Smart Application using MQTT protocol for Industrial IoT and Retail
Keywords:
MQTT Protocol, Industrial Internet of Things, Internet of Things, IoT, Industry 4.0, Real-time Data AnalyticsAbstract
The advent of Industry 4.0 has propelled the integration of smart technologies into various sectors, with the Industrial Internet of Things (IIoT) playing a pivotal role in revolutionizing processes and operations. This paper introduces a smart application leveraging the Message Queuing Telemetry Transport (MQTT) protocol to enhance connectivity in both industrial and retail settings. The proposed solution capitalizes on MQTT's lightweight and efficient communication model, facilitating seamless data exchange between devices, sensors, and control systems. In the industrial context, the application optimizes manufacturing processes, monitoring equipment performance, and facilitating predictive maintenance through real-time data analytics. In the retail sector, it enables intelligent inventory management, personalized customer experiences, and efficient supply chain coordination. The MQTT protocol ensures low-latency communication, minimizing data transfer delays and contributing to a responsive and agile system. Additionally, the application incorporates advanced security measures to safeguard sensitive data, ensuring the integrity and confidentiality of information exchanged. Case studies and performance evaluations illustrate the practical implementation and benefits of the smart application in real-world scenarios, validating its effectiveness in optimizing processes, reducing downtime, and improving overall operational efficiency.
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References
S. G. Kumar, S. Prince, and B. M. Shankar, "Smart tracking and monitoring in supply chain systems using RFID and BLE," in 2021 3rd International Conference on Signal Processing and Communication (ICPSC), 2021: IEEE, pp. 757-760.
Y. Khan, M. B. M. Su’ud, M. M. Alam, S. F. Ahmad, A. Y. B. Ahmad, and N. Khan, "Application of Internet of Things (IoT) in Sustainable Supply Chain Management," Sustainability, vol. 15, no. 1, p. 694, 2022.
S. M. Wagner and C. Postel, "Using the right digital technologies for supply chain visibility," ISE Magazine, vol. 54, no. 8, pp. 32-37, 2022.
S. Shekhawat, "Decentralized Pricing on Mobile Phone-based ESLs," in 2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile, Analytics, and Cloud)(I-SMAC), 2022: IEEE, pp. 245-249.
A. Haroon, M. Basharat, A. M. Khattak, and W. Ejaz, "Internet of things platform for transparency and traceability of food supply chain," in 2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), 2019: IEEE, pp. 0013-0019.
N. K. Gupta, Inside Bluetooth low energy. Artech House, 2016.
R. BUDI, "Drone-based Wireless Sensor Systems Using Zigbee and RFID Technology."
U. Olgun, C.-C. Chen, and J. L. Volakis, "Wireless power harvesting with planar rectennas for 2.45 GHz RFIDs," in 2010 URSI International symposium on electromagnetic theory, 2010: IEEE, pp. 329-331.
S. Krishnan and R. X. Mendoza Santos, "Real-time asset tracking for Smart Manufacturing," Implementing Industry 4.0: The Model Factory as the Key Enabler for the Future of Manufacturing, pp. 25-53, 2021.
R. Vallabhaneni, A. Maroju, S. A. Vaddadi, and S. Dontu, "An Empirical Paradigm on Cybersecurity Vulnerability Mitigation Framework."
M. K. Gowri, "Impact of the Internet of Things (IoT) on Logistics," Journal of Image Processing and Intelligent Remote Sensing (JIPIRS) ISSN 2815-0953, vol. 3, no. 01, pp. 1-10, 2023.
A. P. Singh and P. Tomar, "AI and IoT capabilities: Standards, procedures, applications, and protocols," in Artificial Intelligence to Solve Pervasive Internet of Things Issues: Elsevier, 2021, pp. 67-83.
B. Patel, J. Vasa, and P. Shah, "IoT concepts, characteristics, enabling technologies, applications, and protocol stack: issues and imperatives," International Journal of Wireless and Mobile Computing, vol. 25, no. 4, pp. 397-406, 2023.
H. Tahir, A. Kanwer, and M. Junaid, "Internet of Things (IoT): An overview of applications and security issues regarding implementation," Int. J. Multidiscip. Sci. Eng, vol. 7, no. 1, pp. 14-22, 2016.
Y. Rubin, "Description and Assessment of a Modern Inventory Management Tool for the ISS," in 54th International Astronautical Congress of the International Astronautical Federation, the International Academy of Astronautics, and the International Institute of Space Law, p. T. 2.08.
Singh, Amarjeet, et al. "Improving Business deliveries using Continuous Integration and Continuous Delivery using Jenkins and an Advanced Version control system for Microservices-based system." 2022 5th International Conference on Multimedia, Signal Processing and Communication Technologies (IMPACT). IEEE, 2022.
