IP Library › Granted Patent US 12,520,160
Granted Patent B2
US 12,520,160 · App. 18/231,923 · Granted Jan 6, 2026

Reputation-based trust determination method

Inventors: Ioanna Kapetanidou (Blagnac, FR); Paulo-Jorge Milheiro Mendes (Blagnac, FR); Vassilis Tsaoussidis (Blagnac, FR)
Assignee: Airbus S.A.S.
H04W12/66
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,520,160
App. No.
18/231,923
Granted
Jan 6, 2026
Kind
B2
Abstract

A method for determining, onboard a first mobile entity, a level of trust for a second mobile entity in communication with the first mobile entity, and a communication system implementing the method are described. The method includes determining), by the first mobile entity, direct trust data, calculating an initial reputation indicator based on the direct trust data, including a belief value, a disbelief value, an uncertainty value, and a base trust rate, calculating a confidence level based on the belief value, the base trust rate, and the uncertainty value, and determining an updated reputation indicator based on the initial reputation indicator and the confidence level. The base trust rate is indicative of the trust level for data transmitted from an unknown mobile entity not in contact with the first mobile entity. The updated reputation indicator indicates a level of trust of the first mobile entity in the second mobile entity.

Claims (31)

1 . A method for determining, onboard a first mobile entity, a level of trust for a second mobile entity in communication with the first mobile entity, the method comprising:

determining, by the first mobile entity, direct trust data based on a number of positive interactions and negative interactions between the second mobile entity and the first mobile entity;

calculating, by the first mobile entity, an initial reputation indicator based on the direct trust data, wherein the initial reputation indicator includes a belief value, a disbelief value, an uncertainty value, and a base trust rate;

calculating, by the first mobile entity, a confidence level based on the belief value, the base trust rate, and the uncertainty value; and

determining an updated reputation indicator based on the initial reputation indicator and the confidence level;

wherein the base trust rate is indicative of the trust level for data transmitted from an unknown mobile entity that has not been in contact with the first mobile entity in the past;

wherein a positive interaction is an interaction where the data received from the second mobile entity match defined quality criteria;

wherein a negative interaction is an interaction where the data received from the second mobile entity do not match the quality criteria; and

wherein the updated reputation indicator indicates a level of trust of the first mobile entity in the second mobile entity.

2 . The method of claim 1 , wherein the step of calculating the initial reputation indicator further includes calculating the initial reputation indicator based on a summation of indirect trust data with the direct trust data;

wherein the indirect trust data corresponds to a number of positive recommendations and negative recommendations relating to the second mobile entity and received from at least one third mobile entity.

3 . The method of claim 2 , wherein the positive recommendations and the negative recommendations correspond to positive interactions and negative interactions, respectively, that have been determined by the at least one third mobile entity that has interacted with the second mobile entity in the past.

4 . The method of claim 2 , further comprising periodically broadcasting, by the first mobile entity, an interest packet to fetch indirect trust data from the at least one third mobile entity.

5 . The method of claim 1 , wherein the step of determining the updated reputation indicator further includes applying an exponentially weighted moving average function (EWMA) on the initial reputation indicator and the confidence level.

6 . The method of claim 1 , wherein the communication between the mobile entities is based on named data networking (NDN).

7 . The method of claim 1 , wherein the step of determining the updated reputation indicator further comprises including a global reputation score (GRS) received from a ground station.

8 . The method of claim 7 , wherein the GRS is a reputation score determined by the ground station based on trust data received from mobile entities in communication range to the ground station.

9 . The method of claim 7 , wherein the GRS predominates the reputation indicator if the GRS and the updated reputation indicator determined onboard the first mobile entity without the GRS do not match.

10 . The method of claim 7 , further comprising periodically checking for ground stations in communication range with the first mobile entity and, if a ground station is in communication range, broadcasting an interest packet to fetch a global reputation score from said ground station.

11 . The method of claim 1 , wherein each of the mobile entities is an aerial vehicle.

12 . A communication system onboard a first mobile entity, the communication system comprising:

a transceiver, configured to send and receive data messages wirelessly; and

a controller, configured to determine a level of trust for a second mobile entity in communication with the first mobile entity by:

determining direct trust data based on a number of positive interactions and negative interactions between the second mobile entity and the first mobile entity;

calculating an initial reputation indicator based on the direct trust data, wherein the initial reputation indicator includes a belief value, a disbelief value, an uncertainty value, and base trust rate;

calculating a confidence level based on the belief value, the base trust value, and the uncertainty value; and

determining an updated reputation indicator based on the initial reputation indicator and the confidence level;

wherein the base trust rate is indicative of the trust level for data transmitted from an unknown mobile entity that has not been in contact with the first mobile entity in the past;

wherein a positive interaction is an interaction where the data received from the second mobile entity match defined quality criteria;

