Energy-Efficient Covert Offloading in Blockchain-Enabled IoT: Joint Artificial Noise and Computation Resource Allocation
Yu'e Jiang, Yutong Wang, Haiqin Wu, Yiliang Liu, Langtao Hu · IEEE Internet of Things Journal · 2024
This article proposes an energy-efficient covert offloading scheme for blockchain-enabled Internet of Things (IoT), allowing sensors to upload tasks undetected by adversaries while ensuring satisfaction in paid computation offloading. Covert communication conceals the existence of transmitted signals or links. However, existing schemes primarily rely on artificial noise (AN) or wireless channel uncertainty, resulting in low covert rates for IoT offloading scenarios. Additionally, blockchain-enabled IoT, being value-oriented, necessitates consideration of sensors’ satisfaction during covert offloading. To tackle these challenges, the proposed scheme combines the adversary’s channel estimation errors with AN to enhance the covert rate, while also matching sensors’ satisfaction with the computation resources of mobile edge servers. Notably, a closed-form expression of the average minimum error detection probability is derived to maximize the effective covert rate. Furthermore, an integrated algorithm combining the Kuhn-Munkres (KM) algorithm with two bubble sort algorithms is designed to minimize energy consumption. Both analytical and simulation results demonstrate that the proposed scheme significantly reduces energy consumption compared to existing solutions.