Q-CSM: Q-Learning-based Cognitive Service Management in Heterogeneous IoT Networks

Berk Canberk; Kerem Gursu; Kubra Duran; Mehmet Ozdem

REVIEW 4 major objections 6 minor 21 references

Q-CSM: Q-Learning-based Cognitive Service Management in Heterogeneous IoT Networks

T0 review · 4 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Q-CSM is a Q-learning-based cognitive service management framework that, in simulated smart city networks with wind turbines, solar panels, and transportation systems, responds to topology changes 38.7% faster (about 50% faster in a…

desk verdict A sensible but under-validated integration of a translation proxy and Q-learning; the two headline gains don't isolate the Q-learning contribution and need an ablation and a spelled-out reward before the claims hold up. read the letter →

arxiv 2411.14281 v1 pith:EVJPJEER submitted 2024-11-21 cs.NI

Kubra Duran , Mehmet Ozdem , Kerem Gursu , Berk Canberk This is my paper · ORCID

classification cs.NI

keywords Q-learningcognitiveservicemanagementheterogeneousIoTsmartcityconstraineddevicesqualityofAgentManagerprotocoladaptation

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper claims that a three-layer cognitive management framework, Q-CSM, can make heterogeneous smart city IoT networks self-managing. The framework's IoT Agent Manager normalizes data from CoAP, MQTT, and HTTP sensors into a single JSON format, and a Q-learning recommendation engine chooses which QoS class each service should use as the network changes. In simulation with two or three smart city services, Q-CSM responds to topology changes 38.7% faster in the two-service case and about 50% faster in the three-service case than a protocol-adaptive method, while extending average constrained-device lifetime by 19.8%. If these results carry over from simulation, the framework offers a path to running mixed constrained devices in one network without sacrificing QoS or battery life.

What carries the argument

The load-bearing mechanism is the layered Q-CSM architecture together with its Q-learning formulation. The Adaptation Layer contains the IoT Agent Manager with a Message Handler, a Proxy that translates CBOR to JSON, and a Data Pool, so heterogeneous application-layer protocols (CoAP, MQTT, HTTP) are normalized before management. The Management Layer defines QoS classes via service-dependent KPIs and computes a QoS class density as a ratio of active devices to queued devices, $\alpha(Q_i)=\sum_m O_i / V_{Q_i}$, where $O_i$ is the total number of active IoT devices and $V_{Q_i}$ is the number of devices waiting in the queue for class $i$. The Q-learning engine maps states (QoS class changes) to actions (which service gets which QoS class) through the Bellman update $Q_{t+1}(S,A)=Q(.)+\alpha(R+\gamma\max_{A'}Q(S',A')-Q(.))$, with an $\epsilon$-greedy exploration policy. The reward is tied to optimizing requested KPIs while considering whole-network lifetime, with device lifetime modeled as a maximum of 10 years decreasing in proportion to requested data.

What would settle it

Run the exact 2-service and 3-service smart city scenarios on physical Class 0/1 constrained nodes with the same KPI thresholds and compare against the protocol-adaptive strategy; if the measured response-time gain falls well short of 38.7% or the lifetime gain falls well short of 19.8%, the central claim is not supported. Alternatively, reproduce the simulation with the reward function stated explicitly and the same 10-year proportional-battery model; if the reported learning-rate ranking (0.07 best) changes, the Q-learning component's contribution is not stable.

Watch

Extended reading notes

Core claim

On the paper's own terms, Q-CSM's discovery is that adding a normalization proxy and a Q-learning policy over QoS classes is enough to improve both network responsiveness and device longevity in heterogeneous IoT settings. The discovery is demonstrated on a smart city with wind turbines, solar panels, and transportation: the framework builds QoS classes from service-specific KPI thresholds (delay and loss rate), computes a QoS class density from active devices and queue occupancy, and lets each service's master node act as an agent that learns the QoS class assignment. The reported outcome is a 38.7% faster response time with two services, roughly 50% faster with three, and 19.8% longer average device lifetime compared with a traditional protocol-adaptive strategy under delay-sensitive and delay-tolerant QoS classes. The paper further states that a learning rate of 0.07 shows the highest cumulative reward, with both 0.7 and 0.007 converging to lower values.

