Computer Networks Case Study Topics

We see billions of packets cross campus and cloud networks every second, and we also see how a single misconfigured route or congested link can break a user’s day. We write today as TopicSuggestions, a team of academic researchers who help students turn theory into actionable case studies in computer networks. We aim to share a concise, student‑friendly list of case study topics that are specific, researchable, and grounded in real systems.

Case Study Topic Ideas on Computer Networks

We will map the list by practical areas—core protocols and routing, wireless and mobile, security and resilience, performance and QoS, cloud/SDN/edge and 5G, and measurement and ethics—so you can pick a focus, find data and tools, and scope your project with confidence.

1. We conceptualize a tax system for synthetic labor: taxing autonomous AI agents as income-generating entities

– We define the tax subject by asking whether we assign residency, liability, and personhood to AI agents, owners, or platform orchestrators.
– We model incidence by testing whether taxing AI outputs substitutes for or complements payroll and corporate income taxes.
– We design compliance by prototyping on-chain withholding, oracle-based revenue attribution, and audit trails for agentic workflows.

2. We engineer real-time carbon micro-taxes embedded in IoT supply chains via satellite and sensor MRV

– We measure whether high-frequency, per-gram CO2e micro-tolls shift routing, inventory, and sourcing decisions in logistics.
– We validate privacy-preserving MRV by integrating differential privacy and secure multiparty computation for emissions data.
– We estimate welfare effects by simulating pass-through to consumer prices and cross-border leakage in fragmented value chains.

3. We design a cross-border “data dividend tax” on contributions to federated learning models

– We quantify the taxable base by attributing model performance gains to jurisdiction-tagged gradient updates.
– We allocate revenue by testing Shapley-value apportionment versus nexus-based rules under conflicting privacy regimes.
– We evaluate equity by comparing household-level distributions of data-derived dividends to traditional capital and labor income.

4. We build VAT rules for mixed-reality commerce spanning physical, AR, and VR spaces with shifting nexus

– We locate place-of-supply by auditing avatar geolocation, server provenance, and haptic-device endpoints under competing rules.
– We detect fraud by modeling “avatar laundering” and synthetic identity arbitrage across platforms and tax zones.
– We prototype enforcement by deploying geo-fenced smart contracts that auto-apply rate changes as users transition realities.

5. We govern decentralized autonomous municipalities with on-chain fiscal constitutions and tax collection

– We define taxable events inside DAms by distinguishing protocol-level seigniorage, validator rewards, and local services fees.
– We interoperate with national authorities by designing read/write APIs for audits, withholding, and dispute resolution.
– We test legitimacy by running governance experiments on voter-approved, fork-resistant tax rate adjustments.

6. We tax biometric and neurodata monetization arising from wearables and brain–computer interfaces

– We classify income by determining whether neurodata rentals are labor, capital, or royalty streams for tax purposes.
– We measure regressivity by estimating who sells bio-signals and how tax burdens shift across income and health status.
– We safeguard consent by proposing cryptographic licensing that meters taxable use without revealing raw signals.

7. We pre-arrange taxation of extraterrestrial resource rights via futures, bonding, and treaty-based auctions

– We price pre-extraction tax claims by requiring exploration bonds and insurance to back deferred tax liabilities.
– We allocate rights by testing auction mechanisms that assign revenue shares among launching, manufacturing, and registry states.
– We deter evasion by modeling “flags of convenience” in space and specifying minimum effective tax rates in orbital zones.

8. We implement algorithmically personalized marginal tax rates with verifiable fairness and privacy constraints

– We formalize IC-compliant personalization by constraining AI-set rates with transparency, monotonicity, and equal-opportunity tests.
– We simulate welfare effects by comparing static schedules to adaptive ones under behavioral elasticities and gaming risks.
– We audit algorithms by deploying zero-knowledge proofs that certify fairness metrics without exposing taxpayer features.

9. We deploy quantum-secure, zero-knowledge tax reporting and exchange frameworks

– We benchmark post-quantum schemes for bilateral and multilateral information exchange under the Common Reporting Standard.
– We design ZK attestations that prove correct withholding, residency, and beneficial ownership without revealing amounts.
– We stress-test performance by measuring latency, cost, and verification failure rates at tax-season scale.

