Can Sidechains and AI Save the Edge? A Perspective on Scalability and Security in IoT-Fog Blockchains
Article Information
Abstract
IoT-Fog networks create big challenges in scalability, latency, and security at the edge. This paper gives a simple perspective on how sidechains and artificial intelligence (AI) can help. Sidechains process local transactions near devices, increase throughput, and reduce delay. AI methods, such as Random Forest, detect abnormal traffic, predict workload peaks, and adjust network settings (for example, block time and number of validators). We present clear, small formulas for throughput, latency, checkpoint cost, and security, and we show how these formulas guide design choices (block time $T_b$, confirmations $k$, checkpoint period $\tau$). The goal is an easy model that links design choices to performance and security, helping readers build faster, safer, and more adaptive IoT-Fog Blockchain networks.
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References
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Cite This Article
TY - JOUR AU - Javanmardi, Saeed AU - Scarpa, Marco PY - 2026 DA - 2026/03/23 TI - Can Sidechains and AI Save the Edge? A Perspective on Scalability and Security in IoT-Fog Blockchains JO - Journal of Systems Scalability T2 - Journal of Systems Scalability JF - Journal of Systems Scalability VL - 1 IS - 1 SP - 23 EP - 28 DO - 10.62762/JSS.2025.237121 UR - https://www.icck.org/article/abs/JSS.2025.237121 KW - blockchain KW - sidechain KW - IoT-Fog networks KW - scalability and security KW - artificial intelligence AB - IoT-Fog networks create big challenges in scalability, latency, and security at the edge. This paper gives a simple perspective on how sidechains and artificial intelligence (AI) can help. Sidechains process local transactions near devices, increase throughput, and reduce delay. AI methods, such as Random Forest, detect abnormal traffic, predict workload peaks, and adjust network settings (for example, block time and number of validators). We present clear, small formulas for throughput, latency, checkpoint cost, and security, and we show how these formulas guide design choices (block time $T_b$, confirmations $k$, checkpoint period $\tau$). The goal is an easy model that links design choices to performance and security, helping readers build faster, safer, and more adaptive IoT-Fog Blockchain networks. SN - 3142-7855 PB - Institute of Central Computation and Knowledge LA - English ER -
@article{Javanmardi2026Can,
author = {Saeed Javanmardi and Marco Scarpa},
title = {Can Sidechains and AI Save the Edge? A Perspective on Scalability and Security in IoT-Fog Blockchains},
journal = {Journal of Systems Scalability},
year = {2026},
volume = {1},
number = {1},
pages = {23-28},
doi = {10.62762/JSS.2025.237121},
url = {https://www.icck.org/article/abs/JSS.2025.237121},
abstract = {IoT-Fog networks create big challenges in scalability, latency, and security at the edge. This paper gives a simple perspective on how sidechains and artificial intelligence (AI) can help. Sidechains process local transactions near devices, increase throughput, and reduce delay. AI methods, such as Random Forest, detect abnormal traffic, predict workload peaks, and adjust network settings (for example, block time and number of validators). We present clear, small formulas for throughput, latency, checkpoint cost, and security, and we show how these formulas guide design choices (block time \$T\_b\$, confirmations \$k\$, checkpoint period \$\tau\$). The goal is an easy model that links design choices to performance and security, helping readers build faster, safer, and more adaptive IoT-Fog Blockchain networks.},
keywords = {blockchain, sidechain, IoT-Fog networks, scalability and security, artificial intelligence},
issn = {3142-7855},
publisher = {Institute of Central Computation and Knowledge}
}
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Copyright © 2026 by the Author(s). Published by Institute of Central Computation and Knowledge. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.