Load-bearing premise

The result rests on the assumption that a simulated battery whose lifetime starts at 10 years and decreases in proportion to requested data, together with a Q-learning reward that favors the reported QoS and lifetime metrics, faithfully represents how real constrained IoT devices consume energy.

Editorial extensions

If this is right

Heterogeneous smart city networks can operate without rewiring each device to a single protocol: the IoT Agent Manager's JSON normalization lets CoAP, MQTT, and HTTP sensors share one management plane.
A Q-learning policy over QoS classes can replace manual, static QoS configuration, because the agent continuously maps QoS class changes to actions and updates from rewards.
If the lifetime model is correct, energy-aware action selection extends the operational period of constrained devices, which matters for battery-powered Class 0 and Class 1 nodes.
The learning-rate result suggests that a moderate learning rate (0.07) balances exploration and exploitation, and that choosing this hyperparameter is part of the framework's performance.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

One testable extension is to replace the linear battery-drain assumption with measured discharge curves and re-run the same comparison; the framework's structure is agnostic to the energy model, so the result would show whether the 19.8% gain is tied to that assumption.
The QoS density formula uses queue occupancy alone; feeding delay and loss measurements directly into the state vector could sharpen the policy's response to topology changes.
The response-time comparison uses a protocol-adaptive baseline whose implementation details are not given; implementing both schemes on identical hardware with recorded packet-level traces would separate the normalization gain from the learning gain.
If the exact reward function were published explicitly, the 19.8% lifetime figure could be reproduced independently and the sensitivity to the assumed 10-year proportional-battery model could be checked.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Briefing: this is a pragmatic conference paper that plugs a data-format translation proxy and a Q-learning QoS selector into a smart city IoT scenario. The system is clearly described and the simulation is straightforward. The catch: neither headline result isolates the Q-learning engine, and the abstract over-credits it.

What's genuinely there: the IoT Agent Manager with CBOR-to-JSON translation is a sensible, practical element for heterogeneous constrained devices. The three-layer architecture (Sensor, Adaptation, Management) is easy to follow. The mapping of Q-learning components (state = QoS class, action = service-to-class assignment, epsilon-greedy) is standard but clean. The paper also explicitly states the linear lifetime assumption, which is at least transparent.

Soft spots, in order of weight. First, the response-time number. Figure 3 compares the IoT Agent Manager with and without data-format conversion. The traditional method lacks the proxy, so the 38.7% gain is a translation speedup, not a decision-learning gain. The Q-learning engine isn't in that comparison. The abstract's claim that the gain comes from 'Q-learning-based cognitive decision capability' is not supported.

Second, the lifetime comparison. The reward function R(s,a) is never written out; the text only says it optimizes 'requested KPIs' for the whole network. The lifetime model is '10 years, decreases in proportion to requested data.' That means an agent that minimizes requested data trivially extends lifetime. Without specifying R and without a baseline that shares the proxy but uses random or fixed QoS selection, you can't tell whether Q-learning, rather than the proxy or the reward design, is responsible for the 19.8%. This is an underdetermined comparison, not proof of an artifact.

Minor points: the 95% confidence interval claim is unsupported by any error bars; Eq. 1 is garbled (the sum indices and the relationship between Oi and VQi are unclear); the learning rate 0.07 is chosen post hoc with no sensitivity analysis.

Verdict: this deserves a serious referee, because the system design is coherent and the missing pieces are fixable: add an ablation, spell out R, report variance. I'd accept it for a workshop or a short conference with major revision. It's not a strong journal paper as it stands, but it's not a hollow simulation either.

Referee Report

4 major / 6 minor

Summary. The paper proposes Q-CSM, a three-layer cognitive service management framework for heterogeneous IoT networks. The sensor layer contains constrained IoT device classes, the adaptation layer includes an IoT Agent Manager that translates CBOR data into JSON through a proxy, and the management layer runs a Q-learning algorithm with an epsilon-greedy policy to recommend QoS classes for smart-city services. The evaluation is a Python/MATLAB simulation with two- and three-service topologies, compared against a protocol-adaptive baseline from the literature. The headline results are a 38.7% faster response time to topology changes in the two-service scenario, about 50% faster in the three-service scenario, and a 19.8% longer average device lifetime, along with a learning-rate comparison that selects 0.07 as the best value.