10. We integrate time-banking and mutual-aid credits into formal income tax and welfare interactions

– We standardize valuation by proposing shadow wages and safe harbors for nonmarket service exchanges.
– We estimate labor-market effects by testing substitution between time credits and taxable gig work.
– We run policy pilots by randomizing tax-credit multipliers and observing impacts on participation, equity, and revenue.

11. Programmable metasurface–assisted mmWave mesh for dense urban IoT

We propose studying adaptive mmWave mesh networks augmented by programmable metasurfaces to improve coverage and multi-path diversity.
We pose these research questions: 1) How can control-plane protocols exploit metasurface state to reduce beam training and handover overhead? 2) How does metasurface reconfiguration affect interference patterns and spatial reuse in dense deployments? 3) What distributed algorithms can jointly optimize metasurface states and routing for latency- and energy-sensitive IoT flows?
Overview of how to work: We will model metasurface-actuated channels, implement cross-layer simulators that couple reconfiguration latency with MAC/routing decisions, and validate with a small hardware-in-the-loop testbed combining steerable antennas and reconfigurable reflectors.

12. Quantum-aware routing metrics for hybrid classical/quantum key-distribution networks

We investigate routing metrics and protocols that treat quantum-key-generation capacity as a first-class path attribute.
We pose these research questions: 1) How can path selection integrate ephemeral QKD key rates, classical latency, and security utility into one metric? 2) How do routing dynamics affect end-to-end key freshness and application-level secrecy guarantees? 3) What scalable distributed algorithms maintain quantum-aware routes under link failures and heterogeneous key-generation hardware?
Overview of how to work: We will build analytical models of mixed QKD/classical links, extend existing routing stacks with quantum-capacity-aware metrics, simulate at scale with realistic QKD rate traces, and prototype on a lab testbed combining QKD links and classical routers.

13. Topology-aware federated learning over intermittent vehicular networks

We explore federated learning protocols that exploit predicted vehicular connectivity patterns to improve model convergence under intermittent links.
We pose these research questions: 1) How do we schedule and aggregate federated updates when peer sets are highly transient and routing is multi-hop? 2) How does topology prediction (trajectory-based) improve update selection and compression strategies? 3) How can privacy and robustness be preserved when aggregation occurs opportunistically across vehicle clusters?
Overview of how to work: We will collect vehicular mobility traces, implement federated algorithms on a DTN-capable simulator (or testbed), design topology-prediction modules, and evaluate convergence, communication overhead, and privacy leakage under realistic mobility.

14. Energy-harvesting underwater acoustic swarm networks with bio-inspired routing

We study routing and MAC strategies for underwater swarms where nodes harvest energy and mimic biological swarm behaviors to enhance persistence.
We pose these research questions: 1) Which bio-inspired coordination rules (e.g., quorum sensing, leader-follower) optimize lifetime vs. coverage under stochastic energy arrivals? 2) How should waveform selection and packet timing adapt to both acoustic channel state and local energy budgets? 3) How resilient are such strategies to adversarial noise and bursty interference?
Overview of how to work: We will combine acoustic channel and energy-harvesting models, design RL-augmented bio-inspired protocols, run high-fidelity underwater simulations, and validate with controlled tank experiments or field deployments with energy-harvesting prototypes.

15. Cross-layer PUF-based bootstrapping for secure neighbor discovery in dense IoT

We propose using device-intrinsic Physically Unclonable Functions (PUFs) to bootstrap cross-layer neighbor trust and rapid revocation in dense IoT networks.
We pose these research questions: 1) Can low-entropy PUF outputs be strengthened and integrated into neighbor discovery without heavy PKI? 2) How can link-layer metrics and PUF-derived identities be fused to detect relay and clone attacks? 3) What scalable revocation and key refresh mechanisms work under constrained devices and intermittent connectivity?
Overview of how to work: We will design lightweight cryptographic primitives that fuse PUF measurements and link fingerprints, implement protocols on microcontroller platforms with PUFs, evaluate attack resilience, latency, and energy on representative IoT apps.

