@article {27385,
	title = {Deep Reinforcement Learning and Permissioned Blockchain for Content Caching in Vehicular Edge Computing and Networks},
	journal = {IEEE Transactions on Vehicular Technology},
	volume = {69},
	year = {2020},
	month = {02/2020},
	pages = { 4312 - 4324},
	publisher = { IEEE},
	abstract = {Vehicular Edge Computing (VEC) is a promising paradigm to enable huge amount of data and multimedia content to be cached in proximity to vehicles. However, high mobility of vehicles and dynamic wireless channel condition make it challenge to design an optimal content caching policy. Further, with much sensitive personal information, vehicles may be not willing to caching their contents to an untrusted caching provider. Deep Reinforcement Learning (DRL) is an emerging technique to solve the problem with high-dimensional and time-varying features. Permission blockchain is able to establish a secure and decentralized peer-to-peer transaction environment. In this paper, we integrate DRL and permissioned blockchain into vehicular networks for intelligent and secure content caching. We first propose a blockchain empowered distributed content caching framework where vehicles perform content caching and base stations maintain the permissioned blockchain. Then, we exploit the advanced DRL approach to design an optimal content caching scheme with taking mobility into account. Finally, we propose a new block verifier selection method, Proof-of-Utility (PoU), to accelerate block verification process. Security analysis shows that our proposed blockchain empowered content caching can achieve security and privacy protection. Numerical results based on a real dataset from Uber indicate that the DRL-inspired content caching scheme significantly outperforms two benchmark policies.},
	author = {Dai, Y. and Xu, D. and Zhang, K. and Maharjan, Sabita and Zhang, Yan}
}
@article {27379,
	title = {Differentially Private Asynchronous Federated Learning for Mobile Edge Computing in Urban Informatics},
	journal = { IEEE Transactions on Industrial Informatics},
	volume = {16},
	year = {2020},
	month = {03/2020},
	pages = {2134 - 2143},
	publisher = { IEEE},
	abstract = {Driven by technologies such as mobile edge computing and 5G, recent years have witnessed the rapid development of urban informatics, where a large amount of data is generated. To cope with the growing data, artificial intelligence algorithms have been widely exploited. Federated learning is a promising paradigm for distributed edge computing, which enables edge nodes to train models locally without transmitting their data to a server. However, the security and privacy concerns of federated learning hinder its wide deployment in urban applications such as vehicular networks. In this article, we propose a differentially private asynchronous federated learning scheme for resource sharing in vehicular networks. To build a secure and robust federated learning scheme, we incorporate local differential privacy into federated learning for protecting the privacy of updated local models. We further propose a random distributed update scheme to get rid of the security threats led by a centralized curator. Moreover, we perform the convergence boosting in our proposed scheme by updates verification and weighted aggregation. We evaluate our scheme on three real-world datasets. Numerical results show the high accuracy and efficiency of our proposed scheme, whereas preserve the data privacy.},
	issn = {Print ISSN: 1551-3203 Electronic ISSN: 1941-0050},
	doi = {10.1109/TII.2019.2942179},
	url = {https://ieeexplore.ieee.org/document/8843942/authors$\#$authors},
	author = {Lu, Y. and Huang, X. and Dai, Y. and Maharjan, Sabita and Zhang, Yan}
}
@article {27380,
	title = {Federated Learning for Data Privacy Preservation in Vehicular Cyber-Physical Systems},
	journal = {IEEE Network},
	volume = {34},
	year = {2020},
	month = {05/2020},
	pages = {50-56},
	publisher = { IEEE},
	abstract = {Recent developments in technologies such as MEC and AI contribute significantly in accelerating the deployment of VCPS. Techniques such as dynamic content caching, efficient resource allocation, and data sharing play a crucial role in enhancing the service quality and user driving experience. Meanwhile, data leakage in VCPS can lead to physical consequences such as endangering passenger safety and privacy, and causing severe property loss for data providers. The increasing volume of data, the dynamic network topology, and the availability of limited resources make data leakage in VCPS an even more challenging problem, especially when it involves multiple users and multiple transmission channels. In this article, we first propose a secure and intelligent architecture for enhancing data privacy. Then we present our new privacy-preserving federated learning mechanism and design a two-phase mitigating scheme consisting of intelligent data transformation and collaborative data leakage detection. Numerical results based on a real-world dataset demonstrate the effectiveness of our proposed scheme and show that our scheme achieves good accuracy, efficiency, and high security.},
	issn = {Print ISSN: 0890-8044 Electronic ISSN: 1558-156X},
	doi = {10.1109/MNET.011.1900317},
	url = {https://ieeexplore.ieee.org/document/9105934},
	author = {Lu, Y. and Huang, X. and Dai, Y. and Maharjan, Sabita and Zhang, Yan}
}
@article {26443,
	title = {Artificial Intelligence Empowered Edge Computing and Caching for Internet of Vehicles},
	journal = {IEEE Wireless Communications Magazine},
	volume = {26},
	year = {2019},
	pages = {12-18},
	publisher = {IEEE},
