Managing Byzantine Robots via Blockchain Technology in a Swarm Robotics Collective Decision Making Scenario
Open accessAbstract
While swarm robotics systems are often claimed to be highly fault-tolerant, so far research has limited its attention to safe laboratory settings and has virtually ignored security issues in the presence of Byzantine robots---i.e., robots with arbitrarily faulty or malicious behavior. However, in many applications one or more Byzantine robots may suffice to let current swarm coordination mechanisms fail with unpredictable or disastrous outcomes. In this paper, we provide a proof-of-concept for managing security issues in swarm robotics systems via blockchain technology. Our approach uses decentralized programs executed via blockchain technology (blockchain-based smart contracts) to establish secure swarm coordination mechanisms and to identify and exclude Byzantine swarm members. We studied the performance of our blockchain-based approach in a collective decision-making scenario both in the presence and absence of Byzantine robots and compared our results to those obtained with an existing collective decision approach. The results show a clear advantage of the blockchain approach when Byzantine robots are part of the swarm.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
Generic robots have been used to produce proofs of concept, often addressing tasks that are a direct transposition in the artificial world of analogous tasks performed by self-organized natural systems.
“On the other hand, generic robots (alice [19], [57] and e-puck [37]) have been used to produce proofs of concept, often addressing tasks that are a direct transposition in the artificial world of analogous tasks performed by self-organized natural systems (e.g., foraging [13], [34]).”
From Swarm Robotics: Past, Present, and Future [Point of View] (2021) · cites this paper as [19]
Robot swarms could truly help biologists, providing artificial, controllable models to study the effects of embodiment, perception, action, and the individual cognitive requirements necessary to support collective behavior.
“We believe that this situation can be improved and that robot swarms could truly help biologists, providing artificial, controllable models to study the effects of embodiment, perception, action, and the individual cognitive requirements necessary to support collective behavior [19], [73].”
From Swarm Robotics: Past, Present, and Future [Point of View] (2021) · cites this paper as [19]
Cites
Still being checked.
Cited by
Sorted by citations
Blockchain for AI: review and open research challenges
Salah, K., Rehman, M. H. U., Nizamuddin, N., & Al-Fuqaha, A. (2019) · IEEE Access · doi:10.1109/access.2018.2890507 · Cited by 1,001 · see citing works
Blockchain in Industries: A Survey
Al‐Jaroodi, J., & Mohamed, N. (2019) · IEEE Access · doi:10.1109/access.2019.2903554 · Cited by 569 · see citing works
Swarm Robotics: Past, Present, and Future [Point of View]
Dorigo, M., Theraulaz, G., & Trianni, V. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3072740 · Cited by 401 · see citing works
Self-citationBlockchain for Industry 5.0: Vision, Opportunities, Key Enablers, and Future Directions
Verma, A., Bhattacharya, P., Madhani, N., Trivedi, C., Bhushan, B., Tanwar, S., Sharma, G., Bokoro, P. N., & Sharma, R. (2022) · IEEE Access · doi:10.1109/access.2022.3186892 · Cited by 217 · see citing works
Strobel, V., Castelló Ferrer, E., & Dorigo, M. (2020) · Frontiers in Robotics and AI · doi:10.3389/frobt.2020.00054 · Cited by 127 · see citing works
Self-citationBlockchain-Empowered Multi-Robot Collaboration to Fight COVID-19 and Future Pandemics