16. Airborne mesh networks for wildfire monitoring with delay-prioritized service slices

We examine UAV-based airborne mesh networks that offer delay-prioritized slices for critical wildfire alarms while supporting lower-priority data.
We pose these research questions: 1) How to jointly schedule UAV flight paths, link formation, and slice resources to guarantee sub-second alarm delivery across large burn areas? 2) What fault-tolerance mechanisms maintain prioritized slices when UAVs fail or are grounded? 3) How do we balance energy, coverage, and latency for mixed-criticality sensing?
Overview of how to work: We will formulate mixed-integer online optimization for flight and slice allocation, develop lightweight heuristics for real-time control, simulate with fire-spread models and mobility constraints, and validate with UAV flight trials focusing on alarm latency and robustness.

17. Decentralized ephemeral trust via topology-derived cryptographic puzzles (no blockchain)

We study a decentralized trust mechanism where ephemeral network-topology signatures (contact graphs) serve as lightweight proofs-of-encounter for trust decisions.
We pose these research questions: 1) Can topology-derived puzzles resist Sybil and relay attacks while remaining lightweight for mobile devices? 2) How to design short-lived group keys and reputation anchored to recent encounter graphs without central authorities? 3) What are the privacy implications of encoding topology into trust artifacts, and how can they be mitigated?
Overview of how to work: We will define formal constructions for topology-based puzzles, analyze security properties, implement protocols in mobile ad hoc simulators, and empirically measure attack resistance and privacy leakage under realistic mobility.

18. Visible-light backscatter networks for zero-power indoor sensors and localization

We propose using visible-light backscatter to enable zero- or ultra-low-power indoor sensor networking that simultaneously supports coarse localization.
We pose these research questions: 1) How to design MAC protocols that coordinate backscatter uplinks and support low-latency event reporting under ambient light variation? 2) Can client-side signal features enable continuous passive localization with sub-meter accuracy using backscatter reflections? 3) How to mitigate multi-user interference and exploit multiple light sources for spatial multiplexing?
Overview of how to work: We will prototype optical backscatter tags and receivers, develop PHY/MAC stacks tuned to indoor lighting dynamics, create joint comm+localization algorithms, and evaluate in furnished indoor environments.

19. Machine-checked verification of network-slicing policies for mixed-criticality industrial control

We explore formal, machine-checked proofs that network-slicing configurations meet timing and safety guarantees for mixed-criticality industrial control traffic.
We pose these research questions: 1) How to express slicing policies and schedulability constraints in a proof assistant-friendly specification language? 2) Can we mechanically prove end-to-end real-time bounds under dynamic slice reconfiguration and link variability? 3) How to synthesize slice parameters that are both verifiable and practically deployable on industrial switches?
Overview of how to work: We will formalize slice semantics, encode schedulability theorems in Coq/Isabelle, build automated checkers that consume network configs, and evaluate by verifying representative industrial topologies and controller workloads.

20. AI-driven congestion control with human-in-the-loop ethical constraints

We design congestion control algorithms driven by reinforcement learning that explicitly incorporate human-defined ethical constraints (fairness, safety, priority override).
We pose these research questions: 1) How to encode ethical constraints and human override policies as formal constraints in learned congestion controllers? 2) How do we audit and explain RL decisions in real time to network operators and end-users? 3) Can a human-in-the-loop feedback loop correct undesirable emergent behaviors without destabilizing control?
Overview of how to work: We will build constrained-RL congestion control agents, design interpretability layers and operator interfaces, conduct simulation and emulation experiments with human operator studies, and evaluate stability, fairness, and responsiveness to overrides.

21. Temporal Graph Neural Control for Intermittent UAV Swarm Networks

We ask: How can temporal graph neural networks predict and control connectivity in intermittently connected UAV swarms to maximize mission throughput and safety? We ask: What architectures best capture flight dynamics and radio propagation jointly for real-time routing decisions? We ask: How does learned control compare to classic delay-tolerant routing under varying mission constraints? We outline: We will collect flight and link traces from small-scale UAV experiments and high-fidelity emulators, train temporal GNNs to output routing/control actions, and evaluate in simulation and on hardware-in-the-loop under metrics of delivery rate, latency, and energy.

