Top 20 Networking and Internet Architecture Papers: Oct 26 - Nov 1, 2026

This survey examines networked humanoid intelligence through four functions of information exchange: communication, coordination, collaboration, and social intelligence with human interaction.

We present MIRAGE, a GPU-accelerated channel emulator that applies propagation effects directly to in-phase and quadrature samples exchanged by software 5G New Radio stacks.

However, three obstacles block real-time detection on such roaming endpoints: detection state is lost at every inter-domain handover; transmitting raw telemetry to a fixed detector exhausts the uplink and the device battery; and full-depth language model inference cannot meet millisecond latency budgets on constrained hardware. To tackle these challenges, we propose MAPLE-Sec, a mobility-aware partitioning framework that splits a compact, security-tuned language model across a roaming device and its nearest edge node.

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.

We present DYNAS (Dynamic Network Attack Simulator), which combines parameterized large-scale scenario generation with stateful intrusion detection responses, explicit scan false-positive and false-negative noise, temporary service unavailability, and duration-based action timeouts.

By bridging the AI and satellite networks, this paper provides a research roadmap for the implementation of space-native AI technologies.

This paper develops a kinetic framework for geometry-induced serving-region crossings in nonstationary cellular networks, connecting boundary-normal velocity statistics with fluid spatiotemporal stochastic geometry (F-STSG) for density-based transport reconstruction.

This paper proposes Agentic LinkDreamer, a soft-computing framework integrating a latent world model with an interpretable neuro-fuzzy decision agent.

The study combines semantic analysis with degree-controlled quantum--classical link prediction.

We present Axiom, a language for expressing network designs, and a checker that finds every violation and the responsible commands.

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.

This paper proposes GraphBARC, a graph-structured active radio map construction framework that integrates learned spatial modeling with analytical Bayesian inference.

We present Trim, which models a channel as a two-sided inventory with costly refills.

A game-theoretic framework is developed to capture attack-defense interactions and characterize equilibrium strategies based on attack effectiveness and cost parameters.

We reformulate live migration as a steady-state execution model and introduce a novel virtual stationarity runtime paradigm.

This dissertation investigates whether Natural Language Processing (NLP) based semantic message prioritisation can improve the treatment of urgent disaster messages in opportunistic emergency communication, using the 2015 Nepal earthquake as a case study.

I present a measurement-first edge IoT testbed and dataset that elevate timeliness, provenance, and energy to first-class telemetry dimensions.

Always-on sensing therefore requires a classifier that is small enough to remain powered while the rest of the system sleeps. We investigate whether Spiking Neural Networks (SNNs) can provide such a classifier.

This paper presents a federated learning (FL) framework for semantic communication (SemCom) over the multiple access channel (MAC), where multiple distributed transmitters simultaneously convey semantic information to a common receiver using end-to-end learned semantic representations.