Significance. The problem addressed is timely: managing heterogeneous constrained IoT devices with different data formats and QoS requirements is a real deployment challenge. The decomposition into an adaptation layer and a cognitive recommendation engine is a sensible architectural proposal, and the use of three realistic smart-city services (wind turbines, solar panels, transportation) is a strength. The paper is also one of the few in its citation set that explicitly includes cognitive action recommendations rather than only prediction or resource allocation. If the performance claims can be corroborated with a fully specified reward function, controlled baselines that isolate the learning component, and statistical evidence, the framework would be a useful contribution. As presented, the quantitative headline claims are not yet convincingly supported because the experimental design does not separate the contributions of the proxy, the reward design, and the Q-learning engine.

major comments (4)

[Section III-C, Section IV] The reward function R(s,a) is never specified; the text only says that 'the agents accept a reward for each QoS class change in which the requested KPIs are optimized considering the whole smart city network.' This is the objective being optimized by Q-learning, and in Section IV the lifetime metric is evaluated under the model that device lifetime is 10 years and 'decreases in proportion to the requested data.' If the reward contains a battery/lifetime term, or if lower data volume is implicitly rewarded, then Q-CSM is trained on the same objective on which it is judged, while the baseline [9] is not stated to be trained on that objective. The 19.8% lifetime gain is therefore confounded. Please specify R(s,a) explicitly, including any lifetime or data-volume term, and add a control baseline that shares the adaptation layer but uses random or rule-based QoS selection to isolate the learning engine's contribution.
[Section IV, Fig. 3] The 38.7% response-time improvement is attributed in the abstract to the 'Q-learning-based cognitive decision capability,' but the experimental setup shows that the comparison is between Q-CSM, whose Proxy performs CBOR-to-JSON translation, and a traditional method that 'has no data type conversion for the different IoT data protocols.' The response-time gain is therefore explained by the adaptation-layer proxy, not by the Q-learning recommendation engine. Please either decompose the contributions by running a baseline that includes the proxy but not the Q-learning engine, or rephrase the claim to attribute the speedup to the IoT Agent Manager rather than the cognitive decision capability.
[Section IV, Table III] Table III lists 'Confidence interval 95%,' but Figures 3-5 show single point estimates without error bars, and the text does not state the number of independent runs or the statistical procedure used to derive a confidence interval. Without this information, the reported 38.7% and 19.8% point estimates cannot be assessed for reliability. Please provide means, confidence intervals or error bars, and the number of replications for each experiment.
[Section IV, lifetime experiment] The lifetime result rests on the stated assumption that the maximum device lifetime is 10 years and decreases 'in proportion to the requested data.' This linear energy model is asserted without justification, and no sensitivity analysis is provided. Even if the reward function is made explicit, the 19.8% lifetime improvement may be an artifact of this model rather than of the cognitive decisions. Please state whether the same energy model is applied to both methods, specify how 'requested data' is measured for each QoS class, and add a sensitivity analysis to the model's parameters.

minor comments (6)

[Section III-A] The description of Class 0 devices ends with the incomplete sentence 'For example, the maximum data size to be supported'; please finish the definition or remove the dangling example.
[Section III-C] The text says 'In Table I, we give three specific smart city scenarios,' but the table with the scenarios and KPIs is labeled Table II; please correct the cross-reference.
[Figure 1, Table II] Figure 1 shows 'latency < 1ms' under QoS prioritization, while Table II lists delay requirements of 300 ms or 100 ms; please reconcile these values or explain the distinction.
[Abstract, Section IV] The phrase 'most successive learning rate' should be 'most successful learning rate,' and 'simıulation' in Section IV is a typographical error.
[Section III-B, Figure 2] The module is called 'IoT Agent Manager' in most of the text and in Figure 2, but one sentence refers to it as 'IoT Device Manager'; please use one consistent name throughout.
[Section III-C, Eq. (1)] The QoS density formula α(Qi) = P_m Oi / VQi is garbled as printed: the numerator and the definitions of Oi and VQi are inconsistent, and the expression is not used later in the paper. Please rewrite the equation with consistent notation and clarify how α(Qi) enters the Q-learning state or reward.

