4.8 Article

Blockchain-Based Incentive Energy-Knowledge Trading in IoT: Joint Power Transfer and AI Design

Journal

IEEE INTERNET OF THINGS JOURNAL
Volume 9, Issue 16, Pages 14685-14698

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2020.3024246

Keywords

Batteries; Artificial intelligence; Smart devices; Internet of Things; Wireless power transfer; Performance evaluation; Edge intelligence; game theory; incentive mechanism; permissioned blockchain; wireless power transfer (WPT)

Funding

  1. National Natural Science Foundation of China [61831007, 61972255]

Ask authors/readers for more resources

This article proposes a wirelessly powered edge intelligence framework to achieve stable, robust, and sustainable edge intelligence through energy harvesting methods. The use of a permissioned edge blockchain ensures secure energy and knowledge sharing. The article also investigates a wirelessly powered multiagent edge learning model and designs an optimal learning strategy.
Recently, edge artificial intelligence techniques (e.g., federated edge learning) are emerged to unleash the potential of big data from Internet of Things (IoT). By learning knowledge on local devices, data privacy preserving and Quality of Service (QoS) are guaranteed. Nevertheless, the dilemma between the limited on-device battery capacities and the high energy demands in learning is not resolved. When the on-device battery is exhausted, the edge learning process will have to be interrupted. In this article, we propose a novel wirelessly powered edge intelligence (WPEG) framework, which aims to achieve a stable, robust, and sustainable edge intelligence by energy harvesting (EH) methods. First, we build a permissioned edge blockchain to secure the peer-to-peer (P2P) energy and knowledge sharing in our framework. To maximize edge intelligence efficiency, we then investigate the wirelessly powered multiagent edge learning model and design the optimal edge learning strategy. Moreover, by constructing a two-stage Stackelberg game, the underlying energy-knowledge trading incentive mechanisms are also proposed with the optimal economic incentives and power transmission strategies. Finally, simulation results show that our incentive strategies could optimize the utilities of both parties compared with classic schemes, and our optimal learning design could realize the optimal learning efficiency.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.8
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available