A multi-agent and internet of things framework of digital twin for optimized manufacturing control

Author(s):  
Qingwei Nie ◽  
Dunbing Tang ◽  
Haihua Zhu ◽  
Hongwei Sun
Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3474
Author(s):  
Taehoon Kim ◽  
Wonbin Kim ◽  
Daehee Seo ◽  
Imyeong Lee

Recently, as Internet of Things systems have been introduced to facilitate diagnosis and treatment in healthcare and medical environments, there are many issues concerning threats to these systems’ security. For instance, if a key used for encryption is lost or corrupted, then ciphertexts produced with this key cannot be decrypted any more. Hence, this paper presents two schemes for key recovery systems that can recover the lost or the corrupted keys of an Internet of Medical Things. In our proposal, when the key used for the ciphertext is needed, this key is obtained from a Key Recovery Field present in the cyphertext. Thus, the recovered key will allow decrypting the ciphertext. However, there are threats to this proposal, including the case of the Key Recovery Field being forged or altered by a malicious user and the possibility of collusion among participating entities (Medical Institution, Key Recovery Auditor, and Key Recovery Center) which can interpret the Key Recovery Field and abuse their authority to gain access to the data. To prevent these threats, two schemes are proposed. The first one enhances the security of a multi-agent key recovery system by providing the Key Recovery Field with efficient integrity and non-repudiation functions, and the second one provides a proxy re-encryption function resistant to collusion attacks against the key recovery system.


Author(s):  
Maria G. Juarez ◽  
Vicente J. Botti ◽  
Adriana S. Giret

Abstract With the arises of Industry 4.0, numerous concepts have emerged; one of the main concepts is the digital twin (DT). DT is being widely used nowadays, however, as there are several uses in the existing literature; the understanding of the concept and its functioning can be diffuse. The main goal of this paper is to provide a review of the existing literature to clarify the concept, operation, and main characteristics of DT, to introduce the most current operating, communication, and usage trends related to this technology, and to present the performance of the synergy between DT and multi-agent system (MAS) technologies through a computer science approach.


Author(s):  
Bogdan Manațe ◽  
Florin Fortiş ◽  
Philip Moore

The rapid expansion of the Internet of Things (IoT) will generate a diverse range of data types that needs to be handled, processed and stored. This paper aims to create a multi-agent system that suits the needs introduced by the IoT expansion, thus being able to oversee the Big Data collection and processing and also to maintain the semantic links between the data sources and data consumers. In order to build a complex agent oriented architecture, we have assessed the existing agent oriented methodologies searching for the best solution that is not bound to a specific programming language of framework, and it is flexible enough to be applied in such a divers domain like IoT. As complex scenario, the proposed approach has been applied to medical diagnosis and motoring of mental disorders.


2001 ◽  
Vol 34 (17) ◽  
pp. 63-68
Author(s):  
Olaf Bochmann ◽  
Paul Valckenaers ◽  
Hendrik Van Brussel

2018 ◽  
Vol 188 ◽  
pp. 05006
Author(s):  
Christos Anagnostopoulos ◽  
Christos Alexakos ◽  
Apostolos Fournaris ◽  
Christos Koulamas ◽  
Athanasios Kalogeras

The manufacturing environment is characterized by increased complexity with different devices, systems and applications that need to interoperate, while residing at different layers of the classical industrial environment hierarchy. The introduction of the Industrial Internet of Things with increasingly smarter devices drives towards flatter hierarchies. This paper deals with an architecture for integration of IIoT devices in the manufacturing environment utilizing a Multi Agent System to this end. This extended architecture is utilised so as to perform failure detection of both IIoT devices and manufacturing resources, and react by altering the manufacturing process either automatically or semi-automatically.


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