	abstract = {Recent advances in edge computing and caching have significant impacts on the developments of vehicular networks. Nevertheless, the heterogeneous requirements of onvehicle applications and the time-variability on popularity of contents, bring great challenges for edge servers to efficiently utilize their resources. Moreover, the high mobility of smart vehicles adds substantial complexity in jointly optimizing edge computing and caching. Artificial Intelligence (AI) can greatly enhance the cognition and intelligence of vehicular networks and thus assist in optimally allocating resources for the problems with diverse, time-variant, and complex features. In this article, we propose a new architecture that can dynamically orchestrate edge computing and caching resources to improve system utility by making full use of AI based algorithms. Then we formulate a joint edge computing and caching scheme to maximize system utility and develop a novel resource scheme by exploiting deep reinforcement learning. Numerical results demonstrate the effectiveness of the proposed scheme.},
	author = {Dai, Y. and Xu, D. and Maharjan, Sabita and Zhang, Yan}
}
@article {26973,
	title = {Blockchain and Deep Reinforcement Learning Empowered Intelligent 5G Beyond},
	journal = {IEEE Network},
	volume = {33},
	number = {10-17},
	year = {2019},
	month = {05/2019},
	publisher = {IEEE},
	abstract = {Blockchain and AI are promising techniques for next-generation wireless networks. Blockchain can establish a secure and decentralized resource sharing environment. AI can be explored to solve problems with uncertain, time-variant, and complex features. Both of these techniques have recently seen a surge in interest. The integration of these two techniques can further enhance the performance of wireless networks. In this article, we first propose a secure and intelligent architecture for next generation wireless networks by integrating AI and blockchain into wireless networks to enable flexible and secure resource sharing. Then we propose a blockchain empowered content caching problem to maximize system utility, and develop a new caching scheme by utilizing deep reinforcement learning. Numerical results demonstrate the effectiveness of the proposed scheme.},
	author = {Dai, Y. and Xu, D. and Maharjan, Sabita and Chen, Z. and He, Q. and Zhang, Yan}
}
@article {26979,
	title = {Blockchain and Federated Learning for Privacy-preserved Data Sharing in Industrial IoT},
	journal = { IEEE Transactions on Industrial Informatics (Early Access)},
	year = {2019},
	publisher = { IEEE},
	abstract = {The rapid increase in the volume of data generated from connected devices in Industrial Internet of Things (IIoT) paradigm, opens up new possibilities for enhancing the quality of service for the emerging applications through data sharing. However, security and privacy concerns (e.g, data leakage) are major obstacles for data providers to share their data in wireless networks. The leakage of private data can lead to serious issues beyond financial loss for the providers. In this article, we first propose a blockchain empowered secure data sharing architecture for distributed multiple parties. Then, we transfer the data sharing problem into a machine learning problem by incorporating privacy-preserved federated learning. The privacy of data is well maintained by sharing the data model instead of revealing the actual data. Finally, we integrate federated learning in the consensus process of permissioned blockchain thus the computing work for consensus can also be used for federated training. Numerical results derived from real-world datasets show that the proposed data sharing scheme achieves good accuracy, high efficiency and enhanced security.},
	author = {Lu, Y. and Huang, X. and Dai, Y. and Maharjan, Sabita and Zhang, Yan}
}
@inproceedings {26970,
	title = {Deep Reinforcement Learning for Edge Caching and Content Delivery in Internet of Vehicles},
	journal = {IEEE/CIC International Conference on Communications in China (ICCC)},
	year = {2019},
	publisher = {IEEE/CIC},
	abstract = {To enable the emerging vehicular applications and multimedia services in an Internet of Vehicles (IoV) framework, edge caching is a promising paradigm which can cache content in proximity to vehicles, and thus alleviate heavy load on backhaul links and contribute in reducing transmission latency. However, in a multi-access vehicular network, complex content delivery and high mobility of vehicles introduce new challenges to support edge caching in a dynamic environment. Deep Reinforcement Learning (DRL) is an emerging technique to solve the issue with time-varying feature. In this paper, we utilize DRL to design an optimal vehicular edge caching and content delivery strategy for minimizing content delivery latency. We first propose a multiaccess edge caching and content delivery framework in vehicular networks. Then, we formulate the vehicular edge caching and content delivery problem and propose a novel DRL algorithm to solve it. Numerical results demonstrate the effectiveness of proposed DRL-based algorithm, compared to two benchmark solutions.},
	author = {Dai, Y. and Xu, D. and Lu, Y. and Maharjan, Sabita and Zhang, Yan}
}
@inproceedings {26972,
	title = {Deep Reinforcement Learning for Edge Computing and Resource Allocation in 5G Beyond},
	journal = {2019 IEEE 19th International Conference on Communication Technology (ICCT)},
	year = {2019},
	pages = {866-870},
	publisher = { IEEE},
