CoChat
Sign Up
Cited by 3Multi-Criteria Decision MakingAccess Control and TrustUAV Applications and Optimization

Hierarchical Analysis Process for Belief Management in Internet of Drones

Open access
Gharrad, Hana, Jabeur, Nafaâ, Yasar, Ansar Ul-Haque·2022·Sensors
DOI 10.3390/s22166146PMC PMC9412459
Open this paper in CoChat — free accountOpen PDF

Abstract

Group awareness is playing a major role in the efficiency of mission planning and decision-making processes, particularly those involving spatially distributed collaborative entities. The performance of this concept has remarkably increased with the advent of the Internet of Things (IoT). Indeed, a myriad of innovative devices are being extensively deployed to collaboratively recognize and track events, objects, and activities of interest. A wide range of IoT-based approaches have focused on representing and managing shared information through formal operators for group awareness. However, despite their proven results, these approaches are still refrained by the inaccuracy of information being shared between the collaborating distributed entities. In order to address this issue, we propose in this paper a new belief-management-based model for a collaborative Internet of Drones (IoD). The proposed model allows drones to decide the most appropriate operators to apply in order to manage the uncertainty of perceived or received information in different situations. This model uses Hierarchical Analysis Process (AHP) with Subjective Logic (SL) to represent and combine opinions of different sources. We focus on purely collaborative drone networks where the group awareness will also be provided as service to collaborating entities.

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 · 10.3390/s22166146

Cites

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?

  • The authors of [14,15,16,17] describe the use of RPASs to clean solar panels, define the need to purchase a drone, in maritime installations, and for belief management in internet technology, respectively, applications that all used the AHP method to optimize and prioritize processes, sometimes in combination with other MCDA techniques.

    “The authors of [14,15,16,17] describe the use of RPASs to clean solar panels, define the need to purchase a drone, in maritime installations, and for belief management in internet technology, respectively, applications that all used the AHP method to optimize and prioritize processes, sometimes in combination with other multi-criteria decision analysis (MCDA) techniques.”

    From Multicriteria Methodology for Prioritizing Predictive Maintenance Using RPASs (Drones) with Thermal Cameras on Transmission Lines (2025) · cites this paper as [17]

  • Publications [11,12,13,14,15,16,17,18,19,20] report the use of the AHP method to prioritize and optimize processes in several areas.

    “Publications [11,12,13,14,15,16,17,18,19,20] report the use of the AHP method to prioritize and optimize processes in several areas.”

    From Multicriteria Methodology for Prioritizing Predictive Maintenance Using RPASs (Drones) with Thermal Cameras on Transmission Lines (2025) · cites this paper as [17]

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

No references could be resolved yet.

