@article {27386,
	title = {Blockchain Empowered Asynchronous Federated Learning for Secure Data Sharing in Internet of Vehicles},
	journal = {IEEE Transactions on Vehicular Technology},
	volume = {69},
	year = {2020},
	pages = {4298 - 4311},
	publisher = { IEEE},
	abstract = {In Internet of Vehicles (IoV), data sharing among vehicles for collaborative analysis can improve the driving experience and service quality. However, the bandwidth, security and privacy issues hinder data providers from participating in the data sharing process. In addition, due to the intermittent and unreliable communications in IoV, the reliability and efficiency of data sharing need to be further enhanced. In this paper, we propose a new architecture based on federated learning to relieve transmission load and address privacy concerns of providers. To enhance the security and reliability of model parameters, we develop a hybrid blockchain architecture which consists of the permissioned blockchain and the local Directed Acyclic Graph (DAG). Moreover, we propose an asynchronous federated learning scheme by adopting Deep Reinforcement Learning (DRL) for node selection to improve the efficiency. The reliability of shared data is also guaranteed by integrating learned models into blockchain and executing a two-stage verification. Numerical results show that the proposed data sharing scheme provides both higher learning accuracy and faster convergence.},
	author = {Lu, Y. and Huang, X. and Zhang, K. and Maharjan, Sabita and Zhang, Yan}
}
@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 {27381,
	title = {Deep Reinforcement Learning for Social-Aware Edge Computing and Caching in Urban Informatics},
	journal = { IEEE Transactions on Industrial Informatics},
	volume = {16},
	year = {2020},
	pages = {5467 - 5477},
	publisher = { IEEE},
	abstract = {Empowered with urban informatics, transportation industry has witnessed a paradigm shift. These developments lead to the need of content processing and sharing between vehicles under strict delay constraints. Mobile edge services can help meet these demands through computation offloading and edge caching empowered transmission, while cache-enabled smart vehicles may also work as carriers for content dispatch. However, diverse capacities of edge servers and smart vehicles, as well as unpredictable vehicle routes, make efficient content distribution a challenge. To cope with this challenge, in this article we develop a social-aware nobile edge computing and caching mechanism by exploiting the relation between vehicles and roadside units. By leveraging a deep reinforcement learning approach, we propose optimal content processing and caching schemes that maximize the dispatch utility in an urban environment with diverse vehicular social characteristics.},
	author = {Zhang, K. and Cao, J. and Liu, H. and Maharjan, Sabita and Zhang, Yan}
}
@article {26974,
	title = {Deep Learning Empowered Task Offloading for Mobile Edge Computing in Urban Informatics},
	journal = {IEEE Internet of Things Journal},
	volume = {6},
	number = {Oct 2019},
	year = {2019},
	month = {03/2019},
	publisher = { IEEE},
	abstract = {Led by industrialization of smart cities, numerous interconnected mobile devices, and novel applications have emerged in the urban environment, providing great opportunities to realize industrial automation. In this context, autonomous driving is an attractive issue, which leverages large amounts of sensory information for smart navigation while posing intensive computation demands on resource constrained vehicles. Mobile edge computing (MEC) is a potential solution to alleviate the heavy burden on the devices. However, varying states of multiple edge servers as well as a variety of vehicular offloading modes make efficient task offloading a challenge. To cope with this challenge, we adopt a deep Q-learning approach for designing optimal offloading schemes, jointly considering selection of target server and determination of data transmission mode. Furthermore, we propose an efficient redundant offloading algorithm to improve task offloading reliability in the case of vehicular data transmission failure. We evaluate the proposed schemes based on real traffic data. Results indicate that our offloading schemes have great advantages in optimizing system utilities and improving offloading reliability.},
	author = {Zhang, K. and Zhu, Y. and Leng, S. and He, 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 {26976,
	title = {Edge Intelligence and Blockchain Empowered 5G Beyond for Industrial Internet of Things},
	journal = {IEEE Network},
	volume = {33},
	number = {12-19},
	year = {2019},
	month = {10/2019},
	publisher = { IEEE},
	abstract = {Edge intelligence is a key enabler for IIoT as it offers smart cloud services in close proximity to the production environment with low latency and less cost. The need for ubiquitous communication, computing, and caching resources in 5G beyond will lead to a growing demand to integrate heterogeneous resources into the edge network. Furthermore, distributed edge services can make resource transactions vulnerable to malicious nodes. Ensuring secure edge services under complex industrial networks is a big challenge. In this article, we present an edge intelligence and blockchain empowered IIoT framework, which achieves flexible and secure edge service management. Then we propose a cross-domain sharing inspired edge resource scheduling scheme and design a credit-differentiated edge transaction approval mechanism. Numerical results indicate that the proposed schemes bring significant improvement in both edge service cost and service capacities.},