22. Energy-aware Quantum Network Control for NISQ-era Routers

We ask: How can we design routing and scheduling algorithms for near-term quantum repeater networks that minimize energy and decoherence penalties? We ask: What classical control policies best trade qubit fidelity, entanglement generation rate, and power consumption on NISQ hardware? We outline: We will model repeater nodes with energy and decoherence constraints, develop constrained optimization and RL baselines, and validate via simulator integrating physical error models and estimated power costs to measure throughput per joule and end-to-end fidelity.

23. Socio-technical Resilience Modeling of Community Mesh Networks under Disinformation-driven Load Spikes

We ask: How do coordinated disinformation campaigns alter traffic patterns in community mesh networks and what technical countermeasures reduce disruption? We ask: What hybrid socio-technical metrics capture resilience that pure network metrics miss? We outline: We will combine traffic injection models informed by social media cascades with mesh topology simulations, design adaptive caching/priority policies, and assess resilience by measuring service continuity, latency, and community trust via user studies or surveys.

24. Programmable Optical Packet-level Microburst Mitigation Using Fast Photonic Buffers

We ask: Can actively programmed photonic buffers mitigate sub-microsecond microbursts in optical networks without electronic conversion? We ask: What control plane primitives are required to detect and steer bursts to optical delay lines in real time? We outline: We will prototype a control loop using high-speed optical taps and tunable delay elements, implement controllers in FPGA/software, and quantify packet loss, jitter, and power overhead in lab optical testbeds.

25. Adaptive Semantic Routing for Privacy-preserving IoT Data Aggregation

We ask: How can routers use lightweight semantic-aware compression to route IoT data while preserving differential privacy guarantees? We ask: What trade-offs exist between semantic fidelity, privacy budget, and routing efficiency across heterogeneous edge networks? We outline: We will design routers that classify and compress data streams semantically, integrate DP mechanisms, simulate across IoT workloads, and measure utility, privacy leakage, and network load.

26. Cross-layer Co-design of In-band Model Update Dissemination for Federated Edge Learning

We ask: How can we design cross-layer protocols to disseminate evolving ML model updates in-band with application traffic while meeting latency and convergence goals? We ask: What scheduling and coding strategies minimize staleness and bandwidth without hurting application QoS? We outline: We will instrument edge devices, implement joint transport-scheduling primitives that prioritize model diff packets and apply network coding, and evaluate end-to-end ML convergence, model accuracy, and application performance.

27. Self-healing Interdomain Routing with Economically-incentivized Cryptographic Attestations

We ask: Can cryptographic attestations tied to verifiable economic penalties speed automated recovery from BGP hijacks while preserving incentives for AS operators? We ask: What attestation formats and settlement protocols integrate with existing routing ecosystems with minimal disruption? We outline: We will design attestation schemas, simulate interdomain deployment with game-theoretic incentives, prototype settlement logic using smart contracts or escrow, and measure attack detection time, recovery speed, and incentive compatibility.

28. Deterministic Latency Slicing over Converged Sub-6 GHz and THz Links for AR/VR Mobility

We ask: How can we provide deterministic low-latency slices by jointly scheduling sub-6 GHz and THz channels for mobile AR/VR users? We ask: What link-layer handover and redundancy strategies minimize perceived motion-to-photon delay under mobility? We outline: We will build a cross-frequency scheduler that enforces per-flow latency bounds, run emulation with mobility traces and channel models, and evaluate perceived latency, jitter, and energy across scenarios.

29. Network-coded Microservice Choreography for Low-latency Fog Computing

We ask: How can network coding be applied to microservice RPC flows to reduce end-to-end latency and retransmission overhead in fog deployments? We ask: What service composition patterns benefit most from coded choreography, and how to integrate coding with service meshes? We outline: We will prototype coding-aware sidecars that encode RPC payloads across parallel fog paths, measure tail latency and throughput on microbenchmarked service chains, and analyze complexity vs. benefit.

30. Measurement-driven Synthesis of Realistic Planetary-scale Network Topologies for Mars–Earth Communication Planning

We ask: How can we synthesize realistic temporal network topologies for interplanetary links that capture orbital dynamics, scheduled windows, and deep-space antenna constraints? We ask: How do routing and transport protocols perform on these synthesized topologies versus naive models? We outline: We will combine orbital mechanics, antenna schedules, and historical deep-space network telemetry to generate temporal topology generators, run protocol evaluations in emulation, and report delivery ratios, latency distributions, and recommended protocol adaptations.

