基于雾计算的制造物联网数据处理技术综述

doi:10.3969/j.issn.1006-2475.2024.01.003

摘要/Abstract

摘要： 摘要：制造物联网（MIoT）是一种将制造业生产系统与互联网连接相结合的技术，数据处理更是在制造物联网中发挥着至关重要的作用。随着制造业规模的不断扩大，传统的云计算已经逐渐不能满足数据处理的需求，雾计算的发展则能有效地减少决策延迟，提高系统效率。本文概述基于雾计算的制造物联网数据处理技术，首先介绍MIoT数据的产生、特点，以及数据处理过程中所要面对的挑战。其次，介绍基于雾计算的MIoT数据处理架构。然后，介绍雾计算数据处理的关键技术。最后，介绍该架构在部署时需要面对的挑战，以及雾计算在MIoT中应用的未来发展方向。

关键词: 关键词：MIoT, 数据处理技术, 雾计算, 数据安全

Abstract: Abstract：Manufacturing Internet of Things （MIoT） is a kind of technology that combines manufacturing production system with Internet connection. Data processing plays a crucial role in MIoT. With the continuous expansion of manufacturing scale， traditional cloud computing has gradually failed to meet the needs of data processing， while the development of fog computing can effectively reduce decision delay and improve system efficiency. This paper summarizes the MIoT data processing technology based on fog computing. Firstly， the generation and characteristics of MIoT data are introduced， as well as the challenges to be faced in the data processing process. Secondly， the MIoT data processing architecture based on fog computation is introduced. Then， the key techniques of data processing in fog computation are introduced. Finally， it introduces the challenges to be faced in the deployment of the architecture and the future direction of fog computing in MIoT.

Key words: Key words：MIoT（Manufacturing Internet of Things）, data processing technology, fog calculation, data security

中图分类号:

TP274

韩坤, 王政, 段俊勇, 杨化林. 基于雾计算的制造物联网数据处理技术综述[J]. 计算机与现代化, 2024, 0(01): 13-20.

HAN Kun, WANG Zheng, DUAN Jun-yong, YANG Hua-lin. Overview of Data Processing Techniques for MIoT Based on Fog Computing[J]. Computer and Modernization, 2024, 0(01): 13-20.