Alsamhi, S. H., & Lee, B. (2020) · IEEE Access · doi:10.1109/access.2020.3032450 · Cited by 129 · see citing works
A Survey on Blockchain in Robotics: Issues, Opportunities, Challenges and Future Directions
Aditya, U. S. P. S., Singh, R., Singh, P. K., & Kalla, A. (2021) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2021.103245 · Cited by 100 · see citing works
Alsamhi, S. H., Shvetsov, A. V., Shvetsov, A. V., Hawbani, A., Guizani, M., Alhartomi, M. A., & Ma, O. (2022) · IEEE Transactions on Green Communications and Networking · doi:10.1109/tgcn.2022.3195479 · Cited by 98 · see citing works
Trusted AI in Multiagent Systems: An Overview of Privacy and Security for Distributed Learning
Ma, C., Li, J., Wei, K., Liu, B., Ding, M., Yuan, L., Han, Z., & Vincent Poor, H. (2023) · Proceedings of the IEEE · doi:10.1109/jproc.2023.3306773 · Cited by 96 · see citing works
Multi-Robot Information Gathering for Precision Agriculture: Current State, Scope, and Challenges
Dutta, A., Roy, S., Kreidl, O. P., & Bölöni, L. L. (2021) · IEEE Access · doi:10.1109/access.2021.3130900 · Cited by 64 · see citing works
Machine Learning Algorithm for Cryptocurrencies Price Prediction
Awotunde, J. B., Ogundokun, R. O., Jimoh, R. G., Misra, S., & Aro, T. O. (2021) · Studies in Computational Intelligence · doi:10.1007/978-3-030-72236-4_17 · Cited by 58 · see citing works
RoboChain: A Secure Data-Sharing Framework for Human-Robot InteractionPreprint
Ferrer, E. C., Rudovic, O. O., T, T. H. M. I., & Pentland, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.04480 · Cited by 52 · see citing works
Enhancing Autonomy with Blockchain and Multi-Access Edge Computing in Distributed Robotic Systems
Queralta, J. P., Li, Q., Zou, Z., & Westerlund, T. (2020) · International Conference on Fog and Mobile Edge Computing (FMEC) · doi:10.1109/fmec49853.2020.9144809 · Cited by 46 · see citing works
Following Leaders in Byzantine Multirobot Systems by Using Blockchain Technology
Ferrer, E. C., Jiménez, E., López-Presa, J. L., & Martín-Rueda, J. (2021) · IEEE Transactions on Robotics · doi:10.1109/tro.2021.3104243 · Cited by 45 · see citing works
“I’ll take care of you,” said the robot
Fosch‐Villaronga, E., & Albó-Canals, J. (2019) · Paladyn Journal of Behavioral Robotics · doi:10.1515/pjbr-2019-0006 · Cited by 45 · see citing works
Towards a Blockchain-Based Multi-UAV Surveillance System
Campos, M. G. S. D., Chanel, C. P. C., Chauffaut, C., & Lacan, J. (2021) · Frontiers in Robotics and AI · doi:10.3389/frobt.2021.557692 · Cited by 41 · see citing works
The Applications of Blockchain in Artificial Intelligence
Wang, R., Luo, M., Wen, Y., Wang, L., Choo, K. R., & He, D. (2021) · Security and Communication Networks · doi:10.1155/2021/6126247 · Cited by 43 · see citing works
Zhang, J., Keramat, F., Yu, X., Hernández, D. M., Queralta, J. P., & Westerlund, T. (2022) · International Conference on Fog and Mobile Edge Computing (FMEC) · doi:10.1109/fmec57183.2022.10062523 · Cited by 40 · see citing works
To what extent will blockchain drive the machine economy? Perspectives from a prospective study
Schweizer, A., Knoll, P., Urbach, N., von der Gracht, H. A., & Hardjono, T. (2020) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2020.2979286 · Cited by 39 · see citing works
Strobel, V., & Dorigo, M. (2018) · Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles) · Cited by 38 · see citing works
Self-citationIndustrial Applications of Blockchain
Al‐Jaroodi, J., & Mohamed, N. (2019) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc.2019.8666530 · Cited by 38 · see citing works
The Role of Blockchain in Transforming Industries Beyond Finance
