TY - JOUR

T1 - Game Theory Empowered Carbon-Intelligent Federated Multi-Edge Caching for Industrial Internet of Things

AU - Liang, Jie

AU - Yu, Zhengxin

AU - Pervaiz, Haris

AU - Zheng, Guhan

AU - Suri, Neeraj

PY - 2025/7/10

Y1 - 2025/7/10

N2 - To navigate the carbon emission and functional challenges associated with edge caching within heterogeneous Industrial Internet of Things (IIoT) spanning energy use, cache hit rate, and bandwidth usage, this paper proposes a novel Game Theory Empowered Carbon-Intelligent Federated Multi-Edge Caching framework (GT-FMC). The proposed framework enables distributed collaborative caching by intelligently coordinating edge nodes to optimize content decisions while efficiently integrating content providers (CPs), edge nodes, and users with energy-aware strategies. In GT-FMC, a lightweight federated content popularity prediction method based on Temporal Convolutional Networks (TCN) is introduced to collaboratively learn global content popularity while reducing prediction energy cost. The energy-aware utilities of the three involved parties are jointly formulated as a coupled non-linear optimization problem. To address this challenge, a two-stage game-theoretic algorithm is designed. Experimental results on a real-world testbed show that GT-FMC achieves up to 77.9% of Oracle in cache hit rate and 10.6%–32.4% reduction in transmission energy consumption compared to baseline methods. Complementary evaluations also validate the game-theoretic design’s effectiveness.

AB - To navigate the carbon emission and functional challenges associated with edge caching within heterogeneous Industrial Internet of Things (IIoT) spanning energy use, cache hit rate, and bandwidth usage, this paper proposes a novel Game Theory Empowered Carbon-Intelligent Federated Multi-Edge Caching framework (GT-FMC). The proposed framework enables distributed collaborative caching by intelligently coordinating edge nodes to optimize content decisions while efficiently integrating content providers (CPs), edge nodes, and users with energy-aware strategies. In GT-FMC, a lightweight federated content popularity prediction method based on Temporal Convolutional Networks (TCN) is introduced to collaboratively learn global content popularity while reducing prediction energy cost. The energy-aware utilities of the three involved parties are jointly formulated as a coupled non-linear optimization problem. To address this challenge, a two-stage game-theoretic algorithm is designed. Experimental results on a real-world testbed show that GT-FMC achieves up to 77.9% of Oracle in cache hit rate and 10.6%–32.4% reduction in transmission energy consumption compared to baseline methods. Complementary evaluations also validate the game-theoretic design’s effectiveness.

U2 - 10.1109/jiot.2025.3587250

DO - 10.1109/jiot.2025.3587250

M3 - Journal article

VL - 12

SP - 34875

EP - 34889

JO - IEEE Internet of Things Journal

JF - IEEE Internet of Things Journal

SN - 2327-4662

IS - 17

ER -