参考文献

［1］ CARVALHO N， CHAIM O， CAZARINI E， et al. Manufacturing in the fourth industrial revolution：A positive prospect in sustainable manufacturing［J］. Procedia Manufacturing， 2018，21（4）:671-678.
［2］ DRATH R， HORCH A. Industrie 4.0： Hit or hype? ［Industry Forum］［J］. IEEE Industrial Electronics Magazine， 2014，8（2）:56-58.
［3］ WAN J F， TANG S L， SHU Z G， et al. Software-defined industrial Internet of things in the context of industry 4.0［J］. IEEE Sensors Journal， 2016，16（20）:7373-7380.
［4］ DAI H N， WANG H， XU G， et al. Big data analytics for manufacturing Internet of things： Opportunities， challenges and enabling technologies ［J］. Enterprise Information Systems， 2020，14（9-10）:1279-1303.
［5］任语铮，谢人超，曾诗钦，等. 工业互联网标识解析体系综述［J］. 通信学报， 2019，40（11）:138-155.
［6］ CHEN B T， WAN J F. Emerging trends of ML-based intelligent services for industrial internet of things （IIoT）［C］// 2019 Computing， Communications and IoT Applications （ComComAp）. 2019:135-139.
［7］ HU P F， DHELIM S. Survey on fog computing：Architecture， key technologies， applications and open issues［J］. Journal of Network and Computer Applications， 2017，98:27-42.
［8］ EL-SAYED H， SANKAR S， PRASAD M， et al. Edge of things：The big picture on the integration of edge， IoT and the cloud in a distributed computing environment［J］. IEEE Access， 2018，6:1706-1717.
［9］ JIA B Q， HU H L， ZENG Y， et al. Double-matching resource allocation strategy in fog computing networks based on cost efficiency［J］. Journal of Communications and Networks， 2018，20（3）:237-246.
［10］ ABDULQADIR H R， ZEEBAREE S R， SHUKUR H M， et al. A study of moving from cloud computing to fog computing ［J］. Qubahan Academic Journal， 2021，1（2）：60-70.
［11］ YOUSEFPOUR A， ISHIGAKI G， JUE J P. Fog computing：Towards minimizing delay in the Internet of things［C］// Proceedings of the 2017 IEEE International Conference on Edge Computing （EDGE）. 2017:17-24.
［12］ KUMAR V， LAGHARI A A， KARIM S， et al. Comparison of fog computing & cloud computing［J］. International Journal of Mathematical Sciences and Computing， 2019，5（1）:31-41.
［13］ SCANZIO S， WISNIEWSKI L， GAJ P. Heterogeneous and dependable networks in industry：A survey［J］. Computers in Industry，2021，125（4）. DOI：10.1016/j.compind.2020.
103388.
［14］ CUI Y S， KARA S， CHAN K C. Manufacturing big data ecosystem：A systematic literature review［J］. Robotics and Computer-Integrated Manufacturing， 2019，62（1）. DOI:10.1016/j.rcim.2019.101861.
［15］ MUNIRATHINAM S. Chapter six-industry 4.0：Industrial Internet of things （IIOT）［J］. Advances in Computers， 2020，117（1）:129-164.
［16］ WANG J L， ZHENG P， ZHANG J. Big data analytics for cycle time related feature selection in the semiconductor wafer fabrication system［J］. Computers & Industrial Engineering， 2020，143. DOI： 10.1016/j.cie.2020.106362.
［17］ XU C Q， WANG J L， ZHANG J. Forecasting the power consumption of a rotor spinning machine by using an adaptive squeeze and excitation convolutional neural network with imbalanced data［J］. Journal of Cleaner Production， 2020，275（14）. DOI： 10.1016/j.jclepro.2020.122864.
［18］ LEEVY J L， KHOSHGOFTAAR T M， BAUDER R A. et al. A survey on addressing high-class imbalance in big data［J］. Journal of Big Data， 2018，5（1）. DOI：10.1186/s40537-018-0151-6.
［19］ BOOIJ T M， CHISCOP I， MEEUWISSEN E， et al. ToN_IoT：The role of heterogeneity and the need for standardization of features and attack types in iot network intrusion data sets［J］. IEEE Internet of Things Journal， 2022，9（1）:485-496.
［20］ ERTEK G， CHI X， ZHANG A N. A framework for mining rfid data from schedule-based systems［J］. IEEE Transactions on Systems， Man， and Cybernetics：Systems， 2017，47（11）:2967-298.
［21］ LIU C， SU Z W， XU X， et al. Service-oriented industrial Internet of things gateway for cloud manufacturing［J］. Robotics and Computer-Integrated Manufacturing， 2022，73. DOI： 10.1016/j.rcim.2021.102217.
［22］ LEE J， KAO H A， YANG S. Service innovation and smart analytics for industry 4.0 and big data environment ［J］. Procedia CIRP， 2014，16:3-8.
［23］ MUMTAZ S， ALSOHAILY A， PANG Z， et al. Massive Internet of things for industrial applications：Addressing wireless IIoT connectivity challenges and ecosystem fragmentation［J］. IEEE Industrial Electronics Magazine， 2017，11（1）:28-33.