Vanmathi, C., Farouk, A., Alhammad, S. M., Ramaiah, M., Bhattacharya, S., & Kasyapa, M. S. B. (2024) · IEEE Access · doi:10.1109/access.2024.3468611 · Cited by 38 · see citing works
Blockchain-Powered Collaboration in Heterogeneous Swarms of RobotsPreprint
Queralta, J. P., & Westerlund, T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.01711 · Cited by 35 · see citing works
Secure Communication for Multiquadrotor Networks Using Ethereum Blockchain
Abichandani, P., Lobo, D., Kabrawala, S., & McIntyre, W. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3015716 · Cited by 33 · see citing works
Robotchain: Using Tezos Technology for Robot Event Management
Fernandes, M., & Alexandre, L. A. (2019) · Ledger · doi:10.5195/ledger.2019.175 · Cited by 32 · see citing works
SolGuard: Preventing external call issues in smart contract-based multi-agent robotic systems
Praitheeshan, P., Pan, L., Zheng, X., Jolfaei, A., & Doss, R. (2021) · Information Sciences · doi:10.1016/j.ins.2021.08.007 · Cited by 31 · see citing works
An Investigation of Byzantine Threats in Multi-Robot Systems
Deng, G., Zhou, Y. H., Xu, Y., Zhang, T., & Liu, Y. (2021) · International Symposium on Research in Attacks, Intrusions and Defenses (RAID) · doi:10.1145/3471621.3471867 · Cited by 31 · see citing works
Multi-featured collective perception with Evidence Theory: tackling spatial correlations
Bartashevich, P., & Mostaghim, S. (2021) · Swarm Intelligence · doi:10.1007/s11721-021-00192-8 · Cited by 29 · see citing works
Benchmarking Collective Perception: New Task Difficulty Metrics for Collective Decision-Making
Bartashevich, P., & Mostaghim, S. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-30241-2_58 · Cited by 24 · see citing works
Berman, I., Zereik, E., Kapitonov, A., Bonsignorio, F., Khassanov, A., Орипова, А. А., Lonshakov, S., & Bulatov, V. (2020) · Frontiers in Robotics and AI · doi:10.3389/frobt.2020.00070 · Cited by 23 · see citing works
A Blockchain-Controlled Physical Robot Swarm Communicating via an Ad-Hoc Network
Pacheco, A., Strobel, V., & Dorigo, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60376-2_1 · Cited by 23 · see citing works
Self-citationGuo, S., Cao, S., & Guo, J. J. (2021) · Journal of Bionic Engineering · doi:10.1007/s42235-021-00073-0 · Cited by 23 · see citing works
Suppressing Chain Size of Blockchain-Based Information Sharing for Swarm Robotic Systems
Nishida, Y., Kaneko, K., Sharma, S., & Sakurai, K. (2018) · International Symposium on Computing and Networking Workshops (CANDARW) · doi:10.1109/candarw.2018.00102 · Cited by 22 · see citing works
Securing emergent behaviour in swarm robotics
Chen, L., & Ng, S. (2021) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2021.103047 · Cited by 20 · see citing works
Detecting Robotic Anomalies using RobotChain
Lopes, V., & Alexandre, L. A. (2019) · IEEE International Conference on Autonomous Robot Systems and Competitions · doi:10.1109/icarsc.2019.8733618 · Cited by 20 · see citing works
Collective Decision Making in Swarm Robotics with Distributed Bayesian Hypothesis Testing
Shan, Q., & Mostaghim, S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60376-2_5 · Cited by 20 · see citing works
Blockchains: a Systematic Multivocal Literature ReviewPreprint
Butijn, B.-J., Tamburri, D. A., & van den Heuvel, W. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.11770 · Cited by 20 · see citing works
Robot Workspace Monitoring Using a Blockchain-Based 3D Vision Approach
Lopes, V., Pereira, N. J. M., & Alexandre, L. A. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00340 · Cited by 19 · see citing works
Blockchain for Multi-Robot Collaboration to Combat COVID-19 and Future PandemicsPreprint
Alsamhi, S. H., & Lee, B. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.02137 · Cited by 19 · see citing works
