5G-Enabled Fault Detection and Diagnostics: How Do We Achieve Efficiency?

The fifth-generation (5G) wireless network technologies and mobile-edge computing (MEC) provide great promises of enabling new capabilities for the industrial Internet of Things (IoT). However, the solutions enabled by the 5G ultrareliable low-latency communication (URLLC) paradigm come with challenges, where URLLC alone does not necessarily guarantee the efficient execution of time-critical fault detection and diagnostics (FDD) applications. Based on the Tennessee Eastman (TE) process model, we propose the concept of the communication-edge-computing (CEC) loop and a system model for evaluating the efficiency of FDD applications. We then formulate an optimization problem for achieving the defined CEC efficiency and discuss some typical solutions to the generic CEC-based FDD services (FDDS) and propose a new uplink (UL)-based communication protocol called “ReFlexUp.” From the performance analysis and numerical results, the proposed ReFlexUp protocol shows its effectiveness compared to the typical protocols, such as Selective Repeat automatic repeat request (ARQ), hybrid ARQ (HARQ), and “Occupy CoW” in terms of the key metrics, such as latency, reliability, and efficiency. These results are further convinced from the mmWave-based simulations in a typical 5G MEC-based implementation.

[1]  Maria Rita Palattella,et al.  Using IEEE 802.15.4e Time-Slotted Channel Hopping (TSCH) in the Internet of Things (IoT): Problem Statement , 2015, RFC.

[2]  Ming Jiang,et al.  Low-Rate PBRL-LDPC Codes for URLLC in 5G , 2018, IEEE Wireless Communications Letters.

[3]  Theodore S. Rappaport,et al.  Omnidirectional path loss models in New York City at 28 GHz and 73 GHz , 2014, 2014 IEEE 25th Annual International Symposium on Personal, Indoor, and Mobile Radio Communication (PIMRC).

[4]  Petar Popovski,et al.  Ultra-Reliable Low Latency Communication Using Interface Diversity , 2017, IEEE Transactions on Communications.

[5]  PROPAGATION DATA AND PREDICTION METHODS FOR THE PLANNING OF INDOOR RADIOCOMMUNICATION SYSTEMS AND RADIO LOCAL AREA NETWORKS IN THE FREQUENCY RANGE 900 MHz TO 100 GHz , 1997 .

[6]  Bo Ai,et al.  Coded Tandem Spreading Multiple Access for Massive Machine-Type Communications , 2018, IEEE Wireless Communications.

[7]  Ping Zhang,et al.  A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process , 2012 .

[8]  Peng Hu.  A System Architecture for Software-Defined Industrial Internet of Things , 2015, 2015 IEEE International Conference on Ubiquitous Wireless Broadband (ICUWB).

[9]  Nihar Jindal,et al.  Coding versus ARQ in Fading Channels: How Reliable Should the PHY Be? , 2009, IEEE Transactions on Communications.

[10]  Michael S. Berger,et al.  Cloud RAN for Mobile Networks—A Technology Overview , 2015, IEEE Communications Surveys & Tutorials.

[11]  Sundeep Rangan,et al.  Achieving Ultra-Low Latency in 5G Millimeter Wave Cellular Networks , 2016, IEEE Communications Magazine.

[12]  Jiabin Wang,et al.  A Survey on Mobile Edge Computing: Focusing on Service Adoption and Provision , 2018, Wirel. Commun. Mob. Comput..

[13]  E. F. Vogel,et al.  A plant-wide industrial process control problem , 1993 .

[14]  Yulin Hu,et al.  Relaying-Enabled Ultra-Reliable Low-Latency Communications in 5G , 2018, IEEE Network.

[15]  Bikramjit Singh,et al.  Contention-Based Access for Ultra-Reliable Low Latency Uplink Transmissions , 2018, IEEE Wireless Communications Letters.

[16]  Pingyu Jiang,et al.  RFID-based production data analysis in an IoT-enabled smart job-shop , 2018, IEEE/CAA Journal of Automatica Sinica.

[17]  Eric Levrat,et al.  Information and Communication Technologies Within E-maintenance , 2010 .

[18]  Zdenek Becvar,et al.  Mobile Edge Computing: A Survey on Architecture and Computation Offloading , 2017, IEEE Communications Surveys & Tutorials.

[19]  Krzysztof Wesolowski,et al.  Channel Coding for Ultra-Reliable Low-Latency Communication in 5G Systems , 2016, 2016 IEEE 84th Vehicular Technology Conference (VTC-Fall).

[20]  Zexian Li,et al.  Optimized transmission and resource allocation strategies for ultra-reliable communications , 2016, 2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).

[21]  Wei Yu,et al.  A D2D-Based Protocol for Ultra-Reliable Wireless Communications for Industrial Automation , 2017, IEEE Transactions on Wireless Communications.

[22]  Xingqin Lin,et al.  Overview of 3GPP Release 14 Enhanced NB-IoT , 2017, IEEE Network.

[23]  Jin Cao,et al.  Revisiting Computation Partitioning in Future 5G-Based Edge Computing Environments , 2019, IEEE Internet of Things Journal.

[24]  Jiann-Liang Chen,et al.  Mobile Edge Computing Platform with Container-Based Virtualization Technology for IoT Applications , 2018, Wirel. Pers. Commun..

[25]  Chunlin Yan,et al.  On uplink non-orthogonal multiple access for 5g: opportunities and challenges , 2017, China Communications.

[26]  Alois Knoll,et al.  5G for Robotics: Ultra-Low Latency Control of Distributed Robotic Systems , 2017, 2017 International Symposium on Computer Science and Intelligent Controls (ISCSIC).

[27]  Maria Rita Palattella,et al.  Using IEEE802.15.4e TSCH in an LLN context: Overview, Problem Statement and Goals , 2013 .

[28]  Anant Sahai,et al.  Real-Time Cooperative Communication for Automation Over Wireless , 2016, IEEE Transactions on Wireless Communications.

[29]  Liang Tong,et al.  A hierarchical edge cloud architecture for mobile computing , 2016, IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications.

[30]  Harish Viswanathan,et al.  Variable-rate ultra-reliable and low-latency communication for industrial automation , 2018, 2018 52nd Annual Conference on Information Sciences and Systems (CISS).