［24］ DAI H N， WONG R C， WANG H， et al. Big data analytics for large-scale wireless networks：Challenges and opportunities［J］. ACM Computing Surveys， 2019，52（5）:1-36.
［25］ AZOIDOU E， PANG Z B， LIU Y， et al. Battery lifetime modeling and validation of wireless building automation devices in thread［J］. IEEE Transactions on Industrial Informatics， 2018，14（7）:2869-2880.
［26］ HARB H， JAOUDE C A， LAIYMANI D， et al. A multi-tier data prediction mechanism for the Internet of things networks ［J］. Wireless Personal Communications， 2022，127（4）:1-34.
［27］ XU H S， YU W， GRIFFITH D， et al. A survey on industrial Internet of things：A cyber-physical systems perspective［J］. IEEE Access， 2018，6:78238-78259.
［28］ WANG J， CHEN J H， REN Y J， et al. Data security storage mechanism based on blockchain industrial Internet of things［J］. Computers & Industrial Engineering， 2021，164（2）. DOI: 10.1016/j.cie.2021.107903.
［29］ NGUYEN D C， DING M， PATHIRANA P N， et al. Federated learning for industrial Internet of things in future industries［J］. IEEE Wireless Communications， 2021，28（6）:192-199.
［30］ PENG Y H， TAN A P， WU J J， et al. Hierarchical edge computing：A novel multi-source multi-dimensional data anomaly detection scheme for industrial Internet of things［J］. IEEE Access， 2019，7:111257-111270.
［31］ ZHONG Y， CHEN L， DAN C L， et al. A systematic survey of data mining and big data analysis in Internet of things［J］. The Journal of Supercomputing， 2022，78（17）:18405-18453.
［32］ THAKUR A， MALEKIAN R. Fog computing for detecting vehicular congestion， an Internet of vehicles based approach：A review［J］. IEEE Intelligent Transportation Systems Magazine， 2019，11（2）:8-16.
［33］ YOUNG R， FALLON S， JACOB P. Dynamic collaboration of centralized & edge processing for coordinated data management in an IoT paradigm［C］// Proceedings of the 2018 IEEE 32nd International Conference on Advanced Information Networking and Applications （AINA）. 2018:694-701.
［34］ SHAMSI J A， HANIF M， ZEADALLY S. Fog computing：Data analytics for time-sensitive applications［M// Convergence of Artificial Intelligence and the Internet of Things. Springer. 2020:1-13.
［35］ PAREEK K， TIWARI P K， BHATNAGAR V. Fog computing in healthcare：A review［C］// Proceedings of the IOP Conference Series：Materials Science and Engineering. 2021. DOI: 10.1088/1757-899X/1099/1/012025.
［36］ QI Q L， TAO F. A smart manufacturing service system based on edge computing， fog computing， and cloud computing［J］. IEEE Access， 2019，7:86769-86777.
［37］ MUKHERJEE M， SHU L， WANG D. Survey of fog computing：Fundamental， network applications， and research challenges［J］. IEEE Communications Surveys & Tutorials， 2018，20（3）:1826-1857.
［38］ PUTHAL D， MOHANTY S P， BHAVAKE S A， et al. Fog computing security challenges and future directions ［energy and security］［J］. IEEE Consumer Electronics Magazine， 2019，8（3）:92-96.
［39］ CHIANG M， HA S T， RISSO F， et al. Clarifying fog computing and networking：10 questions and answers［J］. IEEE Communications Magazine， 2017，55（4）:18-20.
［40］ ZHOU Z Y， LIAO H J， GU B， et al. Robust mobile crowd sensing：When deep learning meets edge computing［J］. IEEE Network， 2018，32（4）:54-60.
［41］ RANA B， SINGH Y， SINGH P K. A systematic survey on Internet of things：Energy efficiency and interoperability perspective［J］. Transactions on Emerging Telecommunications Technologies， 2021，32（3）. DOI: 10.1002/ett.4166.
［42］ HERRERA J L， GALÉN-JIMÉNEZ J， BERROCAL J， et al. Optimizing the response time in SDN-fog environments for time-strict IoT applications［J］. IEEE Internet of Things Journal， 2021，8（23）:17172-17185.
［43］ LI X M， ZHU L X， CHU X， et al. Edge computing-enabled wireless sensor networks for multiple data collection tasks in smart agriculture［J］. Journal of Sensors， 2020，2020（1）:1-9.
［44］ CHA H J， YANG H K， SONG Y J. A study on the design of fog computing architecture using sensor networks［J］. Sensors， 2018，18（11）. DOI: 10.3390/s18113633.
［45］ FU J S， LIU Y， CHAO H C， et al. Secure data storage and searching for industrial IoT by integrating fog computing and cloud computing［J］. IEEE Transactions on Industrial Informatics， 2018，14（10）:4519-4528.
［46］ BENHAR H， IDRI A， FERNáNDEZ-ALEMáN J L. Data Preprocessing for decision making in medical informatics：Potential and analysis［C］// Proceedings of the World Conference on Information Systems and Technologies. 2018:1208-1218.
