RadioMapSeer dataset and the real-world Huawei RSRPSet. Compared with the leading baseline, PEFNet, it reduces the parameter count by 78% and lowers the RMSE by 16.96% on the simulated data, while further exhibiting robust cross-dataset transferability and high data efficiency during target-domain adaptation. To interpret these empirical gains, we mathematically establish a parameter-conditional spatial gradient bound. Using discrete Dirichlet energy (DDE) and Jacobian sensitivity heatmaps, we empirically verify that this physical encoding intrinsically constrains the network's spatial derivatives. This structural smoothness effectively eliminates non-physical spatial ripples, preventing overfitting to simulator artifacts and serving as the fundamental driver for robust real world generalization.

Emerging data-intensive space applications generate large volumes of data that must be processed, stored, replicated, and delivered efficiently. However, relying on terrestrial cloud regions can expose satellite data to high delivery latency and loss of timeliness due to intermittent satellite-to-ground visibility and limited access to ground infrastructure. Space Data Centers (SDCs), which organize collaborative satellites into logical compute and storage clusters, offer a promising approach for in-orbit data management and service continuity. In this paper, we first evaluate how satellite clustering with different sizes affects satellite visibility and gateway outage. We then propose BIER-DC, a stateless one-to-many communication protocol tailored for satellite-based SDCs. Unlike terrestrial multicast protocols designed for stable data center fabrics, BIER-DC decouples inter-SDC forwarding from intra-SDC delivery and encodes compact cluster-level forwarding information in packet headers. Comparative evaluations against BIER, BIER-TE, and Yeti show that BIER-DC significantly reduces packet-header length and byte-hop overhead, improving effective goodput and scalability for next-generation LEO space data centers.

Next-location prediction can support proactive mobile-network decisions, but acting on unreliable predictions can waste resources. We apply MetaModelSelect, a lightweight post-hoc confidence metamodel, to selective cellular next-location prediction. The mobility-specific model combines a learned embedding of the predicted location with the four largest softmax probabilities and Softmax Concentration. It learns whether a frozen recurrent neural network's top-1 prediction is correct, using only its outputs and without retraining the backbone. On path-balanced Changchun cellular mobility trajectories, we evaluate backbones trained with cross-entropy (CE) and self-adaptive training plus entropy minimization (SAT+EM), comparing against eight scalar confidence scores, two location-aware lookup methods, and a raw-logit multilayer perceptron. Relative to maximum softmax probability, MetaModelSelect achieves mean per-run reductions in area under the risk–coverage curve (AURC), excess AURC (E-AURC), and area under the generalized risk–coverage curve (AUGRC) of 2.6%, 4.2%, and 3.0% for CE, and 2.0%, 3.3%, and 1.9% for SAT+EM. It also achieves the lowest reported mean risk at every evaluated partial coverage. The results show that MetaModelSelect improves confidence ranking in this mobility setting while leaving the underlying location predictor unchanged.

Wireless sensing systems often transmit highly redundant observations, making communication a bottleneck for low-power devices. We present Memento, a communication system that exploits recurring content by transmitting observations by reference. The sender embeds each observation and searches a shared vector table stored in persistent memory. When it finds a sufficiently similar prior observation, it sends a compact reference; otherwise, it sends the observation itself and adds it to the table. This design shifts work from wireless communication to local computation and storage, without requiring a fixed task-specific model. We implement Memento on a microcontroller with an SD card and evaluate it on three image datasets and one radar dataset. Memento provides favorable accuracy-communication and accuracy-energy tradeoffs against compression-based and task-oriented baselines. At high-accuracy operating points, it matches cloud classification accuracy on three workloads while using 5.5 to 27.1 times less energy, and comes within 0.14 percentage points on a fourth while using 14.7 times less energy. These results show that retrieval over persistent local memory can reduce communication costs in recurrent sensing workloads.

