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Publications (10 of 262) Show all publications
Sathi, V. N., Rohner, C. & Voigt, T. (2023). A PUF-Based Indirect Authentication and Key Establishment Protocol for Wearable Devices. In: IEEE International Conference on Communications (ICC): . Paper presented at IEEE International Conference on Communications (ICC) (pp. 615-621). Institute of Electrical and Electronics Engineers Inc., 2023-May, Article ID 193943.
Open this publication in new window or tab >>A PUF-Based Indirect Authentication and Key Establishment Protocol for Wearable Devices
2023 (English)In: IEEE International Conference on Communications (ICC), Institute of Electrical and Electronics Engineers Inc. , 2023, Vol. 2023-May, p. 615-621, article id 193943Conference paper, Published paper (Refereed)
Abstract [en]

Microwave communication through the fat tissue in the human body enables a new channel for wearable devices to communicate with each other. The wearable devices can communicate to the external world through a powerful device in their network called central control unit (CU); for example, a smartphone. Some wearable devices may be out of the range of the CU temporarily due to body movements or permanently due to low signal strength, in a fat channel communication network. Such devices can connect to the CU with the help of their neighbor device in the same network. In this paper, we propose a protocol to ensure secure indirect authentication and key establishment between the out-of-range device and the CU in a fat channel communication network, via an untrusted intermediate device in the network. The proposed protocol is lightweight and resistant to denial-of-sleep attacks on the intermediate device. We analyze the security and the computation overhead of the proposed protocol. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2023
Keywords
Cryptography; Network security; Wearable technology; Channel communication; Communications networks; Control unit; Denial-of-sleep attack; Fat channel communication; Key establishment protocol; Key establishments; Microwave communications; PUF-based authentication; Wearable devices; Authentication
National Category
Communication Systems Other Medical Engineering
Identifiers
urn:nbn:se:ri:diva-68777 (URN)10.1109/ICC45041.2023.10278883 (DOI)2-s2.0-85178295514 (Scopus ID)
Conference
IEEE International Conference on Communications (ICC)
Funder
Swedish Foundation for Strategic Research
Note

This project is financed by the Swedish Foundation for Strategic Research. 

Available from: 2024-01-15 Created: 2024-01-15 Last updated: 2024-02-06Bibliographically approved
Li, S., Ngai, E.-H. C. & Voigt, T. (2023). An Experimental Study of Byzantine-Robust Aggregation Schemes in Federated Learning. IEEE Transactions on Big Data
Open this publication in new window or tab >>An Experimental Study of Byzantine-Robust Aggregation Schemes in Federated Learning
2023 (English)In: IEEE Transactions on Big Data, E-ISSN 2332-7790Article in journal (Refereed) Epub ahead of print
Abstract [en]

Byzantine-robust federated learning aims at mitigating Byzantine failures during the federated training process, where malicious participants (known as Byzantine clients) may upload arbitrary local updates to the central server in order to degrade the performance of the global model. In recent years, several robust aggregation schemes have been proposed to defend against malicious updates from Byzantine clients and improve the robustness of federated learning. These solutions were claimed to be Byzantine-robust, under certain assumptions. Other than that, new attack strategies are emerging, striving to circumvent the defense schemes. However, there is a lack of systematical comparison and empirical study thereof. In this paper, we conduct an experimental study of Byzantine-robust aggregation schemes under different attacks using two popular algorithms in federated learning, FedSGD and FedAvg. We first survey existing Byzantine attack strategies, as well as Byzantine-robust aggregation schemes that aim to defend against Byzantine attacks. We also propose a new scheme, ClippedClustering, to enhance the robustness of a clustering-based scheme by automatically clipping the updates. Then we provide an experimental evaluation of eight aggregation schemes in the scenario of five different Byzantine attacks. Our experimental results show that these aggregation schemes sustain relatively high accuracy in some cases, but they are not effective in all cases. In particular, our proposed ClippedClustering successfully defends against most attacks under independent and identically distributed (IID) local datasets. However, when the local datasets are Non-IID, the performance of all the aggregation schemes significantly decreases. With Non-IID data, some of these aggregation schemes fail even in the complete absence of Byzantine clients. Based on our experimental study, we conclude that the robustness of all the aggregation schemes is limited, highlighting the need for new defense strategies, in particular for Non-IID datasets.