Robotic Services for New Paradigm Smart Cities Based on Decentralized Technologies
Kapitonov, A., Lonshakov, S., Berman, I., Ferrer, E. C., Bonsignorio, F., Bulatov, V., & Svistov, A. (2019) · Ledger · doi:10.5195/ledger.2019.177 · Cited by 18 · see citing works
Hyperledger Fabric Blockchain and ROS 2 Integration for Autonomous Mobile Robots
Salimi, S., Queralta, J. P., & Westerlund, T. (2023) · IEEE/SICE International Symposium on System Integration (SII) · doi:10.1109/sii55687.2023.10039326 · Cited by 18 · see citing works
Secure Heterogeneous Multi-Robot Collaboration and Docking with Hyperledger Fabric Blockchain
Salimi, S., Morón, P. T., Queralta, J. P., & Westerlund, T. (2022) · IEEE World Forum on Internet of Things (WF-IoT) · doi:10.1109/wf-iot54382.2022.10152244 · Cited by 18 · see citing works
Discrete collective estimation in swarm robotics with distributed Bayesian belief sharing
Shan, Q., & Mostaghim, S. (2021) · Swarm Intelligence · doi:10.1007/s11721-021-00201-w · Cited by 18 · see citing works
Roy, S., Vo, T., Hernandez, S., Lehrmann, A., Ali, A., & Kalafatis, S. (2022) · Sensors · doi:10.3390/s22155569 · Cited by 17 · see citing works
Towards applied swarm robotics: current limitations and enablers
Kegeleirs, M., & Birattari, M. (2025) · Frontiers in Robotics and AI · doi:10.3389/frobt.2025.1607978 · Cited by 16 · see citing works
Military Intelligence Applications for Blockchain Technology
McAbee, A. S. M., Tummala, M., & McEachen, J. (2019) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2019.726 · Cited by 16 · see citing works
A Blockchain Enhanced Framework for Social Networking
Murimi, R. M. (2019) · Ledger · doi:10.5195/ledger.2019.178 · Cited by 15 · see citing works
Swarm Contracts: Smart Contracts in Robotic Swarms with Varying Agent Behavior
Grey, J., Godage, I. S., & Seneviratne, O. W. (2020) · IEEE International Conference on Blockchain (Blockchain) · doi:10.1109/blockchain50366.2020.00040 · Cited by 15 · see citing works
Grey, J., Seneviratne, O. W., & Godage, I. S. (2021) · IEEE International Conference on Blockchain (Blockchain) · doi:10.1109/blockchain53845.2021.00090 · Cited by 14 · see citing works
Blockchain Design for an Embedded System
Falcone, S., Zhang, J. Z., Cameron, A., & Abdel-Rahman, A. (2019) · Ledger · doi:10.5195/ledger.2019.172 · Cited by 14 · see citing works
Discrete Collective Estimation in Swarm Robotics with Ranked Voting Systems
Shan, Q., Heck, A., & Mostaghim, S. (2021) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci50451.2021.9659868 · Cited by 14 · see citing works
End-to-End Design for Self-Reconfigurable Heterogeneous Robotic Swarms
Queralta, J. P., Li, Q., Gia, T. N., Truong, H., & Westerlund, T. (2020) · International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT) · doi:10.1109/dcoss49796.2020.00052 · Cited by 14 · see citing works
Blockchain for Decentralized Multi-Drone to Combat COVID-19Preprint
Alsamhi, S. H., Lee, B., Guizani, M., Kumar, N., Qiao, Y., & Liu, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.00969 · Cited by 14 · see citing works
On the robustness of consensus-based behaviors for robot swarms
Moussa, M., & Beltrame, G. (2020) · Swarm Intelligence · doi:10.1007/s11721-020-00183-1 · Cited by 14 · see citing works
A Generic Framework for Byzantine-Tolerant Consensus Achievement in Robot Swarms
Zhao, H., Pacheco, A., Strobel, V., Reina, A., Liu, X., Dudek, G., & Dorigo, M. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10341423 · Cited by 13 · see citing works
Self-citationBee-Bots: A Blockchain Based Decentralised Swarm Robotic System