Mobile latency is determined by how Mobile Network Operators (MNOs) route traffic: from phones, across the MNO's network to exit points where it enters the public Internet. Exit points are few, and MNOs are quiet about details, but their role prompts us to ask: What is the latency cost of sending traffic through an exit point? We develop a new method to evaluate this question, and demonstrate it on the three largest U.S. MNOs, although in principle it can apply globally. Using new analysis of existing traceroutes, we find that AT&T, T-Mobile, and Verizon funnel nationwide traffic through only 9, 5, and 9 exits, and the geographic detour they incur adds up to 62.1 ms compared to an ideal, direct-path latency. Routing delays also add latency. We show that even assuming speed-of-light paths through the exit points, latency is still up to 46.9 ms more than optimal. To understand if these costs can be overcome, we compare actual to theoretically optimal placements, finding that the latencies of current placements are 18-66% above the population-weighted optimum and 32-72% above the market-share-weighted optimum with the same number of exits. However, we show this gap is small in absolute terms: perfect placement would recover only a few milliseconds of the tens that remain. We show that geographic optimization saves little latency: the inflation users experience comes from routing and other overhead.

Quantum federated learning trains a quantum neural network (QNN) across nodes that hold quantum data, such as the states of quantum sensors. To forget data, unlearning methods for quantum models replay them, and federated unlearning from stored updates removes whole nodes only. Quantum data cannot be replayed, since a state is consumed when measured and cannot be cloned. We propose Shadow-based Data Erasure (SHADE), in which each node records a classical shadow of every training state, a few randomized single-qubit measurements taken at training time. The gradient and curvature of a QNN on a state are expectations of observables, so SHADE estimates them for the forget data from the shadows at any later parameters and removes their effect by a federated Newton correction. Only classical estimates cross the network. We prove that the shadow size needed for a target unlearning error grows exponentially in the size of the backward light cone of the measured observable and not in the number of qubits. In simulation with 50 nodes and on edge boards, SHADE matches the forget accuracy of retraining within 0.5 points, without replay and in under 0.2 s.

An edge server and nearby devices can train language models of different sizes together by mutual distillation. Federated unlearning (FU) and unlearning for large language models (LLMs) execute a removal request on one model. Under mutual distillation, however, the effect of the removed data has been copied into many students, and their uploads teach it back to the corrected teacher. We propose Holder-level Erasure via Replication-weighted Delta-closure (HERD), which decides which holders must be corrected. HERD records a knowledge lineage graph from the transfers that the server already performs and weights each edge by its transfer type. It computes how many times the residual at each holder will be copied and selects the cheapest set of holders whose correction bounds the cumulative residual after the request. A transfer guard and a rejoin rule cover the holders left uncorrected or offline. We prove that partial correction suffices only when the copy number of the lineage graph is below one, that averaging alone never reaches one, and that the correction set remains valid under link outages. On TOFU, HERD leaves 18% to 52% less cumulative residual than the strongest baseline at 6% to 12% of its correction cost.

Ghostline is a device-free motion tracking system built on six ESP32-S3 Wi-Fi boards and one consumer router. The boards' Wi-Fi driver reports channel state information (CSI) for every received frame with the sender's address, and a modified open-source firmware forwards both, so every board carries a CSI stream for the router and for each other board: seven radios give up to 36 directed Links over 21 Spans instead of a star of six. Each Link is compared with a self-forming quiet Baseline, disturbed Links vote on a floor grid, and the grid becomes anonymous Tracks. The system was built and run during a three-day hackathon in an ordinary room. We describe it in full and report a pilot evaluation with one walker: spoken cues at seven marked positions, a two-minute free walk and five minutes of empty room, synchronised to video within 0.18 s (median). Walking between marks, the Track lay a median 0.85 m from the straight path and 1.56 m (95% CI 1.29 to 2.83) from a cue-based reference trajectory (constant speed between cues, not measured positions), with 46% of walk updates within 2 m of it; the free walk was tracked in 94% of updates. The limits: a standing person is lost within seconds, a false Track appeared in 15.9% and 2.9% of updates in two empty-room periods, and informal runs that afternoon did not separate two walkers. Two offline replays through the same pipeline show where this comes from: with the node-to-node Links removed and settings unchanged, the tracked share of walk updates falls from 90% to 19% and the free walk from 96% to 60%; a plain radio tomographic image on the same per-Span evidence, at the same false rate, matches the grid and tracker at 2 m (48% against 47%) and is ahead at 1 m (36% against 21%). On this recording the Tracks depend on the Link set, and the imaging stage adds no measurable accuracy. We state what the pilot establishes and what it does not.