National Category
Engineering and Technology
Identifiers
urn:nbn:se:ri:diva-66173 (URN)10.1109/tbdata.2023.3237397 (DOI)
Available from: 2023-09-11 Created: 2023-09-11 Last updated: 2023-09-11Bibliographically approved
Singhal, C., Voigt, T. & Mottola, L. (2023). Application-aware Energy Attack Mitigation in the Battery-less Internet of Things. In: MobiWac 2023: Proceedings of the International ACM Symposium on Mobility Management and Wireless Access. Paper presented at MSWiM '23: Int'l ACM Conference on Modeling Analysis and Simulation of Wireless and Mobile Systems (pp. 35-43). Association for Computing Machinery, Inc
Open this publication in new window or tab >>Application-aware Energy Attack Mitigation in the Battery-less Internet of Things
2023 (English)In: MobiWac 2023: Proceedings of the International ACM Symposium on Mobility Management and Wireless Access, Association for Computing Machinery, Inc , 2023, p. 35-43Conference paper, Published paper (Refereed)
Abstract [en]

We study how to mitigate the effects of energy attacks in the battery-less Internet of Things∼(IoT). Battery-less IoT devices live and die with ambient energy, as they use energy harvesting to power their operation. They are employed in a multitude of applications, including safety-critical ones such as biomedical implants. Due to scarce energy intakes and limited energy buffers, their executions become intermittent, alternating periods of active operation with periods of recharging their energy buffers. Experimental evidence exists that shows how controlling ambient energy allows an attacker to steer a device execution in unintended ways: energy provisioning effectively becomes an attack vector. We design, implement, and evaluate a mitigation system for energy attacks. By taking into account the specific application requirements and the output of an attack detection module, we tune task execution rates and optimize energy management. This ensures continued application execution in the event of an energy attack. When a device is under attack, our solution ensures the execution of 23.3% additional application cycles compared to the baselines we consider and increases task schedulability by at least 21%, while enabling a 34% higher peripheral availability. 

Place, publisher, year, edition, pages
Association for Computing Machinery, Inc, 2023
Keywords
Internet of things; Safety engineering; Secondary batteries; Ambients; Battery-less; Battery-less iot application; Energy; Energy-attack mitigation; Federated energy harvesting; Intermittent computing; IOT applications; Power; Tasks scheduling; Energy harvesting
National Category
Computer Systems Communication Systems
Identifiers
urn:nbn:se:ri:diva-68773 (URN)10.1145/3616390.3618281 (DOI)2-s2.0-85178079293 (Scopus ID)
Conference
MSWiM '23: Int'l ACM Conference on Modeling Analysis and Simulation of Wireless and Mobile Systems
Funder
Swedish Foundation for Strategic Research
Available from: 2024-01-15 Created: 2024-01-15 Last updated: 2024-01-15Bibliographically approved
Li, S., Ngai, E. & Voigt, T. (2023). Byzantine-Robust Aggregation in Federated Learning Empowered Industrial IoT. IEEE Transactions on Industrial Informatics, 19(2), 1165
Open this publication in new window or tab >>Byzantine-Robust Aggregation in Federated Learning Empowered Industrial IoT
2023 (English)In: IEEE Transactions on Industrial Informatics, ISSN 1551-3203, E-ISSN 1941-0050, Vol. 19, no 2, p. 1165-Article in journal (Refereed) Published
Abstract [en]