［47］ MUBARAKALI A， DURAI A D， ALSHEHRI M， et al. Fog-based delay-sensitive data transmission algorithm for data forwarding and storage in cloud environment for multimedia applications［J］. Big Data， 2020，11（2）. DOI：10.10
89/big.2020.0090.
［48］ XU S D， CHEN C H， CHANG T C. Design of oneM2M-based fog computing architecture［J］. IEEE Internet of Things Journal， 2019，6（6）:9464-9474.
［49］ TEDESCHI P， SCIANCALEPORE S. Edge and fog computing in critical infrastructures：Analysis， security threats， and research challenges［C］// Proceedings of the 2019 IEEE European Symposium on Security and Privacy Workshops （EuroS&PW）. 2019：1-10.
［50］ GHOSH S， CHANDRA V， ADHYA A. Intelligent Internet of things for healthcare and industry［M］// Machine Learning for Fog Computing-Based IoT Networks in Smart City Environment. 2022:267-285.
［51］ ELMISERY A M， RHO S， BOTVICH D. A fog based middleware for automated compliance with OECD privacy principles in internet of healthcare things［J］. IEEE Access， 2016，4:8418-8441.
［52］ HU P F， NING H S， QIU T， et al. Security and privacy preservation scheme of face identification and resolution framework using fog computing in Internet of things［J］. IEEE Internet of Things Journal， 2017，4（5）:1143-1155.
［53］ SARKAR S， MISRA S. Theoretical modelling of fog computing：A green computing paradigm to support IoT applications ［J］. IET Networks， 2016，5（2）:23-29.
［54］ HERROJO C， PAREDES F， MATA-CONTRERAS J， et al. Chipless-RFID：A review and recent developments［J］. Sensors， 2019，19（15）. DOI： 10.3390/s19153385.
［55］ LASS S， GRONAU N. A factory operating system for extending existing factories to industry 4.0［J］. Computers in Industry， 2020，115（4）. DOI：10.1016/j.compind.2019.103128.
［56］ KALSOOM T， RAMZAN N， AHMED S， et al. Advances in sensor technologies in the era of smart factory and industry 4.0［J］. Sensors， 2020，20（23）. DOI：10.3390/s20236783.
［57］ LANDALUCE H， ARJONA L， PERALLOS A， et al. A review of IoT sensing applications and challenges using RFID and wireless sensor networks［J］. Sensors， 2020，20（9）. DOI： 10.3390/s20092495.
［58］ MOON Y S， CHOI H， KIM J， et al. Development of IoT-based sensor tag for smart factory ［J］. International Research Journal of Electronics & Computer Engineering， 2017，3（4）:28-31.
［59］ SALAMONE F， DANZA L， MERONI I， et al. A low-cost environmental monitoring system：How to prevent systematic errors in the design phase through the combined use of additive manufacturing and thermographic techniques［J］. Sensors， 2017，17（4）. DOI： 10.3390/s17040828.
［60］ LI J L， XIE J C， YANG Z J， et al. Fault diagnosis method for a mine hoist in the Internet of things environment［J］. Sensors， 2018，18（6）. DOI： 10.3390/s18061920.
［61］ GE M Z， BANGUI H， BUHNOVA B. Big data for Internet of things：A survey［J］. Future Generation Computer Systems， 2018，87:601-614.
［62］ ZANTALIS F， KOULOURAS G， KARABETSOS S， et al. A review of machine learning and IoT in smart transportation［J］. Future Internet， 2019，11（4）. DOI：10.3390/fi11040094.
［63］ LINS R G， GIVIGI S N. Cooperative robotics and machine learning for smart manufacturing：Platform design and trends within the context of industrial Internet of things［J］. IEEE Access， 2021，9:95444-95455.
［64］ BAJIC B， COSIC I， LAZAREVIC M， et al. Machine learning techniques for smart manufacturing：Applications and challenges in industry 4.0［C］// The 9th International Scientific and Expert Conference TEAM. 2018:29-38.
［65］ KUMAR A， JAISWAL A. A deep swarm-optimized model for leveraging industrial data analytics in cognitive manufacturing［J］. IEEE Transactions on Industrial Informatics， 2021，17（4）:2938-2946.
［66］ MA L， DONG J， PENG K X. A novel robust semisupervised classification framework for quality-related coupling faults in manufacturing industries［J］. IEEE Transactions on Industrial Informatics， 2020，16（5）:2946-2955.
［67］ KUSIAK A. Convolutional and generative adversarial neural networks in manufacturing［J］. International Journal of Production Research， 2020，58（5）:1594-1604.
［68］ ARACHCHIGE P C M， BERTOK P， KHALIL I， et al. A trustworthy privacy preserving framework for machine learning in industrial IoT systems［J］. IEEE Transactions on Industrial Informatics， 2020，16（9）:6092-6102.