31. Microburst Dynamics in Optical Datacenter Fabrics: cross-layer detection and mitigation

We propose to study transient microburst events in dense optical datacenter interconnects and their cross-layer signals.
We ask: How do microbursts manifest differently at the optical, link-layer, and transport-layer in coherent DWDM fabrics?; We ask: Which in-network telemetry features most reliably predict imminent buffer overflow across layers?; We ask: Can lightweight cross-layer controllers dynamically reconfigure optical transceivers and flow schedules to eliminate packet loss without harming throughput?
We outline an approach: We will instrument a small optical testbed and an ns-3 augmented optical module, collect synchronized telemetry across layers, build ML predictors using time-series features, and prototype a cross-layer controller (SDN + transceiver API) to evaluate end-to-end latency, loss, and throughput.

32. Population-coded Pseudonyms for Privacy-Preserving Routing in Intermittent Sensor Meshes

We propose a novel identity scheme that uses population-coded pseudonyms to enable routing while preserving device anonymity in intermittently connected sensor meshes.
We ask: How can population-coded pseudonyms preserve routeability while preventing long-term device linkability?; We ask: What routing algorithms can operate on pseudonym aggregates without requiring global identity resolution?; We ask: What is the trade-off between privacy strength and routing efficiency under varying contact patterns?
We outline an approach: We will design the pseudonym encoding and resolution protocols, simulate DTN/contact graphs with synthetic and real mobility traces, measure anonymity metrics (linkability, entropy) and routing metrics (delivery ratio, delay), and implement a lightweight prototype on constrained motes.

33. Quantum-Safe Fast Handover for mmWave Vehicular Networks

We propose to design a handover protocol for mmWave vehicular networks that provides low-latency re-association and uses post-quantum cryptography for long-lived session security.
We ask: How can handover pre-authentication and beam prediction be combined with PQC primitives to achieve sub-10 ms secure handovers?; We ask: Which PQC schemes are practical within vehicular hardware constraints for frequent handovers?; We ask: How does the combined protocol affect end-to-end application QoS (e.g., V2X safety messages)?
We outline an approach: We will model mobility/beam-dynamics in an urban vehicular trace, integrate candidate PQC algorithms into a handover state machine, measure computational/latency overhead on representative vehicular hardware, and evaluate safety-message latency and authenticity under adversarial scenarios.

34. Renewable-aware Network Slicing: SLA management under green energy variability

We propose to create SLA-aware network slice orchestration that adapts slice resource allocations and pricing based on forecasted renewable energy availability at edge sites.
We ask: How can slice SLAs incorporate probabilistic renewable forecasts while providing predictable service?; We ask: What scheduling and pricing mechanisms can incentivize workloads to shift in time or location in response to green energy signals?; We ask: How do these mechanisms affect overall carbon footprint and operator revenue?
We outline an approach: We will formulate a stochastic optimization model combining SLA constraints, energy forecasts, and economic incentives, run trace-driven simulations with real renewable generation data, and implement a prototype orchestrator extension to validate slice migration and pricing strategies on an edge cloud testbed.

35. Topology-aware Federated Anomaly Detection at the Edge

We propose to design a federated learning aggregation method that incorporates network topology and routing metrics to improve anomaly detection across distributed edge nodes.
We ask: How can topological proximity and traffic flow overlap be encoded into federated model aggregation to reduce false positives?; We ask: What privacy-communication trade-offs arise when exchanging topology-augmented model summaries?; We ask: Can we prove robustness to localized adversarial updates that exploit network topology?
We outline an approach: We will derive aggregation weights from topology-aware similarity metrics, run federated experiments on synthetic and real edge traffic datasets, measure detection accuracy and communication cost, and analyze robustness with adversarial update injections.