Federated Learning (FL) is a promising paradigm to empower on-device intelligence in Industrial Internet of Things(IIoT) due to its capability of training machine learning models across multiple IIoT devices, while preserving the privacy of their local data. However, the distributed architecture of FL relies on aggregating the parameter list from the remote devices, which poses potential security risks caused by malicious devices. In this paper, we propose a flexible and robust aggregation rule, called Auto-weighted Geometric Median (AutoGM), and analyze the robustness against outliers in the inputs. To obtain the value of AutoGM, we design an algorithm based on alternating optimization strategy. Using AutoGM as aggregation rule, we propose two robust FL solutions, AutoGM_FL and AutoGM_PFL. AutoGM_FL learns a shared global model using the standard FL paradigm, and AutoGM_PFL learns a personalized model for each device. We conduct extensive experiments on the FEMNIST and Bosch IIoT datasets. The experimental results show that our solutions are robust against both model poisoning and data poisoning attacks. In particular, our solutions sustain high performance even when 30% of the nodes perform model or 50% of the nodes perform data poisoning attacks.

Keywords
Electrical and Electronic Engineering, Computer Science Applications, Information Systems, Control and Systems Engineering, Computer Sciences, Datavetenskap (datalogi)
National Category
Communication Systems
Identifiers
urn:nbn:se:ri:diva-56965 (URN)10.1109/tii.2021.3128164 (DOI)
Available from: 2021-11-19 Created: 2021-11-19 Last updated: 2023-06-08Bibliographically approved
Shen, Q., Mahima, K., de Zoysa, K., Mottola, L., Voigt, T. & Flierl, M. (2023). CNN-Based Estimation of Water Depth from Multispectral Drone Imagery for Mosquito Control. In: 2023 IEEE International Conference on Image Processing (ICIP): . Paper presented at 2023 IEEE International Conference on Image Processing (ICIP) (pp. 3250-3254). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>CNN-Based Estimation of Water Depth from Multispectral Drone Imagery for Mosquito Control
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2023 (English)In: 2023 IEEE International Conference on Image Processing (ICIP), Institute of Electrical and Electronics Engineers (IEEE), 2023, p. 3250-3254Conference paper, Published paper (Refereed)
Abstract [en]

We present a machine learning approach that uses a custom Convolutional Neural Network (CNN) for estimating the depth of water pools from multispectral drone imagery. Using drones to obtain this information offers a cheaper, timely, and more accurate solution compared to alternative methods, such as manual inspection. This information, in turn, represents an asset to identify potential breeding sites of mosquito larvae, which grow only in shallow water pools. As a significant part of the world’s population is affected by mosquito-borne viral infections, including Dengue and Zika, identifying mosquito breeding sites is key to control their spread. Experiments with 5-band drone imagery show that our CNN-based approach is able to measure shallow water depths accurately up to a root mean square error of less than 0.5 cm, outperforming state-of-the-art Random Forest methods and empirical approaches.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:ri:diva-68550 (URN)10.1109/ICIP49359.2023.10222934 (DOI)
Conference
2023 IEEE International Conference on Image Processing (ICIP)
Note

This work has been partly funded by Digital Futures and the SwedishResearch Council (Grant 2018-05024) 

Available from: 2023-12-13 Created: 2023-12-13 Last updated: 2023-12-13Bibliographically approved
Perez-Ramirez, D. F., Pérez-Penichet, C., Tsiftes, N., Voigt, T., Kostic, D. & Boman, M. (2023). DeepGANTT: A Scalable Deep Learning Scheduler for Backscatter Networks. In: Association for Computing Machinery (Ed.), IPSN '23: Proceedings of the 22nd International Conference on Information Processing in Sensor Networks: . Paper presented at IPSN '23: The 22nd International Conference on Information Processing in Sensor Networks (pp. 163). New York, NY, United States
Open this publication in new window or tab >>DeepGANTT: A Scalable Deep Learning Scheduler for Backscatter Networks
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2023 (English)In: IPSN '23: Proceedings of the 22nd International Conference on Information Processing in Sensor Networks / [ed] Association for Computing Machinery, New York, NY, United States, 2023, p. 163-Conference paper, Published paper (Refereed)
Abstract [en]