Karthik, S., Chandhar, N. P., Akil, M., Chander, S., Amogh, J., & Aditya, R. (2020) · International Conference on Control, Automation and Robotics (ICCAR) · doi:10.1109/iccar49639.2020.9108053 · Cited by 13 · see citing works
Making Robotic Swarms Trustful: A Blockchain-Based Perspective
Thakur, A., Sahoo, S., Mukherjee, A., & Halder, R. (2023) · Journal of Computing and Information Science in Engineering · doi:10.1115/1.4062326 · Cited by 12 · see citing works
Primiero, G., Tuci, E., Tagliabue, J., & Ferrante, E. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00533-7_17 · Cited by 12 · see citing works
Managing Collaborative Tasks within Heterogeneous Robotic Swarms using Swarm Contracts
Mallikarachchi, S., Dai, C., Seneviratne, O. W., & Godage, I. S. (2022) · IEEE International Conference on Decentralized Applications and Infrastructures (DAPPS) · doi:10.1109/dapps55202.2022.00014 · Cited by 12 · see citing works
Minimalistic Collective Perception with Imperfect Sensors
Chin, K. Y., Khaluf, Y., & Pinciroli, C. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10341384 · Cited by 11 · see citing works
Almansoori, A., Alkilabi, M. H. M., & Tuci, E. (2024) · Swarm Intelligence · doi:10.1007/s11721-023-00233-4 · Cited by 11 · see citing works
Kawakura, S., & Shibasaki, R. (2020) · European Journal of Agriculture and Food Sciences · doi:10.24018/ejfood.2020.2.3.45 · Cited by 11 · see citing works
Secure and secret cooperation of robotic swarms by using Merkle treesPreprint
Ferrer, E. C., T, T. H. M. I., Alex, A., & Pentland, A. (2019) · arXiv (Cornell University) · Cited by 11 · see citing works
Mitchell, R., Blumenkamp, J., & Prorok, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.00508 · Cited by 11 · see citing works
Grex: A Decentralized Hive Mind
Khawalid, A., Acristinii, D., Toor, H. V., & Ferrer, E. C. (2019) · Ledger · doi:10.5195/ledger.2019.176 · Cited by 10 · see citing works
Blockchain Technology with Applications to Distributed Control and Cooperative Robotics: A Survey
Khan, A., Cao, X.-W., Li, S., & Milosevic, Z. (2018) · International Journal of Robotics and Control · doi:10.5430/ijrc.v2n1p36 · Cited by 10 · see citing works
BASIC: Towards a Blockchained Agent-Based SImulator for Cities
Marrocco, L., Ferrer, E. C., Bucchiarone, A., Grignard, A., Alonso, L., Larson, K., & Pentland, A. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20937-7_10 · Cited by 10 · see citing works
Optimal Convex Hull Formation on a Grid by Asynchronous Robots with Lights
Hector, R., Vaidyanathan, R., Sharma, G., & Trahan, J. L. (2020) · IEEE International Parallel and Distributed Processing Symposium (IPDPS) · doi:10.1109/ipdps47924.2020.00111 · Cited by 9 · see citing works
Fu, L., Salimi, S., Queralta, J. P., & Westerlund, T. (2023) · International Conference on Ubiquitous Robots (UR) · doi:10.1109/ur57808.2023.10202495 · Cited by 9 · see citing works
Construction Task Allocation Through the Collective Perception of a Dynamic Environment
Khaluf, Y., Allwright, M., Rausch, I., Simoens, P., & Dorigo, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60376-2_7 · Cited by 9 · see citing works
Self-citationA Decentralized Cooperative Navigation Approach for Visual Homing Networks
Rahouti, M., Lyons, D. M., Jagatheesaperumal, S. K., & Xiong, K. (2023) · IT Professional · doi:10.1109/mitp.2023.3323865 · Cited by 9 · see citing works
Decentralized Consensus in Robotic Swarm for Collective Collision and Avoidance
FengYing, Y., Din, A., Liu, H., Babar, M. A., & Ahmad, S. (2024) · IEEE Access · doi:10.1109/access.2024.3402564 · Cited by 8 · see citing works
Advanced Drone Swarm Security by Using Blockchain Governance Game
Kim, S.-K. (2022) · Mathematics · doi:10.3390/math10183338 · Cited by 8 · see citing works