Research works printed but not linked: 54

  1. [1]Jo T.H. Ma J.H. Cha S.H. Elderly Perception on the Internet of Things-Based Integrated Smart-Home System Sensors 2021 21 1284 10.3390/s21041284 33670237 PMC7916975 · DOI
  2. [2]Zhang P. Zhu H. Zhou Y. Modeling cooperative driving strategies of automated vehicles considering trucks’ behavior Phys. A Stat. Mech. Its Appl. 2022 585 126386 10.1016/j.physa.2021.126386 · DOI
  3. [3]Alsamhi S.H. Ma O. Ansari M.S. Almalki F.A. Survey on Collaborative Smart Drones and Internet of Things for Improving Smartness of Smart Cities IEEE Access 2019 7 128125 128152 10.1109/ACCESS.2019.2934998 · DOI
  4. [4]Tian D. Zhang C. Duan X. Wang X. An Automatic Car Accident Detection Method Based on Cooperative Vehicle Infrastructure Systems IEEE Access 2019 7 127453 127463 10.1109/ACCESS.2019.2939532 · DOI
  5. [5]Gharrad H. Jabeur N. Yasar A.U.H. Galland S. Mbarki M. A five-step drone collaborative planning approach for the management of distributed spatial events and vehicle notification using multi-agent systems and firefly algorithms Comput. Netw. 2021 198 108282 10.1016/j.comnet.2021.108282 · DOI
  6. [6]Jøsang A. Subjective Logic Springer Berlin/Heidelberg, Germany 2009 Volume 171
  7. [7]Dong S. Ma M. Feng L. A smart city simulation platform with uncertainty Assoc. Comput. Mach. 2021 1 229 230 10.1145/3450267.3452002 · DOI
  8. [8]Jöckel L. Iese F. Kläs M. Iese F. Model-Based Engineering of Collaborative Embedded Systems: Extensions of the Spes Methodology Springer Nature Berlin/Heidelberg, Germany 2021 10.1007/978-3-030-62136-0 · DOI
  9. [9]Li Y. Chen J. Feng L. Dealing with uncertainty: A survey of theories and practices IEEE Trans. Knowl. Data Eng. 2013 25 2463 2482 10.1109/TKDE.2012.179 · DOI
  10. [10]Machot F.A. A Robust Event Detection under Uncertainty in Video/Audio Surveillance Systems, Alpen-Adria-Universitat Klagenfurt. Faculty of Technical Sciences Austria 2013 Available online: https://www.researchgate.net/profile/Fadi-Al-Machot/publication/303544127_A_Robust_Event_Detection_under_Uncertainty_in_VideoAudio_Surveillance_Systems/links/57478ba108ae2301b0b80694/A-Robust-Event-Detection-under-Uncertainty-in-Video-Audio-Surveillance-Systems.pdf (accessed on 27 June 2022)
  11. [11]Benferhat S. Leray P. Tabia K. Belief Graphical Models for Uncertainty Representation and Reasoning A Guided Tour of Artificial Intelligence Research Springer Cham, Switzerland 2020 209 246 10.1007/978-3-030-06167-8_8 · DOI
  12. [12]Weller A. Methods for Inference in Graphical Models. Doctor of Philosophy in the Graduate School of Arts and Sciences Ph.D. Thesis Columbia University New York, NY, USA 2014
  13. [13]Doherty P. Dunin-Kȩplicz B. Szałas A. Dynamics of approximate information fusion International Conference on Rough Sets and Intelligent Systems Paradigms Springer Berlin/Heidelberg, Germany 2007 Volume 4585 668 677 10.1007/978-3-540-73451-2_70 · DOI
  14. [14]Deng X. Jiang W. Exploring the combination rules of D numbers from a perspective of conflict redistribution Proceedings of the 2017 IEEE 20th International Conference on Information Fusion Xi’an, China 10–13 July 2017 10.23919/ICIF.2017.8009696 · DOI
  15. [15]Almond R.G. Graphical Belief Modeling CRC Press Boca Raton, FL, USA 1995
  16. [16]Ferson S. Sentz K. Epistemic Uncertainty in Agent-Based Modeling Proceedings of the 7th International Workshop on Reliable Engineering Computing Bochum, Germany 15–17 June 2016 65 82
  17. [17]Kaplan D. On the Quantification of Model Uncertainty: A Bayesian Perspective Psychometrika 2021 86 215 238 10.1007/s11336-021-09754-5 33721184 PMC7958145 · DOI
  18. [18]Sahlin U. Helle I. Perepolkin D. This Is What We Don’ t Know’: Treating Epistemic Uncertainty in Bayesian Networks for Risk Assessment Integr. Environ. Assess. Manag. 2021 17 221 232 10.1002/ieam.4367 33151017 PMC7839433 · DOI
  19. [19]Ni S. Lei Y. Tang Y. Improved base belief function-based conflict data fusion approach considering belief entropy in the evidence theory Entropy 2020 22 801 10.3390/e22080801 PMC7517373 33286572 · DOI
  20. [20]Tang Y. Zhou D. Xu S. He Z. A weighted belief entropy-based uncertainty measure for multi-sensor data fusion Sensors 2017 17 928 10.3390/s17040928 28441736 PMC5426924 · DOI
  21. [21]Wang D. Gao J. Wei D. A new belief entropy based on deng entropy Entropy 2019 21 987 10.3390/e21100987 · DOI
  22. [22]Zhou D. Tang Y. Jiang W. A modified belief entropy in Dempster-Shafer framework PLoS ONE 2017 12 e0176832 10.1371/journal.pone.0176832 28481914 PMC5421761 · DOI
  23. [23]Xiao F. A Multiple-Criteria Decision-Making Method Based on D Numbers and Belief Entropy Int. J. Fuzzy Syst 2019 21 1144 1153 10.1007/s40815-019-00620-2 · DOI
  24. [24]Liu W. Williams M. A Framework for Multi Agent Belief Revision Springer Studia Log. Int. J. Symb. Logic. 2016 67 291 312
  25. [25]Fagin R. Hapern J.Y. Moses Y. Vardi M. Reasining about Knowledge The MIT Press London, UK 1995
  26. [26]Andreas H. Belief Revision Dynamic Tractable Reasoning Springer Cham, Switzerland 2020 49 65
  27. [27]Liu J. Tang Y. Conflict data fusion in a multi-agent system premised on the base basic probability assignment and evidence distance Entropy 2021 23 820 10.3390/e23070820 34203135 PMC8308205 · DOI
  28. [28]Jiang W. Zhan J. A modified combination rule in generalized evidence theory Appl. Intell. 2017 46 630 640 10.1007/s10489-016-0851-6 · DOI
  29. [29]Tacnet J.-M. Batton-Hubert M. Dezert J. A two-step fusion process for multi-criteria decision applied to natural hazards in mountains Proceedings of the Workshop on the Theory of Belief Functions Brest, France 1–2 April 2010 10.5281/zenodo.32206 · DOI