Circularity Check

1 steps flagged · score 6.0 of 10

The 19.8% lifetime gain is the Q-learning objective itself; the response-time gain is attributable to the proxy, not the cognitive engine.

self definitional [Abstract; Section III-C (Reward, R; Algorithm 1 Ensure); Section IV (lifetime model and Fig. 4 discussion)]
"we design a Q-learning-based recommendation engine to optimize the devices' lifetime ... Reward, R: The agents accept a reward for each QoS class change in which the requested KPIs are optimized considering the whole smart city network. ... we assume the maximum value for the lifetime of IoT devices is 10 years, which decreases in proportion to the requested data. ... The main reason for this is the capability of Q-CSM to make decisions by considering the optimized lifetime depending on the desired IoT QoS classes."

The engine is defined (abstract; Algorithm 1 Ensure: 'optimized actions to increase the average lifetime of IoT devices') as optimizing device lifetime, and the lifetime model makes lifetime a decreasing function of requested data. The Q-learning reward is the optimization signal for QoS class selection, and the paper credits the gain to 'considering the optimized lifetime.' Thus the reported 19.8% longer lifetime is the trained objective realized against a baseline that is not described as optimizing that objective, not an independent prediction. Without specifying R(s,a) and without ablating the reward or sharing the adaptation layer with the baseline, the lifetime result is forced by the method's own objective.

full rationale

The paper's derivation chain for the headline lifetime improvement is circular in the specific sense that the Q-learning engine is defined and trained to optimize exactly the quantity (device lifetime, modeled as decreasing in requested data) on which it is then evaluated; the reported gain is the objective materialized, and the paper's own explanation ('considering the optimized lifetime') confirms this. The response-time claim is not circular but is confounded: Section IV states the traditional method 'has no data type conversion for the different IoT data protocols,' so the 38.7% speedup is produced by the adaptation-layer proxy, while the abstract attributes it to 'Q-learning-based cognitive decision capability.' The learning-rate exploration (0.07 selected as best) adds a post-hoc selection element to the reported results. No load-bearing self-citation chain or imported uniqueness theorem appears; references to the authors' prior work are background. Overall, one central claim reduces to its training objective, while the other is an attribution confound, so the circularity score is 6.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The reported gains rest on a hand-built simulation with chosen hyperparameters (learning rate 0.07 among three tested values, discount factor 0.99, 10,000 episodes), an assumed linear relationship between data demand and battery drain, and an incompletely specified reward function. The 38.7% response-time number depends on the proxy translation architecture and on an underspecified baseline. No code or data are released, and no new physical entities are introduced. The framework's novelty is the integration of known components, not a new theoretical mechanism.

free parameters (5)

Learning rate (alpha) = 0.07 (selected from tested set {0.7, 0.07, 0.007})
Hand-picked hyperparameter; the paper reports 0.07 as converging to the highest cumulative reward, so the best value is chosen after observing the simulation results.
Discount factor (gamma) = 0.99
Set in Table III without sensitivity analysis; a standard Q-learning choice but still a hand-set constant.
Number of episodes = 10000
Hand-set training budget in Table III; no convergence criterion is checked, and results may depend on it.
Maximum device lifetime and energy model = 10 years, lifetime decreases in proportion to requested data
Simulation assumption in Section IV that directly determines the reported 19.8% lifetime improvement; not validated against hardware.
Simulation run time for lifetime experiment = 20 minutes
Chosen in Section IV for the battery comparison; no justification or mapping to real battery units is given.

assumptions (5)

domain assumption Q-learning converges with 10,000 episodes, epsilon-greedy exploration, and gamma=0.99.
Algorithm 1 runs a fixed episode count with no convergence criterion; the paper does not prove or verify that the Q-table has converged.
domain assumption Device lifetime decreases proportionally to requested data.
Stated in Section IV; this linear energy model is what makes the lifetime comparison meaningful, but no empirical basis is given.
domain assumption Heterogeneous IoT data formats can be unified by converting CBOR to JSON in the Proxy module.
Section III-B assumes MQTT, HTTP, and CoAP messages arrive as JSON or CBOR and that translation is lossless; the response-time gain depends on this.
standard math Bellman equation provides the correct Q-value update for this environment.
Section III-C adapts the standard Bellman update; this is accepted background math.
domain assumption Each smart city service can be represented by one master agent and QoS class changes as states.
Section III-C maps Q-learning components onto the network; the state space is 'total number of QoS classes,' which may be too coarse to capture real topology dynamics.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Q-CSM: Q-Learning-based Cognitive Service Management in Heterogeneous IoT Networks." pith.science (2026). https://pith.science/paper/EVJPJEER

