Volume 2 Issue 2 | 2025 | View PDF
Paper Id:IJMSM-V2I2P122
doi: 10.71141/30485037/V2I2P122
Reliable Message Delivery in Distributed Edge-Cloud Systems: A Comprehensive Survey
Manojava Bharadwaj Bhagavathula
Citation:
Manojava Bharadwaj Bhagavathula, "Reliable Message Delivery in Distributed Edge-Cloud Systems: A Comprehensive Survey" International Journal of Multidisciplinary on Science and Management, Vol. 2, No. 2, pp. 234-237, 2025.
Abstract:
The reliable delivery of messages from edge devices to distributed cloud infrastructure is a foundational requirement for modern industrial, automotive, and consumer IoT applications. Edge environments are characterised by intermittent connectivity, bandwidth constraints, and resource limitations, making standard reliable transport protocols like TCP insufficient for application-level data integrity. This paper presents a comprehensive survey of protocols, architectural patterns, and state-of-the-art mechanisms for ensuring reliable message delivery in distributed edge-cloud systems. The article analyse key protocols, including MQTT, AMQP, CoAP, and QUIC, and evaluates their reliability mechanisms, such as Quality of Service (QoS) levels, persistent sessions, and stream multiplexing. Furthermore, the paper surveys the architectural patterns such as Store-and-Forward, application-level acknowledgements, and idempotency mechanisms that operate above the transport layer. Through a comparative analysis, the trade-offs identified among throughput, latency, and delivery guarantees provide a decision framework for system architects. Finally, emerging trends discussed in AI-driven connectivity management and decentralized consensus for message integrity.
Keywords:
Edge Computing; Reliable Messaging; Distributed Systems; MQTT; AMQP; Store-And-Forward; IoT; Network Resilience.
References:
1. Weisong Shi et al., “Edge Computing: Vision and Challenges, IEEE Internet of Things Journal, vol. 3, no. 5, pp. 637 - 646, 2016.
2. OASIS, MQTT Version 5.0. OASIS Standard, 2019. [Online]. Available: https://docs.oasis-open.org/mqtt/mqtt/v5.0/mqtt-v5.0.html
3. OASIS, Advanced Message Queuing Protocol (AMQP) Version 1.0, 2012. [Online]. Available: https://www.oasis-open.org/standard/amqp/
4. Z. Shelby, K.Hartke, and C. Bormann, The Constrained Application Protocol (CoAP) (RFC 7252), Internet Engineering Task Force (IETF), 2014. [Online]. Available: https://datatracker.ietf.org/doc/html/rfc7252
5. J. Iyengar, and M. Thomson, QUIC: A UDP-Based Multiplexed and Secure Transport (RFC 9000), Internet Engineering Task Force (IETF), 2021. [Online]. Available: https://datatracker.ietf.org/doc/html/rfc9000
6. Flavio Bonomi et al., “Fog Computing and its Role in the Internet of Things,” MCC '12: Proceedings of the first edition of the MCC workshop on Mobile cloud computing, pp. 13-16, 2012.
7. Biswajeeban Mishra, and Attila Kertesz, “The Use of MQTT in M2M and IoT Systems: A Survey,” IEEE Access, vol. 8, pp. 201071–201086, 2020.
8. Dinesh Thangavel et al., “Performance evaluation of MQTT and CoAP via a common middleware,” 2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), Singapore, pp. 1-6, 2014. Publisher Link
9. J. Wang, “A Kafka-based real-time data acquisition and processing system for industrial Internet of Things,” IEEE Access, vol. 9, pp. 156321–156333, 2021.
10. S.K. Gupta, and P.K.Singh, “Store and forward mechanism for reliable data delivery in IoT,” Proc. ICCCS, pp. 45–52, 2019.
11. L. M. Contreras, and S. Baliosian, “QUIC as a Potential Enabler for 5G Low Latency Services,” Proc. IEEE Conference on Standards for Communications and Networking, 2018.
12. Angelo Corsaro, and Orsini O. H. M, “Zenoh: Zero overhead pub/sub, store/query and computations,” In Proc. IEEE International Conference on Edge Computing, 2021.
13. Li, Y et al., “Data Redundancy Elimination in Edge Storage Systems for IoT,” IEEE Transactions on Parallel and Distributed Systems, vol. 32, no. 1, pp. 123–136, 2021.
14. T.M. Fernandez, A. Alchieri, and A.D. Bessani, “A Survey on Consensus Protocols for Edge Computing,” ACM Computing Surveys, vol. 54, no. 2, pp. 1–36, 2021.
15. Mingzhe Chen et al., “Artificial neural networks-based machine learning for wireless networks: A tutorial,” IEEE Communications Surveys & Tutorials, vol. 21, no. 4, pp. 3039–3071, 2019.