Novel backscatter communication techniques enable battery-free sensor tags to interoperate with unmodified standard IoT devices, extending a sensor network’s capabilities in a scalable manner. Without requiring additional dedicated infrastructure, the battery-free tags harvest energy from the environment, while the IoT devices provide them with the unmodulated carrier they need to communicate. A schedule coordinates the provision of carriers for the communications of battery-free devices with IoT nodes. Optimal carrier scheduling is an NP-hard problem that limits the scalability of network deployments. Thus, existing solutions waste energy and other valuable resources by scheduling the carriers suboptimally. We present DeepGANTT, a deep learning scheduler that leverages graph neural networks to efficiently provide near-optimal carrier scheduling. We train our scheduler with optimal schedules of relatively small networks obtained from a constraint optimization solver, achieving a performance within 3% of the optimum. Without the need to retrain, our scheduler generalizes to networks 6 × larger in the number of nodes and 10 × larger in the number of tags than those used for training. DeepGANTT breaks the scalability limitations of the optimal scheduler and reduces carrier utilization by up to compared to the state-of-the-art heuristic. As a consequence, our scheduler efficiently reduces energy and spectrum utilization in backscatter networks.

Place, publisher, year, edition, pages
New York, NY, United States: , 2023
Keywords
scheduling, machine learning, wireless backscatter communications, combinatorial optimization
National Category
Communication Systems Computer Sciences Information Systems
Identifiers
urn:nbn:se:ri:diva-64865 (URN)10.1145/3583120.3586957 (DOI)979-8-4007-0118-4 (ISBN)
Conference
IPSN '23: The 22nd International Conference on Information Processing in Sensor Networks
Projects
SSF Instant Cloud ElasticityHorizon 2020 AI@Edge
Funder
Swedish Foundation for Strategic ResearchEU, Horizon 2020, 101015922Swedish Research Council, 2017-045989
Note

This work was financially supported by the Swedish Foundationfor Strategic Research (SSF), by the European Union’s Horizon 2020AI@EDGE project (Grant 101015922), and by the Swedish ResearchCouncil (Grant 2017-045989). 

Available from: 2023-05-23 Created: 2023-05-23 Last updated: 2023-06-08Bibliographically approved
Mahima, K. T., Weerasekara, M., Zoysa, K. D., Keppitiyagama, C., Flierl, M., Mottola, L. & Voigt, T. (2023). MM4Drone: A Multi-spectral Image and mmWave Radar Approach for Identifying Mosquito Breeding Grounds via Aerial Drones. In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST: . Paper presented at 16th EAI International Conference on Pervasive Computing Technologies for Healthcare, PH 2022. Thessaloniki, Greece. 12 December 2022 through 14 December 2022 (pp. 412-426). Springer Science and Business Media Deutschland GmbH, 488
Open this publication in new window or tab >>MM4Drone: A Multi-spectral Image and mmWave Radar Approach for Identifying Mosquito Breeding Grounds via Aerial Drones
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2023 (English)In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Springer Science and Business Media Deutschland GmbH , 2023, Vol. 488, p. 412-426Conference paper, Published paper (Refereed)
Abstract [en]

Mosquitoes spread disases such as Dengue and Zika that affect a significant portion of the world population. One approach to hamper the spread of the disases is to identify the mosquitoes’ breeding places. Recent studies use drones to detect breeding sites, due to their low cost and flexibility. In this paper, we investigate the applicability of drone-based multi-spectral imagery and mmWave radios to discover breeding habitats. Our approach is based on the detection of water bodies. We introduce our Faster R-CNN-MSWD, an extended version of the Faster R-CNN object detection network, which can be used to identify water retention areas in both urban and rural settings using multi-spectral images. We also show promising results for estimating extreme shallow water depth using drone-based multi-spectral images. Further, we present an approach to detect water with mmWave radios from drones. Finally, we emphasize the importance of fusing the data of the two sensors and outline future research directions. 