Wireless communication remains the most power-consuming operation in embedded systems. Low-power transmitters such as backscatter achieve microwatt-scale operation but produce weak signals susceptible to in-band interference and frequency-selective fading, particularly in non-line-of-sight settings. Multi-band transmission can mitigate these effects through spectral-domain redundancy rather than temporal-domain redundancy, improving reliability without sacrificing bitrate or latency. However, conventional multi-band radio transceivers require duplicated RF chains or power-hungry synthesizers. We introduce HILO, a tunnel diode-based frontend that enables dual-band operation from a single circuit. HILO inverts the conventional paradigm: rather than suppressing the harmonics inherent to tunnel diode nonlinearity, it harnesses them. An external device injection-locks the oscillator to a fundamental frequency, simultaneously stabilizing a higher harmonic and yielding two injection-locked carriers without a second oscillator or frequency multiplier. In transmit mode, HILO exploits self-oscillating mixing to modulate both carriers simultaneously; in receive mode, it uses autodyne downconversion at both frequencies. By turning an inherent nonlinearity into a feature, HILO achieves dual-band links while consuming under 210 $\mu$W.

In Intelligent Transport Systems, protecting vulnerable road users, specifically pedestrians, is a paramount challenge. The present work contributes to this field, putting forth a distributed architecture in which vehicles connected to the cellular infrastructure collect pedestrians' trajectories and fine-tune a foundation model for pedestrian trajectory forecasting via federated training. A server positioned at the edge of the cellular network is responsible for aggregating the local model's parameters from the vehicles and updating the global model's parameters. To reduce radio resource utilization, Low-Rank Adaptation is proposed; to decrease both the number and the size of updates, an increase in the number of training rounds performed by the vehicles per communication round is put forth. A novel figure of merit is then introduced to quantify the use of radio resources dedicated to Vehicle-to-Infrastructure communications as required by the federated solution. The proposed scheme delivers excellent prediction accuracy while significantly reducing the communication cost compared to alternative federated training methods. This shows that combining fine-tuning, Low-Rank Adaptation, and a reduction of the communication periodicity between the vehicles and the edge server is key to enabling a distributed approach to trajectory prediction in vehicular environments.

Disaggregated LLM serving moves each request's KV cache from a prefill GPU to a decode GPU before the response begins, and a coding-agent turn often moves the KV of its whole long context. Behind an oversubscribed packet fabric this transfer adds to time to first token (TTFT). An optical circuit switch (OCS) can give every transfer a full-rate circuit for less than an oversubscribed packet fabric costs, but a mechanical OCS needs tens of milliseconds to reconfigure. We hide this delay inside the prefill. A reservation scheduler commits each circuit while the prefill is still computing, so the circuit is ready when the KV is, and each GPU reaches the OCS over its own wavelength-multiplexed (WDM) optics on one fiber pair. In simulations of GPU clusters on real serving traces with measured prefill times on H100 GPUs, this optical KV cache network matches 400G packet fabrics with adaptive routing at 0.55 to 0.78 times the cost of a 2:1 fabric and tolerates the 50 ms reconfiguration delay of a 384-port mechanical OCS.