wherein a negative interaction is an interaction where the data received from the second mobile entity do not match the quality criteria; and

wherein the updated reputation indicator indicates a level of trust of the first mobile entity in the second mobile entity.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 26, 2023
From: KAPETANIDOU, IOANNA; MENDES, PAULO-JORGE MILHEIRO; TSAOUSSIDIS, VASSILIS
To: AIRBUS S.A.S.
Reel/Frame 065362/0579 →
Priority Claims (1)
EP 22386057 · Aug 18, 2022 · regional
Continuity (1)
Related Publication 20240064522A1 · Feb 22, 2024
References Cited (24)
US 11159644B2 · Neishaboori · 2021 [cited by examiner]
US 11337071B2 · Ferreira · 2022 [cited by examiner]
US 20130258878A1 · Wakikawa · 2013 [cited by examiner]
US 20190020657A1 · Egner · 2019 [cited by examiner]
US 20200177595A1 · Rakshit · 2020 [cited by examiner]
CN 114519149 · 2022 [cited by examiner]
CN 115460255 · 2022 [cited by examiner]
CN 116976468 · 2023 [cited by examiner]
EP 2743726A1 · 2014 [cited by examiner]
Benfriha et al.; “Insiders Detection in the Uncertain IoD using Fuzzy Logic”, 2022, IEEE, pp. 1-6. (Year: 2022). [cited by examiner]
Zhao et al.; “An Effective Exponential-Based Trust and Reputation Evaluation System in Wireless Sensor Networks”, 2019, IEEEAccess, pp. 33859-33869. (Year: 2019). [cited by examiner]
Hasrouny et al.; “Trust model for secure group leader-based communications in VANET”, 2018, Springer, pp. 4639-4661. (Year: 2018). [cited by examiner]
Barka et al.; “A Trusted Lightweight Communication Strategy for Flying Named Data Networking”, Aug. 2018, www.mdpi.com/journal/sensors, pp. 1-18. (Year: 2018). [cited by examiner]
Shabut et al.; “Friendship Based Trust Model to secure routing protocols in Mobile Ad hoc Networks”, 2014 IEEE, pp. 280-287. (Year: 2014). [cited by examiner]
Shabut et al.; “Enhancing Dynamic Recommender Selection Using Multiple Rules for Trust and Reputation Models in MANETs”, 2013, Proceedings of IEEE International Conference on Tools with Artificial Intelligence (ICTAI), … [cited by examiner]
Yan et al.; “AdChatRep: A Reputation System for MANET Chatting”, Sep. 2011, SCI'11, pp. 43-48. (Year: 2011). [cited by examiner]
Lahbib et al.; “Trust Based Routing Metric for RPL Routing Protocol in the Internet of Things”, Jul. 2020, International Journal on AdHoc Networking Systems (IJANS) vol. 10, No. 1/2/3, pp. 1-17. (Year: 2020). [cited by examiner]
Sumra et al.; “Trust Levels in Peer-to-Peer (P2P) Vehicular Network”, 2011, IEEE, pp. 708-714. (Year: 2011). [cited by examiner]
Ahmadi, M. et al., “Probabilistic Key Pre-distribution for Heterogeneous Mobile Ad hoc Networks Using Subjective Logic” IEEE Computer Society, Apr. 2015, pp. 185-192. [cited by applicant]
Extended European Search Report for Application No. 22386057.8 dated Jan. 20, 2023. 8 pgs. [cited by applicant]
Kannan Govindan et al: “Trust Computations and Trust Dynamics in Mobile Ad.hoc Networks: A Survey”, IEEE Communications Surveys & Tutorials, IEEE, USA, vol. 14, No. 2, Apr. 1, 2012 (Apr. 1, 2012), pp. 279-298, XP0114433… [cited by applicant]
Khan Muhammad Saleem et al: “Adaptive Reputation Weights Assignment Scheme for MANETs”, 2016 IEEE Trustcom/Bigdatase/ISPA, IEEE, Aug. 23, 2016 (Aug. 23, 2016), pp. 160-167, XP033063322, DOI: 10.1109/Trustcom.20 16.0059. [cited by applicant]
Bhargava Arpita et al:“KATE: Kalman Trust Estimator for Internet of Drones”, Computer Communications, Elsevier Science PU Blishers BV, Amsterdam, NL, vol. 160, Apr. 22, 2020 (Apr. 22, 2020),pp. 388-401, XP086250779, ISS… [cited by applicant]
Ge Chunpeng et al: “A Provenance-Aware Distributed Trust Model for Resilient Unmanned Aerial Vehicle Networks”, IEEE Internet of Things Journal, IEEE, USA, vol. 8, No. 16, Aug. 7, 2020 (Aug. 7, 2020), pp. 12481-12489, X… [cited by applicant]