CoChat
Sign Up
Cited by 6Distributed Sensor Networks and Detection AlgorithmsDistributed Control Multi-Agent SystemsAdvanced Bandit Algorithms Research

Conformal Distributed Remote Inference in Sensor Networks Under Reliability and Communication Constraints

Open access
Zhu, Meiyi, Zecchin, Matteo, Park, Sangwoo, Guo, Caili, Feng, Chunyan, Popovski, Petar, Simeone, Osvaldo·2025·IEEE Transactions on Signal Processing
DOI 10.1109/tsp.2025.3549222
Open this paper in CoChat — free accountOpen PDF

Abstract

This paper presents communication-constrained distributed conformal risk control (CD-CRC) framework, a novel decision-making framework for sensor networks under communication constraints. Targeting multi-label classification problems, such as segmentation, CD-CRC dynamically adjusts local and global thresholds used to identify significant labels with the goal of ensuring a target false negative rate (FNR), while adhering to communication capacity limits. CD-CRC builds on online exponentiated gradient descent to estimate the relative quality of the observations of different sensors, and on online conformal risk control (CRC) as a mechanism to control local and global thresholds. CD-CRC is proved to offer deterministic worst-case performance guarantees in terms of FNR and communication overhead, while the regret performance in terms of false positive rate (FPR) is characterized as a function of the key hyperparameters. Simulation results highlight the effectiveness of CD-CRC, particularly in communication resource-constrained environments, making it a valuable tool for enhancing the performance and reliability of distributed sensor networks.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1109/tsp.2025.3549222

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Miscalibration can lead to catastrophic failures, such as missed cancer diagnoses in medical imaging or false negatives in anomaly detection systems.

    “This limitation can lead to catastrophic failures, such as missed cancer diagnoses in medical imaging [5] or false negatives in anomaly detection systems [6].”

    From Optimized Certainty Equivalent Risk-Controlling Prediction Sets (2026) · cites this paper as [6]

    The Design Problem section defines false negatives as relevant labels incorrectly classified as irrelevant and discusses missed target pixels in segmentation. It does not address anomaly detection systems or say that miscalibration causes catastrophic failures. In the citing sentence, the citation to this paper is attached to the anomaly-detection example, not the medical-imaging example; page numbers were not available in our copy. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 45