HTTP(S) DDoS attacks have surged in frequency and scale, posing a significant threat to network security. Flow-based detection is a promising solution for large-scale HTTP(S) DDoS detection due to its decryption-free operation and scalability. However, the coarse-grained numerical statistics in flow data limit accurate attack detection and lack robustness when benign and attack traffic patterns drift over time. In this paper, we present Tanchun, a drift-aware flow-based HTTP(S) DDoS detection system that relies solely on decryption-free flow data. Specifically, Tanchun groups flows by IP-pair and aggregates them over consecutive time slots into behavior sequences, capturing temporal access patterns to compensate for the limited information in individual flows. It further abstracts these sequences into human-understandable behavioral attributes that capture stable behavioral semantics. Tanchun then aligns behavior sequences with their corresponding behavioral attributes to guide representation learning, enabling robust detection under traffic drift. We evaluate Tanchun across diverse HTTP(S) DDoS scenarios. Tanchun achieves the best overall performance, with an average F1-score of 0.988, and outperforms the second-best method by 5.3% and 9.6% under benign-traffic and attack-traffic drift, respectively. Experiments on real-world traffic traces further demonstrate its practical effectiveness.

The SKA Regional Centre Network (SRCNet) is a federated global network designed to archive, distribute, and process SKAO data products. This paper examines the evolving SRCNet architecture-focusing on the transition to version 0.2 (v0.2) requirements, including computation, orchestration, and multi-tiered storage-and details the strategy for establishing its proto-SRC infrastructure. We outline the prototype developments for the major and minor nodes in the context of v0.2. We describe the minor node compute and storage demands, establishing a pathway toward a fully operational Indian SKA Regional Centre (INSRC).

Encrypted traffic classification is critical for network management and security, but practical deployment must balance classification accuracy and inference cost. Existing models typically process fixed flow windows with limited control over computation after training, which can limit the accuracy-efficiency trade-off under constrained resources. To address this problem, we present NetCoFi, a budget-aware classifier that allocates computation at coarse and fine granularities. Using byte representations and observable packet metadata, NetCoFi selects token neighborhoods for fine-grained encoding while compressing the full window into complementary coarse context. Multi-budget training enables the same model to operate at different routing budgets without retraining. Evaluation on six public datasets shows that NetCoFi achieves state-of-the-art classification performance when trained from scratch. Under low-budget settings, NetCoFi improves average Macro-F1 by 7.86 percentage points across three datasets, compared with a same-depth static baseline. In a DPDK-GPU traffic processing pipeline, it increases mean batch throughput from 26.42 to 43.42 Mb/s, a 1.64x improvement over NetMamba.

Predictive service assurance converts forecasts of key performance indicators into alarms, and that conversion decides whether the assurance function is deployable. We present CALM-5G, an assurance layer that operates above any forecasting backbone and issues alarms with distribution-free, finite-sample guarantees. It calibrates on service-level violation events, scores violation onset directly, selects its calibration scheme out of sample, and reports the alarm duty cycle with every certified miss rate. Four corpora are evaluated separately, namely a production cellular network, commercial 5G traces, an open radio access network testbed and our 5G standalone testbed. Risk control on the regression-derived score yields duty cycles of 0.24 to 0.91. Onset targeting with cross-validated calibration reduces time-in-alarm by 56.3% to 89.7% on three corpora, with no significant change on the fourth. The realised miss rate stays within a two-standard-error tolerance on three corpora and within one standard error of it on the fourth. Under label shift, pooled batch calibration exceeds that tolerance structurally, by 10.9 standard errors on the production corpus. Online calibration restores compliance. Calibration schemes cannot be ranked on the split that fits their thresholds. Miss rate and duty cycle are certified jointly where within-entity dependence leaves sufficient calibration units.