  30. [30]Dunin-Keplicz B. Verbrugge R. Teamwork in Multi-Agent Systems John Wiley & Sons Hoboken, NJ, USA 2011 Volume 22
  31. [31]Loke S.W. Intelligent Information Agents Springer Berlin/Heidelberg, Germany 2001 Volume 2112 10.1007/3-540-45408-x_20 · DOI
  32. [32]Singh R. Sonenberg L. Miller T. Communication and Shared Mental Models for Teams Performing Interdependent Tasks Springer Coordination, Organizations, Institutions, and Norms in Agent Systems XII Singapore 2017 Volume 10315 81 97 10.1007/978-3-319-46882-2_10 · DOI
  33. [33]Clark H.H. Marshall C.R. Definite knowledge and mutual knowledge Elements of Discourse Understanding Cambridge University Press Cambridge, UK 1981 10 63
  34. [34]Niler A.A. Mesmer-Magnus J.R. Larson L.E. Plummer G. DeChurch L.A. Contractor N.S. Conditioning team cognition: A meta-analysis Organ. Psychol. Rev. 2021 11 144 174 10.1177/2041386620972112 · DOI
  35. [36]Scheutz M. DeLoach S.A. Adams J.A. A Framework for Developing and Using Shared Mental Models in Human-Agent Teams J. Cogn. Eng. Decis. Mak. 2017 11 203 224 10.1177/1555343416682891 · DOI
  36. [37]Gervits F. Thurston D. Thielstrom R. Fong T. Pham Q. Scheutz M. Toward genuine robot teammates: Improving human-robot team performance using robot shared mental models Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems London, UK 3–7 May 2021 429 437
  37. [38]Sarkadi S. Panisson A.R. Bordini R.H. McBurney P. Towards an Approach for Modelling Uncertain Theory of Mind in Multi-Agent Systems Proceedings of the 6th International Conference on Agreement Technologies Bergen, Norway 6–7 December 2018
  38. [39]Wang Y. Li H. Qian L. Belief Propagation and Quickest Detection-Based Cooperative Spectrum Sensing in Heterogeneous and Dynamic Environments IEEE Trans. Wirel. Commun. 2017 16 7446 7459 10.1109/TWC.2017.2748589 · DOI
  39. [40]Li Y. Wang Y. Guan X. Belief propagation based multi-AUV cooperative localization in anchor-free environments Proceedings of the 4th Underwater Communications and Networking Conference Lerici, Italy 28–30 August 2018 1 5 10.1109/UComms.2018.8493170 · DOI
  40. [41]Fan X. Shi Z. Loopy belief propagation algorithm in distributed wireless cooperative spectrum sensing Proceedings of the 2011 3rd International Conference on Communications and Mobile Computing Qingdao, China 18–20 April 2011 282 285 10.1109/CMC.2011.103 · DOI
  41. [42]Kim H. Choi S.W. Kim S. Connectivity information-aided belief propagation for cooperative localization IEEE Wirel. Commun. Lett. 2018 7 1010 1013 10.1109/LWC.2018.2844785 · DOI
  42. [43]Chen H. Xian-Bo W. Liu J. Wang J. Ye W. Collaborative Multiple UAVs Navigation with GPS/INS/UWB Jammers Using Sigma Point Belief Propagation IEEE Access 2020 8 193695 193707 10.1109/ACCESS.2020.3031605 · DOI
  43. [44]Jung S. Yang P. Quek T.Q.S. Kim J.H. Belief Propagation based Scheduling for Energy Efficient Multi-drone Monitoring System Proceedings of the 2020 International Conference on Information and Communication Technology Convergence Jeju Island, Korea 21–23 October 2020 261 263 10.1109/ICTC49870.2020.9289157 · DOI
  44. [45]Li J. Yang G. Cai Q. Niu H. Li J. Cooperative navigation for UAVs in GNSS-denied area based on optimized belief propagation Meas. J. Int. Meas. Confed. 2022 192 110797 10.1016/j.measurement.2022.110797 · DOI
  45. [46]Zhilenkov A.A. Epifantsev I.R. The Use of Convolution Artificial Neural Networks for Drones Autonomous Trajectory Planning Proceedings of the IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus) Moscow, Russia St. Petersburg, Russia 29 January–1 February 2018 1044 1047
  46. [47]Salh A. Audah L. Alhartomi M.A. Kim K.S. Alsamhi S.H. Almalki F.A. Abdullah Q. Saif A. Algethami H. Smart Packet Transmission Scheduling in Cognitive IoT Systems: DDQN Based Approach IEEE Access 2022 10 50023 50036 10.1109/ACCESS.2022.3168549 · DOI
  47. [48]Salh A. Audah L. Kim K.S. Alsamhi S.H. Alhartomi M.A. Almalki F.A. Algethami H. Refiner GAN Algorithmically Enabled Deep-RL for Guaranteed Traffic Packets in Real-Time URLLC B5G Communication Systems IEEE Access 2022 10 50662 50676 10.1109/ACCESS.2022.3170447 · DOI
  48. [50]Altieri M.G. Dell’Orco M. Marinelli M. Sinesi S. Evidence (Dempster—Shafer) Theory-Based evaluation of different Transport Modes under Uncertainty.: Theoretical basis and first findings Transp. Res. Procedia 2017 27 508 515 10.1016/j.trpro.2017.12.117 · DOI
  49. [51]Tacnet J.M. Dezert J. New Belief Function Based Methods for Multi-Criteria Decision-Making Euro Working Group—Decision Support Systems Workshop Paris, France 2011 Volume 121 14 10.1007/978-3-642-32191-7_8 · DOI
  50. [52]Fei L. Xia J. Feng Y. Liu L. An ELECTRE-Based Multiple Criteria Decision-Making Method for Supplier Selection Using Dempster-Shafer Theory IEEE Access 2019 7 84701 84716 10.1109/ACCESS.2019.2924945 · DOI
  51. [53]Ganguly K. Integration of analytic hierarchy process and Dempster-Shafer theory for supplier performance measurement considering risk Int. J. Product. Perform. Manag. 2014 63 85 102 10.1108/IJPPM-10-2012-0117 · DOI
  52. [54]Wedley W.C. Consistency Prediction for Incomplete AHP Matrices Mathl. Comput. Model. 1993 17 151 161 10.1016/0895-7177(93)90183-Y · DOI
  53. [55]Lu X. Ma H. Wang Z. Analysis of OODA Loop based on Adversarial for Complex Game Environments. March 2022 Available online: http://arxiv.org/abs/2203 (accessed on 29 June 2022)
  54. [56]Yang Q. Parasuraman R. A General Cooperative Multi-Agent Hierarchical Decision Architecture in Adversarial Environments arXiv 2020 2009.00288v2