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH, 2023
Keywords
Aerial Drones, mmWave Radar, Multispectral Imagery, Object Detection, Aircraft detection, Antennas, Drones, Millimeter waves, Object recognition, Radar imaging, Tracking radar, Aerial drone, Breeding grounds, Low-costs, Mm waves, Mosquito breeding, Multispectral images, Objects detection, World population
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:ri:diva-65700 (URN)10.1007/978-3-031-34586-9_27 (DOI)2-s2.0-85164166242 (Scopus ID)9783031345852 (ISBN)
Conference
16th EAI International Conference on Pervasive Computing Technologies for Healthcare, PH 2022. Thessaloniki, Greece. 12 December 2022 through 14 December 2022
Note

This work has been partly funded by Digital Futures and the Swedish Research Council (Grant 2018-05024).

Available from: 2023-08-11 Created: 2023-08-11 Last updated: 2023-08-11Bibliographically approved
Krentz, K.-F. & Voigt, T. (2023). Reducing Trust Assumptions with OSCORE, RISC-V, and Layer 2 One-Time Passwords. In: Lecture Notes in Computer Science Volume 13877 Pages 389 - 405 2023: . Paper presented at 15th International Symposium on Foundations and Practice of Security, FPS 2022. Ottawa 12 December 2022 through 14 December 2022. (pp. 389-405). Springer Science and Business Media Deutschland GmbH
Open this publication in new window or tab >>Reducing Trust Assumptions with OSCORE, RISC-V, and Layer 2 One-Time Passwords
2023 (English)In: Lecture Notes in Computer Science Volume 13877 Pages 389 - 405 2023, Springer Science and Business Media Deutschland GmbH , 2023, p. 389-405Conference paper, Published paper (Refereed)
Abstract [en]

In the Internet of things (IoT), traffic often goes via middleboxes, such as brokers or virtual private network (VPN) gateways, thereby increasing the trusted computing base (TCB) of IoT applications considerably. A remedy is offered by the application layer security protocol Object Security for Constrained RESTful Environments (OSCORE). It allows for basic middlebox functions without breaking end-to-end security. With OSCORE, however, traffic is routed to IoT devices largely unfiltered. This opens up avenues for remote denial-of-sleep attacks where a remote attacker injects OSCORE messages so as to cause IoT devices to consume more energy. The state-of-the-art defense is to let a trusted middlebox perform authenticity, freshness, and per-client rate limitation checks before forwarding OSCORE messages to IoT devices, but this solution inflates the TCB and hence negates the idea behind OSCORE. In this paper, we suggest filtering OSCORE messages in a RISC-V-based trusted execution environment (TEE) running on a middlebox that remains widely untrusted. To realize this approach, we also put forward the tiny remote attestation protocol (TRAP), as well as a Layer 2 integration that prevents attackers from bypassing our TEE. Experimental results show our remote denial-of-sleep defense to be lightweight enough for low-end IoT devices and to keep the TCB small. © 2023, The Author(s)

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH, 2023
Keywords
Authentication, Network security, Virtual private networks, Application layer securities, Breakings, End-to-end security, Energy, Layer 2, Middleboxes, Security protocols, Trust assumptions, Trusted computing base, Trusted execution environments, Internet of things
National Category
Computer Systems
Identifiers
urn:nbn:se:ri:diva-64399 (URN)10.1007/978-3-031-30122-3_24 (DOI)2-s2.0-85152544169 (Scopus ID)9783031301216 (ISBN)
Conference
15th International Symposium on Foundations and Practice of Security, FPS 2022. Ottawa 12 December 2022 through 14 December 2022.
Note

 Funding details: Stiftelsen för Strategisk Forskning, SSF, 2017-045989; Funding text 1: Acknowledgment. This work was carried out within the LifeSec project, which is funded by the Swedish Foundation for Strategic Research (grant 2017-045989).