Ising Model as a Switch Voting Mechanism in Collective Perception
Bartashevich, P., & Mostaghim, S. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-30244-3_51 · Cited by 8 · see citing works
Design Methodology For Using Blockchain In Swarm Robotics
RANGANATHAN, S., Mariappan, M., & Muthukaruppan, K. (2021) · IEEE Student Conference on Research and Development (SCOReD) · doi:10.1109/scored53546.2021.9652778 · Cited by 8 · see citing works
Optimal Convex Hull Formation on a Grid by Asynchronous Robots With Lights
Hector, R., Vaidyanathan, R., Sharma, G., & Trahan, J. L. (2022) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2022.3158202 · Cited by 8 · see citing works
UAV Swarm Exploration With Byzantine Fault Tolerance
Liao, Z., Zhang, L., & Dong, Z. (2021) · China Automation Congress (CAC) · doi:10.1109/cac53003.2021.9727874 · Cited by 8 · see citing works
If blockchain is the solution, robot security is the problem
Ferrer, E. C. (2023) · Frontiers in Blockchain · doi:10.3389/fbloc.2023.1181820 · Cited by 8 · see citing works
Yang, H. (2025) · Finance research letters · doi:10.1016/j.frl.2025.108163 · Cited by 7 · see citing works
Efficient Distributed Consensus Algorithm For Swarm Robotic
RANGANATHAN, S., Mariappan, M., & Muthukaruppan, K. (2022) · IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) · doi:10.1109/iicaiet55139.2022.9936787 · Cited by 7 · see citing works
Wang, K., Liu, Z., Zhu, Z., Qi, G., Yao, J., & Miao, G. (2022) · Simulation Modelling Practice and Theory · doi:10.1016/j.simpat.2022.102599 · Cited by 7 · see citing works
Generating Collective Behavior of a Multi-Legged Robotic Swarm Using Deep Reinforcement Learning
Morimoto, D., Iwamoto, Y., Hiraga, M., & Ohkura, K. (2023) · Journal of Robotics and Mechatronics · doi:10.20965/jrm.2023.p0977 · Cited by 7 · see citing works
ISRaft Consensus Algorithm for Autonomous Units
Zamir, L., Shaan, A., & Nojoumian, M. (2021) · IEEE International Conference on Network Protocols (ICNP) · doi:10.1109/icnp52444.2021.9651979 · Cited by 7 · see citing works
Gaka-Chu: A Self-Employed Autonomous Robot Artist
Ferrer, E. C., Berman, I., Kapitonov, A., Manaenko, V., Chernyaev, M., & Tarasov, P. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160866 · Cited by 7 · see citing works
Optimal Arbitrary Pattern Formation on a Grid by Asynchronous Autonomous Robots
Hector, R., Sharma, G., Vaidyanathan, R., & Trahan, J. L. (2022) · IEEE International Parallel and Distributed Processing Symposium (IPDPS) · doi:10.1109/ipdps53621.2022.00115 · Cited by 6 · see citing works
Abdelli, A., Yachir, A., Amamra, A., & Khaldi, B. (2023) · Robotica · doi:10.1017/s0263574723000668 · Cited by 6 · see citing works
Filtering Inconsistent Failure in Robot Collective Decision with Blockchain
Luo, J., Ding, B., & Xu, J. (2019) · IEEE/ASME International Conference on Advanced Intelligent Mechatronics · doi:10.1109/aim.2019.8868871 · Cited by 6 · see citing works
Guo, S., Cao, S., Guo, J. J., & Xu, J. (2020) · IEEE International Conference on Mechatronics and Automation (ICMA) · doi:10.1109/icma49215.2020.9233789 · Cited by 6 · see citing works
On Securing Mobile Agents using Blockchain Technology
Ishita, I., Kulkarni, D. D., Semwal, T., & Nair, S. B. (2019) · International Conference on Advanced Computational and Communication Paradigms (ICACCP) · doi:10.1109/icaccp.2019.8882907 · Cited by 6 · see citing works
Reliability and security: from swarm robots to AI agents
Yan, Y., Xie, Y., Tang, J., Li, Y., & Jin, Y. (2025) · Journal of Reliability Science and Engineering · doi:10.1088/3050-2454/adea7a · Cited by 5 · see citing works
Proof of Location Verification towards Trustworthy Collaborative Multi-Vendor Robotic Systems