36. Motion-predictive Topological Routing for AR/VR Multi-user Streams

We propose routing and in-network caching strategies that incorporate short-term user motion prediction to stabilize AR/VR stream quality in wireless mesh networks.
We ask: How far ahead do motion predictions need to be to meaningfully reduce rebuffer and motion-to-photon jitter?; We ask: Which network-level actions (route prefetch, multipath steering, in-network transcoding) yield the best perceived QoE given prediction uncertainty?; We ask: How can we quantify the combined cost of prediction errors and network control actions?
We outline an approach: We will integrate lightweight motion predictors into an emulation of multi-user AR sessions, compare routing strategies that use predictions versus reactive methods, and evaluate QoE metrics (frame loss, latency, motion sickness proxies) and network costs.

37. Bio-inspired Self-healing Multipath Protocols for Underwater Acoustic Swarms

We propose multipath routing protocols for underwater acoustic swarms that draw on biological swarm repair behaviors to tolerate link failures and mobility.
We ask: Which bio-inspired local rules (e.g., pheromone-like signals, quorum sensing) best support rapid route repair without global reconfiguration?; We ask: How do acoustic channel constraints and energy budgets shape effective self-healing behaviors?; We ask: Can self-healing multipath improve mission success for cooperative sensing tasks?
We outline an approach: We will model swarm behaviors mapping to routing primitives, simulate realistic underwater acoustic channels and swarm missions, implement protocols in an underwater acoustic emulator or lab tank acoustic nodes, and measure delivery, repair latency, and energy consumption.

38. Lightweight In-band Metadata Channels for IoT Using Homomorphic Fingerprints

We propose an in-band metadata encoding that uses compact homomorphic fingerprints to allow middleboxes to perform simple operations (aggregation, filtering) without exposing raw IoT payloads.
We ask: How can homomorphic fingerprints be designed to enable sum/count operations while remaining resilient to inversion attacks?; We ask: What is the computational and bandwidth overhead for IoT-class devices and middleboxes?; We ask: Which privacy/utility trade-offs arise for common IoT analytics tasks?
We outline an approach: We will design fingerprint encodings supporting target operations, prove privacy bounds under threat models, implement firmware-level encoders for constrained devices and middlebox decoders, and evaluate analytics accuracy, latency, and privacy on IoT datasets.

39. In-network SLA Enforcement via Programmable Compute and Micro-pricing

We propose to enforce fine-grained SLAs by combining programmable in-network compute (P4/SmartNICs) with dynamic micro-pricing signals to shape congestion and priorities.
We ask: Which minimal in-network primitives are sufficient to guarantee per-flow micro-SLA properties (latency percentile, jitter) at line rate?; We ask: How can micro-pricing be computed and applied in-network to incentivize flow adaptation without centralized control?; We ask: What are stability and fairness properties when many tenants react to micro-prices?
We outline an approach: We will prototype P4 pipelines implementing SLA metering and price signaling, develop local pricing algorithms based on queue states, run multi-tenant emulations to evaluate SLA compliance, economic outcomes, and stability under adaptive tenant strategies.

40. Decentralized Trust Bootstrap for Opportunistic Networks via Social-proof Overlays

We propose a decentralized overlay that leverages ephemeral social proofs (small co-presence encounters witnessed by third parties) to bootstrap trust without central PKI in opportunistic networks.
We ask: How many co-witnessed encounters and what witness diversity are required to reach a target trust level for message acceptance?; We ask: How resilient is social-proof bootstrapping to collusion and sybil attacks in sparse encounter graphs?; We ask: How can we design compact witness attestations suitable for intermittent connectivity and constrained devices?
We outline an approach: We will formalize a trust metric based on witness graphs, simulate encounter traces with adversarial nodes, design compact attestation formats and epidemic propagation strategies, and validate trust convergence and attack resistance on mobile testbeds and trace-driven simulations.

41. Autonomous Intent-Conflict Resolution for Multi-Domain SDN:

Research questions: How do we design an intent-merging algebra that provably avoids oscillations when conflicting high-level policies arrive from heterogeneous administrative domains; How can we quantify and enforce weakest-link safety guarantees across composed intents without centralized authority? We will formalize intent composition using algebraic structures, build a distributed consensus-assisted mediator prototype on OpenDaylight, and evaluate stability and safety through emulated inter-domain topologies and adversarial intent injection.