  1. T. M. Behera, S. K. Mohapatra, U. C. Samal, M. S. Khan, M. Danesh- mand, and A. H. Gandomi, "I-SEP: An improved routing protocol for heterogeneous WSN for IoT-based environmental monitoring," IEEE Internet Things J., vol. 7, no. 1, pp. 710-717, Jan. 2020.
  2. K. Muhammad, R. Hamza, J. Ahmad, J. Lloret, H. Wang, and S. W. Baik, "Secure surveillance framework for IoT systems using probabilis- tic image encryption," IEEE Trans. Ind. Informat., vol. 14, no. 8, pp. 3679-3689, Jan. 2018.
  3. G. Sun, Y. Liu, Z. Chen, A. Wang, Y. Zhang, D. Tian, and V. C. Leung, "Energy efficient collaborative beamforming for reducing sidelobe in wireless sensor networks," IEEE Trans. Mob. Comput., vol. 20, no. 3, pp. 965-982, Nov. 2019.
  4. S. Gong, S. Ma, C. Xing, and G. Yang, "Optimal beamforming and time allocation for partially wireless powered sensor networks with downlink SWIPT," IEEE Trans. Signal Process., vol. 67, no. 12, pp. 3197-3212, Apr. 2019.
  5. J. H. Hansen, C. Busso, Y. Zheng, and A. Sathyanarayana, "Driver modeling for detection and assessment of driver distraction: Examples from the UTDrive test bed," IEEE Signal Process. Mag., vol. 34, no. 4, pp. 130-142, Jul. 2017.
  6. B. C. Tedeschini, M. Brambilla, L. Barbieri, G. Balducci, and M. Nicoli, "Cooperative lidar sensing for pedestrian detection: Data association based on message passing neural networks," IEEE Trans. Signal Pro- cess., vol. 71, pp. 3028-3042, Aug. 2023.
  7. K. Kamtue, J. M. Moura, O. Sangpetch, and P. Garcia, "PhyOT: Physics- informed object tracking in surveillance cameras," in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., Apr. 2024, pp. 7030-7034.
  8. Y. Wang, Z. Jiang, Y. Li, J.-N. Hwang, G. Xing, and H. Liu, "RODNet: A real-time radar object detection network cross-supervised by camera- radar fused object 3D localization," IEEE J. Sel. Top. Signal Process., vol. 15, no. 4, pp. 954-967, Feb. 2021.
  9. Z. Mao, H. Su, B. He, and X. Jing, "Moving source localization in passive sensor network with location uncertainty," IEEE Signal Process. Lett., vol. 28, pp. 823-827, Apr. 2021.
  10. D. L. Hall and J. Llinas, "An introduction to multisensor data fusion," Proc. of the IEEE, vol. 85, no. 1, pp. 6-23, Aug. 1997.
  11. K. Rammelkamp, S. Schröder, S. Kubitza, D. S. Vogt, S. Frohmann, P. B. Hansen, U. Böttger, F. Hanke, and H.-W. Hübers, "Low-level LIBS and Raman data fusion in the context of in situ Mars exploration," Journal of Raman Spectroscopy, vol. 51, no. 9, pp. 1682-1701, Sept. 2020.
  12. J. Wang, Y. Zeng, S. Wei, Z. Wei, Q. Wu, and Y. Savaria, "Multi- sensor track-to-track association and spatial registration algorithm under incomplete measurements," IEEE Trans. Signal Process., vol. 69, pp. 3337-3350, May. 2021.
  13. A. K. Gostar, T. Rathnayake, R. Tennakoon, A. Bab-Hadiashar, G. Bat- tistelli, L. Chisci, and R. Hoseinnezhad, "Centralized cooperative sensor fusion for dynamic sensor network with limited field-of-view via labeled multi-Bernoulli filter," IEEE Trans. Signal Process., vol. 69, pp. 878- 891, Dec. 2020.
  14. N. Katenka, E. Levina, and G. Michailidis, "Local vote decision fusion for target detection in wireless sensor networks," IEEE Trans. Signal Process., vol. 56, no. 1, pp. 329-338, Dec. 2007.
  15. R. Niu, B. Chen, and P. K. Varshney, "Fusion of decisions transmitted over rayleigh fading channels in wireless sensor networks," IEEE Trans. Signal Process., vol. 54, no. 3, pp. 1018-1027, Mar. 2006.
  16. A. Sharma and S. Chauhan, "Sensor fusion for distributed detection of mobile intruders in surveillance wireless sensor networks," IEEE Sens. J., vol. 20, no. 24, pp. 15 224-15 231, Jul. 2020.
  17. R. Ravindran, M. J. Santora, and M. M. Jamali, "Camera, LiDAR, and radar sensor fusion based on Bayesian neural network (CLR-BNN)," IEEE Sens. J., vol. 22, no. 7, pp. 6964-6974, Feb. 2022.
  18. H. Su, S. Maji, E. Kalogerakis, and E. Learned-Miller, "Multi-view convolutional neural networks for 3D shape recognition," in Proc. IEEE Int. Conf. Comput. Vis., Dec. 2015, pp. 945-953.
  19. Z. Liu, C. Guo, and M. Zhu, "Semantic redundancy-aware multi-view edge inference based on rate-distortion in IoT systems," in IEEE Wireless Communications and Networking Conference (WCNC), Apr. 2024, pp. 1-6.
  20. Q. Lan, Q. Zeng, P. Popovski, D. Gündüz, and K. Huang, "Progressive feature transmission for split classification at the wireless edge," IEEE Trans. Wireless Commun., vol. 22, no. 6, pp. 3837-3852, Nov. 2022.
  21. M. Gasparin and A. Ramdas, "Conformal online model aggregation," arXiv preprint arXiv:2403.15527, May. 2024. · arXiv
  22. I. Gibbs and E. Candes, "Adaptive conformal inference under distribu- tion shift," Proc. Adv. Neural Inf. Process. Syst., vol. 34, pp. 1660-1672, Dec. 2021.
  23. S. De Rooij, T. Van Erven, P. D. Grünwald, and W. M. Koolen, "Follow the leader if you can, hedge if you must," J. Mach. Learn. Res., vol. 15, no. 1, pp. 1281-1316, Apr. 2014.
  24. V. Vovk, A. Gammerman, and G. Shafer, Algorithmic learning in a random world. Springer, 2005, vol. 29.
  25. R. F. Barber, E. J. Candes, A. Ramdas, and R. J. Tibshirani, "Predictive inference with the jackknife+," Ann. Statist., vol. 49, no. 1, pp. 486-507, Feb. 2021.
  26. P. Humbert, B. Le Bars, A. Bellet, and S. Arlot, "One-shot federated conformal prediction," in Proc. Int. Conf. Mach. Learn., Jul. 2023, pp. 14 153-14 177.
  27. M. Zhu, M. Zecchin, S. Park, C. Guo, C. Feng, and O. Simeone, "Fed- erated inference with reliable uncertainty quantification over wireless channels via conformal prediction," IEEE Trans. Signal Process., Jan. 2024.
  28. A. Bhatnagar, H. Wang, C. Xiong, and Y. Bai, "Improved online conformal prediction via strongly adaptive online learning," in Proc. Int. Conf. Mach. Learn., Jul. 2023, pp. 2337-2363.
  29. A. N. Angelopoulos, R. F. Barber, and S. Bates, "Online conformal prediction with decaying step sizes," arXiv preprint arXiv:2402.01139, May. 2024. · arXiv
  30. I. Gibbs and E. J. Candès, "Conformal inference for online prediction with arbitrary distribution shifts," J. Mach. Learn. Res., vol. 25, no. 162, pp. 1-36, May. 2024.
  31. A. N. Angelopoulos, S. Bates, A. Fisch, L. Lei, and T. Schuster, "Conformal risk control," arXiv preprint arXiv:2208.02814, Apr. 2023. · arXiv
  32. K. M. Cohen, S. Park, O. Simeone, and S. S. Shitz, "Cross-validation conformal risk control," in Proc. IEEE Int. Symp. Inf. Theory. (ISIT), Jul. 2024, pp. 250-255.
  33. M. Zecchin, F. Hellström, S. Park, S. Shamai, and O. Simeone, "Gener- alization and informativeness of weighted conformal risk control under covariate shift," arXiv preprint arXiv:2501.11413, Jan. 2025. · arXiv
  34. S. Feldman, L. Ringel, S. Bates, and Y. Romano, "Achieving risk control in online learning settings," arXiv preprint arXiv:2205.09095, Jan. 2023. · arXiv
  35. A. Angelopoulos, E. Candes, and R. J. Tibshirani, "Conformal PID control for time series prediction," Proc. Adv. Neural Inf. Process. Syst., vol. 36, Dec. 2023.
  36. T. Feng, L. Xie, J. Yao, and J. Xu, "UAV-enabled data collection for wireless sensor networks with distributed beamforming," IEEE Trans. Wireless Commun., vol. 21, no. 2, pp. 1347-1361, Aug. 2021.
  37. K. Rezaee, S. M. Rezakhani, M. R. Khosravi, and M. K. Moghimi, "A survey on deep learning-based real-time crowd anomaly detection for secure distributed video surveillance," Personal and Ubiquitous Computing, vol. 28, no. 1, pp. 135-151, 2024.
  38. M. Gasparin and A. Ramdas, "Merging uncertainty sets via majority vote," arXiv preprint arXiv:2401.09379, Mar. 2024. · arXiv
  39. F. Orabona, "A modern introduction to online learning," arXiv preprint arXiv:1912.13213, May. 2023. · arXiv
  40. V. Vovk, "Competitive on-line statistics," International Statistical Re- view, vol. 69, no. 2, pp. 213-248, Aug. 2001.
  41. N. Cesa-Bianchi and G. Lugosi, Prediction, learning, and games. Cambridge university press, Mar. 2006.
  42. M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisser- man, "The PASCAL visual object classes (VOC) challenge," Interna- tional journal of computer vision, vol. 88, pp. 303-338, 2010.
  43. J. Long, E. Shelhamer, and T. Darrell, "Fully convolutional networks for semantic segmentation," in Proc. IEEE Conf. Comput. Vis. Pattern Recog. (CVPR), Jun. 2015, pp. 3431-3440.
  44. I. Kontoyiannis and S. Verdú, "Optimal lossless data compression: Non- asymptotics and asymptotics," IEEE Trans. Inf. Theory., vol. 60, no. 2, pp. 777-795, Nov. 2013.
  45. S. Shalev-Shwartz et al., "Online learning and online convex optimiza- tion," Foundations and Trends® in Machine Learning, vol. 4, no. 2, pp. 107-194, 2012.