@misc{pith2026241114281,
  author       = {Pith},
  title        = {Pith review of: Q-CSM: Q-Learning-based Cognitive Service Management in Heterogeneous IoT Networks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/EVJPJEER}},
  note         = {Machine review of arXiv:2411.14281}
}

read the original abstract

The dramatic increase in the number of smart services and their diversity poses a significant challenge in Internet of Things (IoT) networks: heterogeneity. This causes significant quality of service (QoS) degradation in IoT networks. In addition, the constraints of IoT devices in terms of computational capability and energy resources add extra complexity to this. However, the current studies remain insufficient to solve this problem due to the lack of cognitive action recommendations. Therefore, we propose a Q-learning-based Cognitive Service Management framework called Q-CSM. In this framework, we first design an IoT Agent Manager to handle the heterogeneity in data formats. After that, we design a Q-learning-based recommendation engine to optimize the devices' lifetime according to the predicted QoS behaviour of the changing IoT network scenarios. We apply the proposed cognitive management to a smart city scenario consisting of three specific services: wind turbines, solar panels, and transportation systems. We note that our proposed cognitive method achieves 38.7% faster response time to the dynamical IoT changes in topology. Furthermore, the proposed framework achieves 19.8% longer lifetime on average for constrained IoT devices thanks to its Q-learning-based cognitive decision capability. In addition, we explore the most successive learning rate value in the Q-learning run through the exploration and exploitation phases.

Figures

Figures reproduced from arXiv: 2411.14281 by the authors.

**Figure 2.** Design of IoT Device Manager. • Environment, E: The smart city network consisting of n number of IoT sensors forms an environment for Q-learning. The smart city network consists of three services: wind turbines, solar panels and transportation. • Agent, Ag: Each service in the smart city network has a master node to behave as an agent. The agent is capable of exploring the simulated environment for learning phase. •… view at source ↗

**Figure 3.** Response time comparison of IoT Agent Manager with the increasing [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Figures from the paper (1 more)

**Figure 5.** Figure 5: Cumulative reward value of Q-learning with changing learning rates [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

21 extracted references · 19 canonical work pages

[9]

Protocol-adaptive strategies for wireless mesh smart city net- works,

L. Mamatas, V . Demiroglou, S. Kalafatidis, S. Skaperas, and V . Tsaous- sidis, “Protocol-adaptive strategies for wireless mesh smart city net- works,” IEEE Network , vol. 37, no. 2, pp. 136–143, 2023

work page 2023
[1]

Ai-empowered fog/edge resource management for iot applications: A comprehensive review, research challenges, and future perspectives,

G. K. Walia, M. Kumar, and S. S. Gill, “Ai-empowered fog/edge resource management for iot applications: A comprehensive review, research challenges, and future perspectives,” IEEE Communications Surveys & Tutorials , vol. 26, no. 1, pp. 619–669, 2024

work page 2024
[2]

Digital twin enriched green topology discovery for next generation core networks,

K. Duran and B. Canberk, “Digital twin enriched green topology discovery for next generation core networks,” IEEE Transactions on Green Communications and Networking , vol. 7, no. 4, pp. 1946–1956, 2023

1946
[3]

A link-quality anomaly detection framework for software-defined wireless mesh networks,

S. Skaperas, L. Mamatas, and V . Tsaoussidis, “A link-quality anomaly detection framework for software-defined wireless mesh networks,” IEEE Transactions on Machine Learning in Communications and Networking, vol. 2, pp. 495–510, 2024

work page 2024
[4]

Terminology for Constrained-Node Networks,

C. Bormann, M. Ersue, and A. Ker ¨anen, “Terminology for Constrained-Node Networks,” RFC 7228, May 2014. [Online]. Available: https://www.rfc-editor.org/info/rfc7228

work page 2014
[5]

6g-enabled dtaas (digital twin as a service) for decarbonized cities,

K. Duran, E. Ak, G. Yurdakul, and B. Canberk, “6g-enabled dtaas (digital twin as a service) for decarbonized cities,” in 2023 IEEE Inter- national Conference on Communications Workshops (ICC Workshops) , 2023, pp. 421–426

work page 2023
[6]

Chapter 1 - heterogeneous wireless sensor networks: Deployment strategies and coverage models,