	author = {Zhang, K. and Zhu, Y. and Maharjan, Sabita and Zhang, Yan}
}
@article {26439,
	title = {Artificial Intelligence Inspired Transmission Scheduling in Cognitive Vehicular Communications and Networks},
	journal = {IEEE Internet of Things Journal},
	year = {2018},
	month = {09/2018},
	publisher = {IEEE},
	abstract = {The Internet of things (IoT) platform has played a significant role in improving road transport safety and efficiency by ubiquitously connecting intelligent vehicles through wireless communications. Such an IoT paradigm however, brings in considerable strain on limited spectrum resources due to the need of continuous communication and monitoring. Cognitive radio (CR) is a potential approach to alleviate the spectrum scarcity problem through opportunistic exploitation of the underutilized spectrum. However, highly dynamic topology and time-varying spectrum states in CR-based vehicular networks introduce quite a few challenges to be addressed. Moreover, a variety of vehicular communication modes, such as vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V), as well as data QoS requirements pose critical issues on efficient transmission scheduling. Based on this motivation, in this paper, we adopt a deep Q-learning approach for designing an optimal data transmission scheduling scheme in cognitive vehicular networks to minimize transmission costs while also fully utilizing various communication modes and resources. Furthermore, we investigate the characteristics of communication modes and spectrum resources chosen by vehicles in different network states, and propose an efficient learning algorithm for obtaining the optimal scheduling strategies. Numerical results are presented to illustrate the performance of the proposed scheduling schemes.},
	doi = {10.1109/JIOT.2018.2872013},
	author = {Zhang, K. and Leng, S. and Peng, X. and Pan, L. and Maharjan, Sabita and Zhang, Yan}
}
@article {25570,
	title = {Contract-theoretic Approach for Delay Constrained Offloading in Vehicular Edge Computing Networks},
	journal = {Mobile Networks and Applications},
	year = {2018},
	pages = {1-12},
	publisher = {Springer},
	address = {US},
	abstract = {Mobile Edge Computing (MEC) is a promising solution to improve vehicular services through offloading computation to cloud servers in close proximity to mobile vehicles. However, the self-interested nature together with the high mobility characteristic of the vehicles make the design of the computation offloading scheme a significant challenge. In this paper, we propose a new Vehicular Edge Computing (VEC) framework to model the computation offloading process of the mobile vehicles running on a bidirectional road. Based on this framework, we adopt a contract theoretic approach to design optimal offloading strategies for the VEC service provider, which maximize the revenue of the provider while enhancing the utilities of the vehicles. To further improve the utilization of the computing resources of the VEC servers, we incorporate task priority distinction as well as additional resource providing into the design of the offloading scheme, and propose an efficient VEC server selection and computing resource allocation algorithm. Numerical results indicate that our proposed schemes greatly enhance the revenue of the VEC provider, and concurrently improve the utilization of cloud computing resources.},
	keywords = {Cloud, Contract theory, Mobile edge computing, Vehicular network},
	issn = {1572-8153},
	doi = {10.1007/s11036-018-1032-0},
	author = {Zhang, K. and Mao, Y. and Leng, S. and Maharjan, Sabita and Vinel, A. and Zhang, Yan}
}
@article {26434,
	title = {Cooperative Content Caching in 5G Networks with Mobile Edge Computing},
	journal = {IEEE Wireless Communications Magazine},
	volume = {25},
	number = {3},
	year = {2018},
	month = {06/2018},
	pages = {80-87},
	publisher = {IEEE},
	abstract = {Along with modern wireless networks being content-centric, the demand for rich multimedia services has been growing at a tremendous pace, which brings significant challenges to mobile networks in terms of the need for massive content delivery. Edge caching has emerged as a promising approach to alleviate the heavy burden on data transmission through caching and forwarding contents at the edge of networks. However, existing studies always treat storage and computing resources separately, and neglect the mobility characteristic of both the content caching nodes and end users. Driven by these issues, in this work, we propose a new cooperative edge caching architecture for 5G networks, where mobile edge computing resources are utilized for enhancing edge caching capability. In the architecture, we focus on mobility-aware hierarchical caching, where smart vehicles are taken as collaborative caching agents for sharing content cache tasks with base stations. To further utilize the caching resource of smart vehicles, we introduce a new vehicular caching cloud concept, and propose a vehicle-aided edge caching scheme, where the caching and computing resources at the wireless network edge are jointly scheduled. Numerical results indicate that the proposed scheme minimizes content access latency and improves caching resource utilization.},
	author = {Zhang, K. and Leng, S. and He, Y. and Maharjan, Sabita and Zhang, Yan}
}
@article {26437,
	title = {Mobile Edge Computing and Networking for Green and Low Latency Internet of Things},
	journal = {IEEE Communications Magazine},
	volume = {56},
	number = {5},
	year = {2018},
	month = {05/2018},
	pages = {39-45},
	publisher = {IEEE},