［69］ QUIROZ J C， MARIUN N， MEHRJOU M R， et al. Fault detection of broken rotor bar in LS-PMSM using random forests［J］. Measurement， 2018，116:273-280.
［70］ PATEL R K， GIRI V K. Feature selection and classification of mechanical fault of an induction motor using random forest classifier［J］. Perspectives in Science， 2016，8:334-337.
［71］ SYAFRUDIN M， ALFIAN G， FITRIYANI N L， et al. Performance analysis of IoT-based sensor， big data processing， and machine learning model for real-time monitoring system in automotive manufacturing［J］. Sensors， 2018，18（9）. DOI： 10.3390/s18092946.
［72］ LEE J N H Y C， NOH S D， KIM H， et al. Implementation of cyber-physical production systems for quality prediction and operation control in metal casting［J］. Sensors， 2018，18（5）. DOI: 10.3390/s18051428.
［73］ CHEN J Q， WANG T， GAO X D， et al. Real-time monitoring of high-power disk laser welding based on support vector machine［J］. Computers in Industry， 2018，94:75-81.
［74］ PARK J， CHI S Y. An implementation of a high throughput data ingestion system for machine logs in manufacturing industry［C］// Proceedings of the 2016 8th International Conference on Ubiquitous and Future Networks （ICUFN）. 2016:117-120:
［75］ FERRY N， TERRAZAS G， KALWEIT P， et al. Towards a big data platform for managing machine generated data in the cloud［C］// Proceedings of the 2017 IEEE 15th International Conference on Industrial Informatics （INDIN）. 2017：263-270.
［76］ ASLANPOUR M S， TOOSI A N， CICCONETTI C， et al. Serverless edge computing：Vision and challenges［C］// Proceedings of the 2021 Australasian Computer Science Week Multiconference. 2021:1-10.
［77］ CAIZA G， SAETEROS M， ONATE W， et al. Fog computing at industrial level， architecture， latency， energy， and security：A review［J］. Heliyon， 2020，6（4）. DOI：10.1016/j.heliyon.2020.e03706.
［78］ WU D Z， ROSEN D W， WANG L H， et al. Cloud-based design and manufacturing：A new paradigm in digital manufacturing and design innovation［J］. Computer-Aided Design， 2015，59:1-14.
［79］ ZHANG C L， SUN Y H， MO Y T， et al. Social-aware content downloading for fog radio access networks supported device-to-device communications［C］// Proceedings of the 2016 IEEE International Conference on Ubiquitous Wireless Broadband （ICUWB）. 2016:1-4.
［80］ CAU E， CORICI M， BELLAVISTA P， et al. Efficient exploitation of mobile edge computing for virtualized 5G in EPC architectures［C］// Proceedings of the 2016 4th IEEE International Conference on Mobile Cloud Computing， Services， and Engineering （MobileCloud）. 2016:100-109.
［81］ PONTOIS N， KANEKO M， DINH T H L， et al. User pre-scheduling and beamforming with outdated CSI in 5G fog radio access networks［C］// Proceedings of the 2018 IEEE Global Communications Conference （GLOBECOM）. 2018:1-6.
［82］ LI L， GUAN Q S， JIN L W， et al. Resource allocation and task offloading for heterogeneous real-time tasks with uncertain duration time in a fog queueing system［J］. IEEE Access， 2019，7:9912-9925.
［83］ MOURADIAN C， NABOULSI D， YANGUI S， et al. A comprehensive survey on fog computing：State-of-the-art and research challenges［J］. IEEE Communications Surveys & Tutorials， 2018，20（1）:416-464.
［84］ MOYSIADIS V， SARIGIANNIDIS P， MOSCHOLIOS I. Towards distributed data management in fog computing［J］. Wireless Communications and Mobile Computing， 2018，2018（1）:1-14.
［85］ SELLAMI B， HAKIRI A， YAHIA S B， et al. Energy-aware task scheduling and offloading using deep reinforcement learning in SDN-enabled IoT network［J］. Computer Networks， 2022，210（3）. DOI:10.1016/j.comnet.2022.108957.
［86］ JAMIL B， SHOJAFAR M， AHMED I， et al. A job scheduling algorithm for delay and performance optimization in fog computing［J］. Concurrency and Computation Practice and Experience， 2020，32（7）. DOI: 10.1002/cpe.5581.

[1]	刘铮, 吴柯桦, 叶春晓. 增强安全的云存储数据访问控制方案[J]. 计算机与现代化, 2021, 0(10): 119-126.
[2]	秦璐璐，周李京，王敏. 支持多关键字搜索的条件代理重加密[J]. 计算机与现代化, 2020, 0(01): 100-.
[3]	方舟，程清，裴旭斌 . 电力营销信息系统数据安全防护[J]. 计算机与现代化, 2019, 0(03): 111-.
[4]	温贺平1,鲍晶晶2,柯居鑫1,刘树威1. 基于Hadoop的Lorenz超混沌加密算法设计[J]. 计算机与现代化, 2018, 0(03): 108-.
[5]	李学斯，王虹，吴飞，林济南. 基于物化视图的Oracle增量备份方法的实现[J]. 计算机与现代化, 2014, 0(1): 168-170.
[6]	孙萍萍. 基于表字段的数据库加密服务器的设计与实现[J]. 计算机与现代化, 2009, 1(11): 6-11,1.