Cited by

Sorted by citations

Authors
Gharrad, H., Jabeur, N., & Yasar, A. U.-H.
Affiliations
  • Hasselt University (BE)
  • Computer Sciences Department, German University of Technology in Oman (GUtech), Athaibah, Muscat 130, Oman
ORCID
Venue
Sensors
Publisher
MDPI AG
Published
2022-08-17
Volume
22
Issue
16
Pages
6146
ISSN
1424-8220 (ISSN-L)
DOI
10.3390/s22166146
PMC
PMC9412459
OpenAlex
W4293514222
Cited by
3
References
0
Citations by year
2026: 1 · 2025: 2
FWCI
0.31
Type
journal article
Language
en
Open access
Licence
Locations
  • Sensors (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Document Server@UHasselt (UHasselt) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
UHasselt Bilateral Scientific Cooperation BOF BILA (BOF21BL10)
Topics
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Multi-Criteria Decision Making
  • Social Sciences › Sociology and Political Science › Access Control and Trust
  • Physical Sciences › Engineering › Aerospace Engineering › UAV Applications and Optimization
Keywords
Internet of Drones, group awareness, belief management, analytic hierarchy process, subjective logic, uncertainty management, data fusion, multi-agent decision making, Internet of Things
MeSH terms
Unmanned Aerial Devices, Data Collection, Internet

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