Encrypted traffic classification is commonly evaluated under a range of experimental protocols, with classification performance reported as the primary measure. However, the reported performance may be influenced by shortcuts left uncontrolled in the evaluation protocol, such as shortcut features and cross-partition sharing, rather than reflect generalizable traffic patterns. And test-only feature transformations may further introduce train-test mismatch. In this paper, we study how current evaluation protocols are affected by shortcut-related factors, including field-level shortcuts, cross-partition sharing, and train-test processing. We compare feature importance, retrained feature removal, and field-only split sensitivity. We also vary Source-IP overlap and packets per flow, compare test-only IP transformations with matched train-test transformations. Experiments on three public datasets show that the performance of existing methods are affected by both cross-partition sharing and the consistency of train-test transformations. Specifically, increasing Source-IP overlap from 0% to 100% raises accuracy by about 32-33 points before identifier masking, compared with only about 8 points after masking. For two state-of-the-art pretrained models (i.e., YaTC and NetMamba), the 11-13 points loss caused by test-only IP zeroing is almost fully recovered when the same transformation is applied during both training and testing.

Virtualization enables mixed-criticality applications to run on shared hardware by isolating them within separate virtual machines (VMs). However, achieving end-to-end timing guarantees for critical tasks hosted on virtual CPUs (VCPUs) without relying on static partitioning is hard. A possible solution is a time-triggered hypervisor that dispatches VMs' VCPUs to processor cores according to a predetermined global schedule. A global schedule includes the computation, the network, and the virtualization layer to provide end-to-end timing guarantees across hosts and VMs during design time. In this work, we propose a VirtIO-based virtual network that realizes IEEE 802.1Qbv quasi-compliant inter-VM communication, providing predictable timing for critical messages exchanged between time-triggered VMs. We present our VirtIO-based virtual network architecture and derive network-level scheduling constraints that emerge from the properties of the proposed solution. We implement the proposed design in the ACRN hypervisor and evaluate the end-to-end latency of critical traffic in a loaded network, demonstrating that our time-triggered VirtIO-net prototype provides predictable end-to-end latency under load.

In remote regions lacking terrestrial infrastructure, aviation users increasingly demand Internet services comparable to ground cellular networks. This paper proposes a space-air-ground integrated network (SAGIN) architecture that combines ground networks, civil aviation networks, and low Earth orbit (LEO) satellites. Based on real North Atlantic flight data, a heterogeneous aviation Internet is constructed to support diverse aircraft service requirements. A composite Quality of Experience (QoE) metric, characterized by transmission delay, jitter, and content similarity, guides efficient resource utilization. The dynamic connectivity of SAGIN is modeled using a reconfigurable time-expanded graph (RTEG). With Network Function Virtualization (NFV), data delivery is formulated as a Service Function Chain (SFC) deployment across ground stations, relay aircraft, and satellites, resulting in a long-term QoE maximization problem. A tailored Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm incorporating LCSS-based path filtering and softmax-based action selection is developed. Simulation results demonstrate that the proposed approach enables seamless connectivity for heterogeneous aircraft and consistently outperforms game-theoretic and MATD3 methods in terms of QoE.

Transformer-based network intrusion detection systems (NIDS) have shown strong potential for detecting multi-stage cyberattacks, yet most methods construct input sequences using fixed temporalization schemes rather than treating sequence construction as a systems design decision. In flow-based intrusion detection, timestamp-based windowing often fragments related attack activities while merging unrelated communications, reducing the behavioral context available to transformers. We present a unified pipeline abstraction that decomposes transformer-based NIDS into four design dimensions (traffic representation, temporalization, transformer backbone, and prediction aggregation) and propose session-coherent temporalization, which constructs sequences from communication sessions instead of fixed time windows. Experiments on three complementary intrusion detection datasets show that temporalization is the dominant factor governing transformer performance: session-coherent temporalization more than doubles Macro-F1 over timestamp-based windowing, enabling lightweight transformers to achieve up to 0.95 Macro-F1 at substantially higher inference throughput than pretrained models. We further vary the transformer size from 0.65M to 3.38M parameters and find that larger transformer models provide no measurable accuracy improvement despite substantially higher computational cost, while zero-shot cross-dataset evaluation shows only modest gains under domain shift. These results indicate that preserving communication-session semantics, rather than scaling transformer capacity, is the key to accurate and efficient transformer-based intrusion detection.