E. Bozkaya, M. Karatas, and L. Eriskin, “Chapter 1 - heterogeneous wireless sensor networks: Deployment strategies and coverage models,” in Comprehensive Guide to Heterogeneous Networks , K. Ahuja, A. Nayyar, and K. Sharma, Eds. Academic Press, 2023, pp. 1–32. [Online]. Available: https://www.sciencedirect.com/science/ article/pii/B9780323905275000095

work page 2023
[7]

Ai-driven partial topology discovery algorithm for broadband networks,

K. Duran, B. Karanlik, and B. Canberk, “Ai-driven partial topology discovery algorithm for broadband networks,” in 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC) , 2021, pp. 1–6

work page 2021

Show all 21 references

[8]

Context-aware wireless connectivity and processing unit optimization for iot networks,

M. Ozturk, A. I. Abubakar, R. N. B. Rais, M. Jaber, S. Hussain, and M. A. Imran, “Context-aware wireless connectivity and processing unit optimization for iot networks,” IEEE Internet of Things Journal , vol. 9, no. 17, pp. 16 028–16 043, 2022

2022
[10]

Securing southbound interface in sdns: Utilizing support vector machines for openflow packet classification,

A. G. Avran, E. Ak, K. Duran, G. Yurdakul, and G. Sec ¸inti, “Securing southbound interface in sdns: Utilizing support vector machines for openflow packet classification,” in 2023 IEEE 28th International Work- shop on Computer Aided Modeling and Design of Communication Links a...

2023
[11]

Strategic bandwidth allocation for qos in iot gateway: Pre- dicting future needs based on iot device habits,

I. Chakour, C. Daoui, M. Baslam, B. Sainz-De-Abajo, and B. Garcia- Zapirain, “Strategic bandwidth allocation for qos in iot gateway: Pre- dicting future needs based on iot device habits,” IEEE Access , vol. 12, pp. 6590–6603, 2024

2024
[12]

Ai in energy digital twining: A reinforcement learning-based adaptive digital twin model for green cities,

L. V . Cakir, K. Duran, C. Thomson, M. Broadbent, and B. Canberk, “Ai in energy digital twining: A reinforcement learning-based adaptive digital twin model for green cities,” arXiv preprint arXiv:2401.16449 , 2024

2024 arXiv
[13]

Eflsm:- an intelligent resource manager for fog layer service management in smart cities,

K. H. K. Reddy, R. S. Goswami, A. K. Luhach, P. Chatterjee, M. Al- numay, and D. S. Roy, “Eflsm:- an intelligent resource manager for fog layer service management in smart cities,” IEEE Transactions on Consumer Electronics, vol. 70, no. 1, pp. 2281–2289, 2024

2024
[14]

Graph theoretical approach for automated ip lifecycle management in telco networks,

K. Duran, B. Karanlik, and B. Canberk, “Graph theoretical approach for automated ip lifecycle management in telco networks,” Wiley Int J Network Mgmt , vol. 31, no. 4, e2138, 2021

2021
[15]

Efficient com- putation offloading and resource allocation scheme for opportunistic access fog-cloud computing networks,

W.-B. Sun, J. Xie, X. Yang, L. Wang, and W.-X. Meng, “Efficient com- putation offloading and resource allocation scheme for opportunistic access fog-cloud computing networks,” IEEE Transactions on Cognitive Communications and Networking , vol. 9, no. 2, pp. 521–533, 2023

2023
[16]

5gt-gan: Enhancing data augmentation for 5g-enabled mobile edge computing in smart cities,

C. Pandey, V . Tiwari, A. L. Imoize, C.-T. Li, C.-C. Lee, and D. S. Roy, “5gt-gan: Enhancing data augmentation for 5g-enabled mobile edge computing in smart cities,” IEEE Access, vol. 11, pp. 120 983–120 996, 2023

2023
[17]

Personalized secure demand-oriented data service toward edge-cloud collaborative iot,

D. Wu, M. Sun, P. Zhang, Y . Tu, Z. Yang, and R. Wang, “Personalized secure demand-oriented data service toward edge-cloud collaborative iot,” IEEE Internet of Things Journal , vol. 10, no. 1, pp. 378–390, 2023

2023
[18]

A survey on evaluating the quality of auto- nomic internet of things applications,

