Decentralized Network Building Change in Large Manufacturing Companies towards Industry 4.0

Abstract In complex industrial ecosystems together with an increasing global competition, success depends on a complete value chain transformation. The use of Industry 4.0 standards is therefore gradually emerging in many industries to ensure significantly higher factory productivity, flexibility, and efficiency. However, selected methodology and results are required to be studied to fully understand the digital transformation as well as its characteristics. This research presents a system conversion study, from centralized to decentralized systems, using epidemic membership protocols on a large manufacturing company towards Industry 4.0. The system conversion shows that the epidemic membership protocols provide an ability to rewrite the structure of the overlay topology. The experimental results are presented in two categories: (1) convergence speed and (2) accuracy of the epidemic applications. These provide the information for the performance guarantee of the global aggregate computation. The expectation of this paper is to present a preliminary study focusing on the system conversion methodology in the context of Industry 4.0. There are several recent publications based on Industry 4.0; however, nothing has been done to address any methodologies applied in Industry 4.0 or their simulation results.

[1]  G. Fortino,et al.  Symmetric Push-Sum Protocol for Decentralised Aggregation , 2011, AP2PS 2011.

[2]  Malte Brettel,et al.  How Virtualization, Decentralization and Network Building Change the Manufacturing Landscape: An Industry 4.0 Perspective , 2014 .

[3]  Halenar Igor,et al.  Proposal of communication standardization of industrial networks in Industry 4.0 , 2016, 2016 IEEE 20th Jubilee International Conference on Intelligent Engineering Systems (INES).

[4]  Salvatore Cuomo,et al.  A computational scheme to predict dynamics in IoT systems by using particle filter , 2017, Concurr. Comput. Pract. Exp..

[5]  Abbas Jamalipour,et al.  An Epidemic P2P Content Search Mechanism for Intermittently Connected Mobile Ad Hoc Networks , 2009, GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference.

[6]  Giancarlo Fortino,et al.  Epidemic K-Means Clustering , 2011, 2011 IEEE 11th International Conference on Data Mining Workshops.

[7]  Piet Van Mieghem,et al.  Modeling gossip-based content dissemination and search in distributed networking , 2011, Comput. Commun..

[8]  Soummya Kar,et al.  Gossip Algorithms for Distributed Signal Processing , 2010, Proceedings of the IEEE.

[9]  Johannes Gehrke,et al.  Gossip-based computation of aggregate information , 2003, 44th Annual IEEE Symposium on Foundations of Computer Science, 2003. Proceedings..

[10]  Jafar Habibi,et al.  GoSCAN: Decentralized scalable data clustering , 2012, Computing.

[11]  Valentin Cristea,et al.  Epidemic-Style Global Load Monitoring in Large-Scale Overlay Networks , 2010, 2010 International Conference on P2P, Parallel, Grid, Cloud and Internet Computing.

[12]  Spyros Makridakis,et al.  The M3-Competition: results, conclusions and implications , 2000 .

[13]  Jean-Marie Gorce,et al.  Joint Estimation and Gossip Averaging for Sensor Network Applications , 2010, IEEE Transactions on Automatic Control.

[14]  Giuseppe Di Fatta,et al.  Connectivity Recovery in Epidemic Membership Protocols , 2015, IDCS.

[15]  Stephen P. Boyd,et al.  Randomized gossip algorithms , 2006, IEEE Transactions on Information Theory.

[16]  Jean Mayo,et al.  MuON: Epidemic based mutual anonymity in unstructured P2P networks , 2008, Comput. Networks.

[17]  Márk Jelasity,et al.  PeerSim: A scalable P2P simulator , 2009, 2009 IEEE Ninth International Conference on Peer-to-Peer Computing.

[18]  Giuseppe Di Fatta,et al.  Scalable and Fault Tolerant Failure Detection and Consensus , 2015, EuroMPI.

[19]  Michael Lang,et al.  A gossip-based approach to exascale system services , 2013, ROSS '13.

[20]  Robbert van Renesse,et al.  A Gossip-Style Failure Detection Service , 2009 .

[21]  Yu-Chi Liu,et al.  Advanced manufacturing solution to industry 4.0 trend through sensing network and Cloud Computing technologies , 2014, 2014 IEEE International Conference on Automation Science and Engineering (CASE).

[22]  Sanjay Ranka,et al.  Aggregation methods for large-scale sensor networks , 2008, TOSN.

[23]  George Pavlou,et al.  An adaptive clustering approach for the management of dynamic systems , 2005, IEEE Journal on Selected Areas in Communications.

[24]  Giuseppe Di Fatta,et al.  Expansion Quality of Epidemic Protocols , 2014, IDC.

[25]  Márk Jelasity,et al.  Gossip-based aggregation in large dynamic networks , 2005, TOCS.

[26]  Petr Novotný.  Fault localization in service-based systems hosted in mobile ad hoc networks , 2013 .

[27]  Jinho Ahn,et al.  Gossip-Pull System-based Dissemination Protocol Satisfying Message Causality Condition , 2013, CSE 2013.

[28]  Wilfried N. Gansterer,et al.  Fault Tolerance Properties of Gossip-Based Distributed Orthogonal Iteration Methods , 2013, ICCS.

[29]  Amin Vahdat,et al.  Epidemic Routing for Partially-Connected Ad Hoc Networks , 2009 .

[30]  Giancarlo Fortino,et al.  Gossiping-Based AODV for Wireless Sensor Networks , 2013, 2013 IEEE International Conference on Systems, Man, and Cybernetics.

[31]  Márk Jelasity,et al.  An approach to massively distributed aggregate computing on peer-to-peer networks , 2004, 12th Euromicro Conference on Parallel, Distributed and Network-Based Processing, 2004. Proceedings..