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Li, Jing, Liu, Tingting, Niyato, Dusit, Wang, Ping, Li, Jun, Han, Zhu.  2019.  Contract-Based Approach for Security Deposit in Blockchain Networks with Shards. 2019 IEEE International Conference on Blockchain (Blockchain). :75–82.
As a decentralized ledger technology, blockchain is considered to be a potential solution for applications with highly concentrated management mechanism. However, most of the existing blockchain networks are employed with the hash-puzzle-solving consensus protocol, known as proof-of-work. The competition of solving the puzzle introduces high latency, which directly leads to a long transaction-processing time. One solution of this dilemma is to establish a blockchain network with shards. In this paper, we focus on the blockchain network with shards and adopt the security-deposit based consensus protocol, studying the problem of how to balance the security incentive and the economic incentive. Also, the inherent features of the blockchain, i.e., anonymity and decentralization, introduce the information asymmetric issue between the beacon chain and the participants. The contract theory is utilized to formulate the problem between them. As such, the optimal rewards related to the different types of validators can be obtained, as well as the reasonable deposits accordingly. Compared with the fixed deposits, the flexible deposits can provide enough economic incentive for the participants without losing the security incentives. Besides, the simulation results demonstrate that the contract theory approach is capable of maximizing the beacon chain's utility and satisfying the incentive compatibility and individual rationality of the participants.
Lim, Wei Yang Bryan, Xiong, Zehui, Niyato, Dusit, Huang, Jianqiang, Hua, Xian-Sheng, Miao, Chunyan.  2020.  Incentive Mechanism Design for Federated Learning in the Internet of Vehicles. 2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall). :1—5.
In the Internet of Vehicles (IoV) paradigm, a model owner is able to leverage on the enhanced capabilities of Intelligent Connected Vehicles (ICV) to develop promising Artificial Intelligence (AI) based applications, e.g., for traffic efficiency. However, in some cases, a model owner may have insufficient data samples to build an effective AI model. To this end, we propose a Federated Learning (FL) based privacy preserving approach to facilitate collaborative FL among multiple model owners in the IoV. Our system model enables collaborative model training without compromising data privacy given that only the model parameters instead of the raw data are exchanged within the federation. However, there are two main challenges of incentive mismatches between workers and model owners, as well as among model owners. For the former, we leverage on the self-revealing mechanism in contract theory under information asymmetry. For the latter, we use the coalitional game theory approach that rewards model owners based on their marginal contributions. The numerical results validate the performance efficiency of our proposed hierarchical incentive mechanism design.
Liu, Yang, Wang, Meng, Xu, Jing, Gong, Shimin, Hoang, Dinh Thai, Niyato, Dusit.  2021.  Boosting Secret Key Generation for IRS-Assisted Symbiotic Radio Communications. 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring). :1—6.
Symbiotic radio (SR) has recently emerged as a promising technology to boost spectrum efficiency of wireless communications by allowing reflective communications underlying the active RF communications. In this paper, we leverage SR to boost physical layer security by using an array of passive reflecting elements constituting the intelligent reflecting surface (IRS), which is reconfigurable to induce diverse RF radiation patterns. In particular, by switching the IRS's phase shifting matrices, we can proactively create dynamic channel conditions, which can be exploited by the transceivers to extract common channel features and thus used to generate secret keys for encrypted data transmissions. As such, we firstly present the design principles for IRS-assisted key generation and verify a performance improvement in terms of the secret key generation rate (KGR). Our analysis reveals that the IRS's random phase shifting may result in a non-uniform channel distribution that limits the KGR. Therefore, to maximize the KGR, we propose both a heuristic scheme and deep reinforcement learning (DRL) to control the switching of the IRS's phase shifting matrices. Simulation results show that the DRL approach for IRS-assisted key generation can significantly improve the KGR.