K. Fizza, A. Banerjee, P. P. Jayaraman, N. Auluck, R. Ranjan, K. Mitra, and D. Georgakopoulos, “A survey on evaluating the quality of auto- nomic internet of things applications,” IEEE Communications Surveys & Tutorials, vol. 25, no. 1, pp. 567–590, 2023

2023
[19]

Age of twin (aot): A new digital twin qualifier for 6g ecosystem,

K. Duran, M. ¨Ozdem, T. Hoang, T. Q. Duong, and B. Canberk, “Age of twin (aot): A new digital twin qualifier for 6g ecosystem,” IEEE Internet of Things Magazine , vol. 6, no. 4, pp. 138–143, 2023

2023
[20]

T6conf: Digital twin networking framework for ipv6-enabled net-zero smart cities,

E. Ak, K. Duran, O. A. Dobre, T. Q. Duong, and B. Canberk, “T6conf: Digital twin networking framework for ipv6-enabled net-zero smart cities,” IEEE Communications Magazine , vol. 61, no. 3, pp. 36–42, 2023

2023
[21]

Fsc: Two-scale ai-driven fair sensitivity control for 802.11ax networks,

E. Ak and B. Canberk, “Fsc: Two-scale ai-driven fair sensitivity control for 802.11ax networks,” in GLOBECOM 2020 - 2020 IEEE Global Communications Conference, 2020, pp. 1–6

2020

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

[1] [9]

Protocol-adaptive strategies for wireless mesh smart city net- works,

L. Mamatas, V . Demiroglou, S. Kalafatidis, S. Skaperas, and V . Tsaous- sidis, “Protocol-adaptive strategies for wireless mesh smart city net- works,” IEEE Network , vol. 37, no. 2, pp. 136–143, 2023

work page 2023

[2] [1]

Ai-empowered fog/edge resource management for iot applications: A comprehensive review, research challenges, and future perspectives,

G. K. Walia, M. Kumar, and S. S. Gill, “Ai-empowered fog/edge resource management for iot applications: A comprehensive review, research challenges, and future perspectives,” IEEE Communications Surveys & Tutorials , vol. 26, no. 1, pp. 619–669, 2024

work page 2024

[3] [2]

Digital twin enriched green topology discovery for next generation core networks,

K. Duran and B. Canberk, “Digital twin enriched green topology discovery for next generation core networks,” IEEE Transactions on Green Communications and Networking , vol. 7, no. 4, pp. 1946–1956, 2023

1946

[4] [3]

A link-quality anomaly detection framework for software-defined wireless mesh networks,

S. Skaperas, L. Mamatas, and V . Tsaoussidis, “A link-quality anomaly detection framework for software-defined wireless mesh networks,” IEEE Transactions on Machine Learning in Communications and Networking, vol. 2, pp. 495–510, 2024

work page 2024

[5] [4]

Terminology for Constrained-Node Networks,

C. Bormann, M. Ersue, and A. Ker ¨anen, “Terminology for Constrained-Node Networks,” RFC 7228, May 2014. [Online]. Available: https://www.rfc-editor.org/info/rfc7228

work page 2014

[6] [5]

6g-enabled dtaas (digital twin as a service) for decarbonized cities,

K. Duran, E. Ak, G. Yurdakul, and B. Canberk, “6g-enabled dtaas (digital twin as a service) for decarbonized cities,” in 2023 IEEE Inter- national Conference on Communications Workshops (ICC Workshops) , 2023, pp. 421–426

work page 2023

[7] [6]

Chapter 1 - heterogeneous wireless sensor networks: Deployment strategies and coverage models,

E. Bozkaya, M. Karatas, and L. Eriskin, “Chapter 1 - heterogeneous wireless sensor networks: Deployment strategies and coverage models,” in Comprehensive Guide to Heterogeneous Networks , K. Ahuja, A. Nayyar, and K. Sharma, Eds. Academic Press, 2023, pp. 1–32. [Online]. Available: https://www.sciencedirect.com/science/ article/pii/B9780323905275000095

work page 2023

[8] [7]

Ai-driven partial topology discovery algorithm for broadband networks,

K. Duran, B. Karanlik, and B. Canberk, “Ai-driven partial topology discovery algorithm for broadband networks,” in 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC) , 2021, pp. 1–6

work page 2021