Low-altitude aviation is moving toward large-scale operations increasingly dependent on 5G cellular connectivity. However, 5G mobility remains blind to the operational risk of its decisions: the same interruption may be harmless in cruise over an empty field but critical near obstacles, crowds or other aircraft. Although 5G already provides mechanisms to obtain location, population density, and radio conditions, mobility decisions do not translate this information into an operational risk metric. We propose Risk-Aware Mobility (RAM), which maps predicted communication disruptions to an operational-risk proxy using information staleness, position uncertainty, and operational context, and exposes this risk to existing mobility mechanisms. In a dense São Paulo deployment with 188 licensed 5G sectors, risk-aware handover timing reduces the mean operational-risk proxy by 45% relative to conventional A3 without degrading serving-link quality. These results motivate shifting 5G mobility from optimizing handover performance to managing the operational risk of mobility decisions. Beyond handover timing, this perspective raises a broader question: which cells should serve aerial operations when connectivity is evaluated by operational risk rather than radio performance alone?

We address the problem of designing an efficient algorithm for computing the all-pairs shortest-time paths in time-varying networks, specifically targeting the dynamic topological characteristics of low earth orbit (LEO) satellite constellations. We model such temporal graphs as $G = (V, E_t)$, where $V=\{1, 2,\ldots , n\}$ is the set of $n$ vertices in the graph, and $E_t$ is the set of $M$ time-varying edges considered over a total time window of size $T = M\tau$, $\tau$ being the duration of each interval in that time window. For such a network, our algorithm finds the all-pair shortest paths in $O(M^2n^3)$-time. A key advantage of our approach is its computational structure, which mirrors two matrix multiplication steps, first in the forward direction and then in the backward direction. This structure of the algorithm makes it exceptionally suitable for parallelization on SIMD architectures like GPUs, thereby offering a significant speedup over traditional shortest-path algorithms and enabling efficient, network-wide message routing in rapidly changing LEO satellite constellations.

Emerging healthcare services, such as real-time diagnostics, remote health monitoring and computer-assisted surgery, require stringent Quality of Service (QoS) guarantees while operating under limited and heterogeneous edge resources. Existing Internet of Medical Things (IoMT) orchestration methods primarily focus on communication metrics such as latency and bandwidth, while overlooking edge resource constraints and dynamic adaptation to workload and user mobility. This paper presents a novel resource-slicing-based adaptive orchestration framework that integrates 5G network slicing (NS), resource-aware orchestration, and mobility-aware task offloading into a single adaptive system. IoMT services are assigned to distinct 5G network slices while jointly considering CPU capacity, memory availability and amount of storage, enabling more efficient service placement and execution. In addition, the system supports user mobility and dynamic task offloading to maintain service continuity under changing network conditions. SimPy-based simulation demonstrates that the proposed method enhances service deployment efficiency, reduces request blocking, and improves user acceptance while maintaining reliable performance across diverse IoMT scenarios.

Collective communication is a critical performance bottleneck in large-scale distributed training. Existing libraries and synthesis frameworks compute open-loop schedules once, assuming a static topology and peak link capacities. In production, however, persistent gray failures can silently degrade link throughput and latency for hours, causing pre-computed schedules to lose much of their efficiency during execution. In this paper, we present Measurement-based Collective Communication Library (MCCL), a collective communication library that closes the loop between runtime network measurement and dynamic schedule synthesis. MCCL passively measures GPU-to-GPU throughput, detects persistent degradation, re-synthesizes the affected communication-intensive schedules with warm-started synthesizers, and hot-swaps them without interrupting training. We validate the complete loop on a multi-node hardware prototype and use simulations to evaluate cluster-scale scenarios. In our experiments, MCCL incurs negligible overhead (<2%) while maintaining high measurement accuracy (<1.2% error) and low schedule hot-swap time (about 12 ms). Notably, under gray failures, MCCL reduces training time by up to 4.99x over static schedules on simulated training workloads for state-of-the-art LLMs such as Llama and DeepSeek.