Wu, E. W., Jurt, M., Holden, B., & Jin, Y. (2024) · IEEE International Conference on Industrial Technology (ICIT) · doi:10.1109/icit58233.2024.10540894 · Cited by 5 · see citing works
A machine education approach to swarm decision-making in best-of-n problems
Hussein, A., Elsawah, S., Petraki, E., & Abbass, H. A. (2021) · Swarm Intelligence · doi:10.1007/s11721-021-00206-5 · Cited by 5 · see citing works
Kawakura, S., & Shibasaki, R. (2019) · European Journal of Agriculture and Food Sciences · doi:10.24018/ejfood.2019.1.2.13 · Cited by 5 · see citing works
Integrating Blockchain With AI
Vedula, R. M. S., Bhadoria, R. S., & Dixit, M. (2020) · Advances in data mining and database management book series · doi:10.4018/978-1-7998-5876-8.ch001 · Cited by 5 · see citing works
Leveraging Blockchain for Spoof-Resilient Robot Networks
Alam, T., Taylor, J., Taylor, J., Badsha, S., Shahid, A. R., & Kayes, A. S. M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-66645-3_18 · Cited by 5 · see citing works
A Blockchain-Based Robotic Process Automation Mechanism in Educational Setting
Sibanyoni, N. A. (2021) · Advances in data mining and database management book series · doi:10.4018/978-1-7998-6650-3.ch002 · Cited by 5 · see citing works
Hiraga, M., Morimoto, D., Katada, Y., & Ohkura, K. (2023) · Journal of Robotics and Mechatronics · doi:10.20965/jrm.2023.p0988 · Cited by 5 · see citing works
Opinion dissemination in a swarm of simulated robots with stubborn agents: a comparative study
Maître, G., Tuci, E., & Ferrante, E. (2020) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec48606.2020.9185581 · Cited by 5 · see citing works
Possibilities at the Intersection of AI and Blockchain Technology
(2019) · International Journal of Innovative Technology and Exploring Engineering · doi:10.35940/ijitee.a1030.1191s19 · Cited by 5 · see citing works
LLM2Swarm: Robot Swarms that Responsively Reason, Plan, and Collaborate through LLMs
Strobel, V., Dorigo, M., & Fritz, M. (2024) · arXiv · doi:10.48550/arxiv.2410.11387 · Cited by 4 · see citing works
“…for example, be used to store LLM responses or decisions made in different sub-swarms—thus preventing Byzantine robots (robots that show a discrepancy between their intended behavior and their actual behavior, for example, due to broken parts or malicious control [37]) from tampering with the data.”
BackgroundMethodSelf-citation
- Authors
- Strobel, V., Castelló Ferrer, E., & Dorigo, M.
- Affiliations
- Université Libre de Bruxelles (BE)
- Venue
- International Joint Conference on Autonomous Agents and Multiagent Systems
- Publisher
- IEEE Computer Society
- Published
- 2018-07-09
- Pages
- 541-549
- ISSN
- 1548-8403 (ISSN-L)
- 1558-2914
- OpenAlex
- W2808250907
- Cited by
- 149
- References
- 0
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 10 · 2023: 21 · 2022: 17 · 2021: 34 · 2020: 26 · 2019: 28 · 2018: 4
- FWCI
- 18.05
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- cc-by-nc-sa
- Locations
- Adaptive Agents and Multi-Agents Systems (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles) (submittedVersion)
- Adaptive Agents and Multi-Agents Systems
- Funders
- Fonds De La Recherche Scientifique - FNRS
- H2020 Marie Skłodowska-Curie Actions
- Topics
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Engineering › Mechanical Engineering › Modular Robots and Swarm Intelligence
- Physical Sciences › Computer Science › Computer Networks and Communications › Distributed Control Multi-Agent Systems
- Keywords
- swarm robotics, blockchain, smart contracts, group decision making, swarm coordination, fault tolerance, robot security, distributed coordination
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