Singh, Amarjeet, et al. "Event Driven Architecture for Message Streaming data driven Microservices systems residing in distributed version control system." 2022 International Conference on Innovations in Science and Technology for Sustainable Development (ICISTSD). IEEE, 2022.
Pargaonkar, Shravan. "Bridging the Gap: Methodological Insights from Cognitive Science for Enhanced Requirement Gathering." Journal of Science & Technology 1.1 (2020): 61-66.
Raparthi, Mohan, Sarath Babu Dodda, and SriHari Maruthi. "Examining the use of Artificial Intelligence to Enhance Security Measures in Computer Hardware, including the Detection of Hardware-based Vulnerabilities and Attacks." European Economic Letters (EEL) 10.1 (2020).
Pargaonkar, Shravan. "A Review of Software Quality Models: A Comprehensive Analysis." Journal of Science & Technology 1.1 (2020): 40-53.
Reddy, Surendranadha Reddy Byrapu, and Surendranadha Reddy. "Large Scale Data Influences Based on Financial Landscape Using Big Data." Tuijin Jishu/Journal of Propulsion Technology 44.4 (2023): 3862-3870.
Ding, Liang, et al. "Understanding and improving lexical choice in non-autoregressive translation." arXiv preprint arXiv:2012.14583 (2020).
Pargaonkar, Shravan. "Bridging the Gap: Methodological Insights from Cognitive Science for Enhanced Requirement Gathering." Journal of Science & Technology 1.1 (2020): 61-66.
Raparthi, Mohan, Sarath Babu Dodda, and Srihari Maruthi. "AI-Enhanced Imaging Analytics for Precision Diagnostics in Cardiovascular Health." European Economic Letters (EEL) 11.1 (2021).
Reddy, Byrapu, and Surendranadha Reddy. "Demonstrating The Payroll Reviews Based On Data Visualization For Financial Services." Tuijin Jishu/Journal of Propulsion Technology 44.4 (2023): 3886-3893.
Pargaonkar, Shravan. "Future Directions and Concluding Remarks Navigating the Horizon of Software Quality Engineering." Journal of Science & Technology 1.1 (2020): 67-81.
Ding, Liang, Di Wu, and Dacheng Tao. "Improving neural machine translation by bidirectional training." arXiv preprint arXiv:2109.07780 (2021).
Pargaonkar, Shravan. "Quality and Metrics in Software Quality Engineering." Journal of Science & Technology 2.1 (2021): 62-69.
Raparthi, Mohan, et al. "AI-Driven Metabolmics for Precision Nutrition: Tailoring Dietary Recommendations based on Individual Health Profiles." European Economic Letters (EEL) 12.2 (2022): 172-179.
Pargaonkar, Shravan. "The Crucial Role of Inspection in Software Quality Assurance." Journal of Science & Technology 2.1 (2021): 70-77.
Byrapu, Surendranadha Reddy. "Big Data Analysis in Finance Management." JOURNAL OF ALGEBRAIC STATISTICS 14.1 (2023): 142-149.
Ding, Liang, Longyue Wang, and Dacheng Tao. "Self-attention with cross-lingual position representation." arXiv preprint arXiv:2004.13310 (2020).
Pargaonkar, Shravan. "Unveiling the Future: Cybernetic Dynamics in Quality Assurance and Testing for Software Development." Journal of Science & Technology 2.1 (2021): 78-84.
Raparthi, Mohan, et al. "AI-Driven Metabolmics for Precision Nutrition: Tailoring Dietary Recommendations based on Individual Health Profiles." European Economic Letters (EEL) 12.2 (2022): 172-179.
Ding, Liang, et al. "Rejuvenating low-frequency words: Making the most of parallel data in non-autoregressive translation." arXiv preprint arXiv:2106.00903 (2021).
Pargaonkar, Shravan. "Unveiling the Challenges, A Comprehensive Review of Common Hurdles in Maintaining Software Quality." Journal of Science & Technology 2.1 (2021): 85-94.
Raparthy, Mohan, and Babu Dodda. "Predictive Maintenance in IoT Devices Using Time Series Analysis and Deep Learning." Dandao Xuebao/Journal of Ballistics 35: 01-10.
Ding, Liang, et al. "Context-aware cross-attention for non-autoregressive translation." arXiv preprint arXiv:2011.00770 (2020).
Raparthi, Mohan. "Biomedical Text Mining for Drug Discovery Using Natural Language Processing and Deep Learning." Dandao Xuebao/Journal of Ballistics 35.
Ding, Liang, et al. "Redistributing low-frequency words: Making the most of monolingual data in non-autoregressive translation." Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022.
Raparthi, Mohan. "Predictive Maintenance in Manufacturing: Deep Learning for Fault Detection in Mechanical Systems." Dandao Xuebao/Journal of Ballistics 35: 59-66
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