	abstract = {By extending computation capacity to the edge of wireless networks, edge computing has the potential to enable computation-intensive and delay-sensitive applications in 5G and beyond via computation offloading. However, in multi-user heterogeneous networks, it is challenging to capture complete network information, such as wireless channel state, available bandwidth or computation resources. The strong couplings among devices on application requirements or radio access mode make it more difficult to design an optimal computation offloading scheme. Deep Reinforcement Learning (DRL) is an emerging technique to address such an issue with limited and less accurate network information. In this paper, we utilize DRL to design an optimal computation offloading and resource allocation strategy for minimizing system energy consumption. We first present a multi-user edge computing framework in heterogeneous networks. Then, we formulate the joint computation offloading and resource allocation problem as a DRL form and propose a new DRL-inspired algorithm to minimize system energy consumption. Numerical results based on a realworld dataset demonstrate demonstrate the effectiveness of our proposed algorithm, compared to two benchmark solutions.},
	author = {Dai, Y. and Xu, D. and Zhang, K. and Lu, Y. and Maharjan, Sabita and Zhang, Yan}
}
@article {26442,
	title = {Joint Computation Offloading and User Association in Multi-task Mobile Edge Computing},
	journal = {IEEE Transactions on Vehicular Technology},
	volume = {67},
	year = {2018},
	month = {12/2018},
	pages = {12313-12325},
	publisher = {IEEE},
	abstract = {Computation intensive and delay-sensitive applications impose severe requirements on mobile devices of providing required computation capacity and ensuring latency. Mobile edge computing (MEC) is a promising technology that can alleviate computation limitation of mobile users and prolong their lifetime through computation offloading. However, computation offloading in an MEC environment faces severe issues due to dense deployment of MEC servers. Moreover, a mobile user has multiple mutually dependent tasks, which make offloading policy design even more challenging. To address the above-mentioned problems in this paper, we first propose a novel two-tier computation offloading framework in heterogeneous networks. Then, we formulate joint computation offloading and user association problem for multi-task mobile edge computing system to minimize overall energy consumption. To solve the optimization problem, we develop an efficient computation offloading algorithm by jointly optimizing user association and computation offloading where computation resource allocation and transmission power allocation are also considered. Numerical results illustrate fast convergence of the proposed algorithm, and demonstrate the superior performance of our proposed algorithm compared to state of the art solutions.},
	author = {Dai, Y. and Xu, D. and Maharjan, Sabita and Zhang, Yan}
}
@article {26441,
	title = {Joint Load Balancing and Offloading in Vehicular Edge Computing and Networks},
	journal = {IEEE Internet of Things Journal},
	year = {2018},
	month = {10/2018},
	publisher = {IEEE},
	abstract = {The emergence of computation intensive and delay sensitive on-vehicle applications makes it quite a challenge for vehicles to be able to provide the required level of computation capacity, and thus the performance. Vehicular Edge Computing (VEC) is a new computing paradigm with a great potential to enhance vehicular performance by offloading applications from the resource-constrained vehicles to lightweight and ubiquitous VEC servers. Nevertheless, offloading schemes where all vehicles offload their tasks to the same VEC server, can limit the performance gain due to overload. To address this problem, in this paper, we propose integrating load balancing with offloading, and study resource allocation for a multi-user multi-server VEC system. First, we formulate the joint load balancing and offloading problem as a mixed integer non-linear programming problem to maximize system utility. Particularly, we take IEEE 802.11p protocol into consideration for modeling the system utility. Then, we decouple the problem as two subproblems and develop a low-complexity algorithm to jointly make VEC server selection, and optimize offloading ratio and computation resource. Numerical results illustrate that the proposed algorithm exhibits fast convergence and demonstrates the superior performance of our joint optimal VEC server selection and offloading (JSCO) algorithm compared to the benchmark solutions.},
	author = {Dai, Y. and Xu, D. and Maharjan, Sabita and Zhang, Yan}
}
@inproceedings {26432,
	title = {Joint Offloading and Resource Allocation in Vehicular Edge Computing and Networks},
	journal = {IEEE Globecom 2018},
	year = {2018},
	publisher = {IEEE},
	abstract = {The emergence of computation intensive on-vehicle applications poses a significant challenge to provide the required computation capacity and maintain high performance. Vehicular Edge Computing (VEC) is a new computing paradigm with a high potential to improve vehicular services by offloading computation-intensive tasks to the VEC servers. Nevertheless, as the computation resource of each VEC server is limited, offloading may not be efficient if all vehicles select the same VEC server to offload their tasks. To address this problem, in this paper, we propose offloading with resource allocation. We incorporate the communication and computation to derive the task processing delay. We formulate the problem as a system utility maximization problem, and then develop a low-complexity algorithm to jointly optimize offloading decision and resource allocation. Numerical results demonstrate the superior performance of our Joint Optimization of Selection and Computation (JOSC) algorithm compared to state of the art solutions.},
	author = {Dai, Y. and Xu, D. and Maharjan, Sabita and Zhang, Yan}
}
