Age-of-Information Analysis for Blockchain-Based Mobile Edge Computing

Mobile edge computing (MEC) has emerged as a disruptive paradigm that facilitates effective offloading from clouds and enables processing tasks near users. With the surge of mobile data traffic and escalating demands for responsive wireless services, it becomes imperative to enhance trust, security, and efficiency within MEC environments. Blockchain technology, renowned for its immutability, transparency, and security, has proven to be a compelling solution for securing data, enhancing supervision, and fostering trusted collaborations among heterogeneous MEC stakeholders. Despite these advantages, integrating blockchain with MEC also introduces a substantial efficiency bottleneck. Specifically, the blockchain consensus process can result in the aging of critical MEC system information, causing users to perform suboptimal service decisions, thus risking a degradation in service performance. To analyze this bottleneck, we first explore a blockchain-based MEC model that ensures secure task processing across diverse stakeholders. We employ the practical Byzantine fault tolerance (PBFT) consensus to validate and share service statuses, providing critical on-chain references for users to select their preferred target MEC servers. We then introduce the age-of-information (AoI) as a metric of freshness to characterize the aging of status reports, identifying variable consensus delay as a key factor affecting AoI. We reveal the critical impact of AoI on MEC service performance through analysis, challenging the notion that a lower AoI always leads to better performance. Finally, we validate our analysis and findings through comprehensive simulations.

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Publication Details

Journal
IEEE Transactions on Communications
Published
2025-08-11
DOI
https://doi.org/10.1109/tcomm.2025.3597823
Primary Topic
Age of Information Optimization
Type
article
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article

Age-of-Information Analysis for Blockchain-Based Mobile Edge Computing

Derrick Wing Kwan Ng, Yuwei Le, Xintong Ling, Yongming Huang et al.
1 links
IEEE Transactions on Communications
Age of Information Optimization
article

Age-of-Information Analysis for Blockchain-Based Mobile Edge Computing

Derrick Wing Kwan Ng, Yuwei Le, Xintong Ling, Yongming Huang, Yiheng Jiang, Jiaheng Wang, Rui Jiang
article en

Abstract

Mobile edge computing (MEC) has emerged as a disruptive paradigm that facilitates effective offloading from clouds and enables processing tasks near users. With the surge of mobile data traffic and escalating demands for responsive wireless services, it becomes imperative to enhance trust, security, and efficiency within MEC environments. Blockchain technology, renowned for its immutability, transparency, and security, has proven to be a compelling solution for securing data, enhancing supervision, and fostering trusted collaborations among heterogeneous MEC stakeholders. Despite these advantages, integrating blockchain with MEC also introduces a substantial efficiency bottleneck. Specifically, the blockchain consensus process can result in the aging of critical MEC system information, causing users to perform suboptimal service decisions, thus risking a degradation in service performance. To analyze this bottleneck, we first explore a blockchain-based MEC model that ensures secure task processing across diverse stakeholders. We employ the practical Byzantine fault tolerance (PBFT) consensus to validate and share service statuses, providing critical on-chain references for users to select their preferred target MEC servers. We then introduce the age-of-information (AoI) as a metric of freshness to characterize the aging of status reports, identifying variable consensus delay as a key factor affecting AoI. We reveal the critical impact of AoI on MEC service performance through analysis, challenging the notion that a lower AoI always leads to better performance. Finally, we validate our analysis and findings through comprehensive simulations.

IEEE Transactions on CommunicationsVol. 73(12)
UNSW Sydney (AU), Purple Mountain Laboratories (CN), Southeast University (CN)
Openalex Percentile: Top 77%
Age of Information Optimization
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