42. Energy-Proportional Opportunistic MAC for Low-Power Mobile VR:

Research questions: How can a MAC protocol dynamically trade latency and energy across clustered mobile VR clients to maintain perceptual quality; What sensing signals and lightweight models best predict acceptable perceptual degradation for opportunistic sleep scheduling? We will instrument a mobile VR testbed, design a MAC that uses real-time head-motion and frame-drop predictors, and validate QoE vs. energy savings with human-subjects and automated perceptual metrics.

43. Verifiable Route Attribution in Multi-Path Overlays for Accountability:

Research questions: How can we cryptographically bind path provenance to packets with sub-millisecond verification and minimal header overhead; Can we design a revocation/responsibility mechanism that attributes misbehavior to specific autonomous segments without exposing full path secrets? We will design a compact cryptographic tagging scheme, implement it in a userspace overlay, and stress-test attribution accuracy and performance under partial trust and path-churn scenarios.

44. Adaptive Metadata Minimization for Encrypted IoT Flows:

Research questions: How can edge nodes adaptively strip or obfuscate flow metadata while preserving application-layer functionality; What machine-learned policies best predict when metadata minimization safely preserves functionality versus when it breaks protocol semantics? We will collect encrypted-IoT traffic traces, train lightweight classifiers at edge gateways, and measure privacy gains, false-positive functional breaks, and computational cost on constrained hardware.

45. Intent-Aware Quantum-Safe Routing in Hybrid Classical-Quantum Networks:

Research questions: How should routing metrics be redefined to account for fragile quantum entanglement lifetimes and classical control-plane latencies; Can we design hybrid path selection that maximizes usable quantum channel capacity under dynamic decoherence and node failures? We will model entanglement decay in network simulators, extend routing algorithms to include quantum-cost metrics, and run co-simulation of classical control and quantum link dynamics to evaluate throughput and reliability.

46. Federated AI for Congestion Prediction with Privacy Budgets in Edge Networks:

Research questions: How do we balance timely congestion predictions with differential-privacy constraints in a federated learning setup across heterogeneous edge nodes; What aggregation protocols minimize utility loss while bounding privacy leakage from partial model updates? We will deploy a federated pipeline across emulated edge sites, implement DP mechanisms and secure aggregation, and benchmark prediction accuracy, latency, and privacy-utility trade-offs under realistic traffic patterns.

47. Resilient Service Discovery over Intermittent Disaster Meshes:

Research questions: How can we design a service-discovery protocol that guarantees probabilistic reachability bounds under partition-prone, energy-limited disaster meshes; What replication and cache coherence strategies yield the best trade-off between discovery latency and energy consumption? We will build a DTN-aware discovery protocol, simulate various disaster scenarios with mobility and energy constraints, and measure time-to-discovery, message overhead, and service freshness.

48. Microsecond-Scale Network Slicing Admission Control for Ultra-Reliable Low-Latency Services:

Research questions: How can admission control decisions be made at microsecond timescales across distributed slice controllers while preserving SLA isolation; What lightweight consistency model suffices to avoid SLA violations under bursty correlated arrivals? We will prototype a hierarchical admission-control mechanism using programmable dataplane primitives (P4), measure decision latency and SLA adherence under synthetic and replayed workload bursts, and analyze consistency-error impacts.

49. Privacy-Preserving Topology Learning from Encrypted Traffic Metadata:

Research questions: How accurately can we infer fine-grained topology features (e.g., peering relationships, bottleneck locations) from encrypted traffic patterns while provably bounding privacy leakage of endpoints; Which feature-aggregation mechanisms best hide individual behaviors yet retain topology signal? We will design aggregation and feature-sanitization pipelines, run topology-inference attacks on sanitized datasets, and quantify attacker success vs. privacy leakage using information-theoretic metrics.

50. Bio-Signal-Driven Network Admission for Wearable Health Meshes:

Research questions: How can we integrate real-time physiological signals into network admission and prioritization decisions without creating adversarial privacy or safety risks; What safety constraints and failover policies ensure that network decisions do not endanger critical health data delivery? We will collect synchronized wearable bio-signals and network traces, design admission policies that map clinical urgency to network priority, and evaluate timeliness, privacy risk, and safety under attacker models and network stress tests.