Available from: 2023-05-08 Created: 2023-05-08 Last updated: 2023-06-08Bibliographically approved
Padmal, M., Rohner, C., Augustine, R. & Voigt, T. (2023). RFID Tags as Passive Temperature Sensors. In: 2023 IEEE International Conference on RFID, RFID 2023: . Paper presented at 2023 IEEE International Conference on RFID, RFID 2023. Seattle, USA. Seattle13 June 2023 through 15 June 2023 (pp. 48-53). Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>RFID Tags as Passive Temperature Sensors
2023 (English)In: 2023 IEEE International Conference on RFID, RFID 2023, Institute of Electrical and Electronics Engineers Inc. , 2023, p. 48-53Conference paper, Published paper (Refereed)
Abstract [en]

Temperature sensing and monitoring play a vital role in various applications. Non-invasive, item-level temperature sensing methods that require no direct line of sight with the measuring object are attractive. This paper presents such a temperature sensing method using commodity RFID tags with no infrastructure changes. RFID tags are widely deployed for product identification purposes. We explore the possibility of leveraging the RSSI measurements from commodity RFID tags for temperature sensing. Essentially, we model a relationship between the temperature and the relative permittivity of a material in terms of RSSI. Our method can measure temperature in the range of 22°C to 60°C and achieves a measurement accuracy of 2 ° C with 3 mean error of 1.5°C.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2023
Keywords
Passive RFID, RSSI, Temperature Sensing, Radio frequency identification (RFID), Item-level, Line of Sight, Lines-of-sight, Product identification, RFID-tag, RSSI measurement, Temperature monitoring, Temperature sensors
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:ri:diva-65957 (URN)10.1109/RFID58307.2023.10178523 (DOI)2-s2.0-85166939781 (Scopus ID)9798350335514 (ISBN)
Conference
2023 IEEE International Conference on RFID, RFID 2023. Seattle, USA. Seattle13 June 2023 through 15 June 2023
Note

This work has been financially supported by the Swedish Research Council (Grant 2021-04968 and 2018-05480), and the Swedish Foundation for Strategic Research

Available from: 2023-08-24 Created: 2023-08-24 Last updated: 2023-08-24Bibliographically approved
Li, S., Ngai, E., Ye, F. & Voigt, T. (2022). Auto-Weighted Robust Federated Learning with Corrupted Data Sources. ACM Transactions on Intelligent Systems and Technology, 13(5)
Open this publication in new window or tab >>Auto-Weighted Robust Federated Learning with Corrupted Data Sources
2022 (English)In: ACM Transactions on Intelligent Systems and Technology, ISSN 2157-6904, E-ISSN 2157-6912, Vol. 13, no 5Article in journal (Refereed) Published
Abstract [en]

Federated learning provides a communication-efficient and privacy-preserving training process by enabling learning statistical models with massive participants without accessing their local data. Standard federated learning techniques that naively minimize an average loss function are vulnerable to data corruptions from outliers, systematic mislabeling, or even adversaries. In this article, we address this challenge by proposing Auto-weighted Robust Federated Learning (ARFL), a novel approach that jointly learns the global model and the weights of local updates to provide robustness against corrupted data sources. We prove a learning bound on the expected loss with respect to the predictor and the weights of clients, which guides the definition of the objective for robust federated learning. We present an objective that minimizes the weighted sum of empirical risk of clients with a regularization term, where the weights can be allocated by comparing the empirical risk of each client with the average empirical risk of the best ( p ) clients. This method can downweight the clients with significantly higher losses, thereby lowering their contributions to the global model. We show that this approach achieves robustness when the data of corrupted clients is distributed differently from the benign ones. To optimize the objective function, we propose a communication-efficient algorithm based on the blockwise minimization paradigm. We conduct extensive experiments on multiple benchmark datasets, including CIFAR-10, FEMNIST, and Shakespeare, considering different neural network models. The results show that our solution is robust against different scenarios, including label shuffling, label flipping, and noisy features, and outperforms the state-of-the-art methods in most scenarios.

Place, publisher, year, edition, pages
Association for Computing Machinery, 2022
Keywords
Federated learning, robustness, Auto-weighted, distributed learning, neural networks
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:ri:diva-59799 (URN)10.1145/3517821 (DOI)
Available from: 2022-07-14 Created: 2022-07-14 Last updated: 2023-06-08Bibliographically approved
Projects
Secure and Reliable In-body Backscatter [2021-04968_VR]; Uppsala University
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-2586-8573

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