Cited by

Sorted by citations

Authors
Zhu, M., Zecchin, M., Park, S., Guo, C., Feng, C., Popovski, P., & Simeone, O.
Affiliations
  • Beijing University of Posts and Telecommunications (CN)
  • King's College London (GB)
  • Aalborg University (DK)
ORCID
Venue
IEEE Transactions on Signal Processing
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2025
Volume
73
Pages
1485-1500
ISSN
  • 1053-587X (ISSN-L)
  • 1941-0476
DOI
10.1109/tsp.2025.3549222
OpenAlex
W4408235809
Cited by
6
References
1
Citations by year
2026: 3 · 2025: 3
FWCI
2.54
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Transactions on Signal Processing (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • VBN Forskningsportal (Aalborg Universitet) (submittedVersion, other-oa)
  • VBN Forskningsportal (Aalborg Universitet) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • King's College London
  • European Commission (101096379)
  • National Natural Science Foundation of China (61871047, 62371070)
  • Aalborg Universitet
  • Beijing University of Posts and Telecommunications (100876)
  • Beijing Key Laboratory of Network System Architecture and Convergence
  • HORIZON EUROPE Framework Programme (101096379)
  • Engineering and Physical Sciences Research Council (EP/W024101/1, EP/X011852/1)
  • Menzies Centre for Australian Studies, King's College London, University of London
Topics
  • Physical Sciences › Computer Science › Computer Networks and Communications › Distributed Sensor Networks and Detection Algorithms
  • Physical Sciences › Computer Science › Computer Networks and Communications › Distributed Control Multi-Agent Systems
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Advanced Bandit Algorithms Research
Keywords
conformal risk control, sensor networks, communication constraints, multi-label classification, false negative rate, threshold control, regret analysis, communication overhead

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review