	abstract = {IoT, a heterogeneous interconnection of smart devices, is a great platform to develop novel mobile applications. Resource constrained smart devices, however, often become the bottlenecks to fully realize such developments, especially when it comes to intensive-computation-oriented and low-latency-demanding applications. MEC is a promising approach to address such challenges. In this article, we focus on MEC applications for IoT, and address energy efficiency as well as offloading performance of such applications in terms of end-user experience. In this regard, we present a mobility-aware hierarchical MEC framework for green and low-latency IoT. We deploy a game theoretic approach for computation offloading in order to optimize the utility of the service providers while also reducing the energy cost and the task execution time of the smart devices. Numerical results indicate that the proposed scheme does brings significant enhancement in both energy efficiency and latency performance of MEC applications for IoT.},
	author = {Zhang, K. and Leng, S. and He, Y. and Maharjan, Sabita and Zhang, Yan}
}
@article {26438,
	title = {Optimal Charging Schemes for Electric Vehicles in Smart Grid: A Contract Theoretic Approach},
	journal = {IEEE Transactions on Intelligent Transportaion Systems},
	volume = {19},
	year = {2018},
	month = {09/2018},
	pages = {3046-3058},
	publisher = {IEEE},
	abstract = {Due to their environment friendliness, electric vehicles (EVs) are anticipated to form a considerable fraction of vehicles for transportation in smart cities. It is essential to design an electricity charging scheme that takes the utilities of both the charging stations and the EVs into consideration. However, the self-interested nature of the EVs together with the information asymmetry between the energy demand and supply sides makes the design a significant challenge. In this paper, we propose a queuing network-based model to characterize the charging process of the multiple EVs in a renewable energy-aided charging station. Based on the model, we adopt a contract theoretic approach to design an optimal charging policy in an information asymmetry scenario. Furthermore, we propose the new contract-based charging rate assignment and admission control schemes that maximize the utility of the charging station under certain charging constraints. To derive the optimal contract, we present a two-step iterative algorithm and prove its convergence. We evaluate the proposed schemes based on the IEEE 69-bus distribution test system. Results indicate that the contract-based charging schemes can effectively benefit both the charging stations and the EVs and concurrently improve the load level of the smart grid.},
	author = {Zhang, K. and Mao, Y. and Leng, S. and He, Y. and Maharjan, Sabita and Gjessing, Stein and Zhang, Yan and Tsang, D.}
}
@article {24548,
	title = {Incentive-Driven Energy Trading in the Smart Grid},
	journal = {IEEE Access},
	volume = {4},
	year = {2016},
	month = {03/2016},
	pages = {1243-1257},
	publisher = {IEEE},
	abstract = {The smart grid is widely considered as an efcient and intelligent power system. With the aid of communication technologies, the smart grid can enhance the efciency and reliability of the grid system through intelligent energy management. However, with the development of new energy sources, storage and transmission technologies together with the heterogeneous architecture of the grid network, several new features have been incorporated into the smart grid. These features make the energy trading more complex and pose a signicant challenge on designing efcient trading schemes. Based on this motivation, in this paper, we present a comprehensive review of several typical economic incentive approaches adopted in the energy-trading control mechanisms. We focus on the technologies that address the challenges specic to the new features of the smart grid. Furthermore, we investigate the energy trading in a new cloud-based vehicle-to-vehicle energy exchange scenario. We propose an optimal contract-based electricity trading scheme, which efciently increases the generated profit.},
	issn = {2169-3536},
	doi = {10.1109/ACCESS.2016.2543841},
	author = {Zhang, K. and Mao, Y. and Leng, Supeng and Maharjan, Sabita and Zhang, Yan and Vinel, A. and Jonsson, Magnus}
}
@article {24546,
	title = {Social-aware Energy Harvesting Device to Device Communications in 5G Networks},
	journal = {IEEE Wireless Communication Magazine},
	volume = {23},
	year = {2016},
	pages = {20-27},
	publisher = {IEEE},
	abstract = {With ever-increasing demands for local area services in the fifth generation (5G) cellular networks, solutions are necessary to deliver local area data in both a spectrum- and an energy-efficient manner. In this paper we propose a new cellular communication architecture which integrates energy harvesting technologies and social networking characteristics into device-to-device (D2D) communications for local data dissemination. The proposed architecture includes three domains: the physical domain, the energy domain and the social domain. Specifically, in the physical domain, D2D communications enable two nearby users to communicate with each other directly. In the energy domain, devices harvest energy from renewable energy sources. In the social domain, D2D users form social networks exhibiting stable social structures and relations. Then we mainly focus on the efficient local data dissemination issues in the proposed architecture and propose two social-aware energy harvesting D2D communication schemes (a device relaying and a device multicast). Illustrative results demonstrate significant spectrum and energy efficiency enhancement for local data dissemination in 5G cellular networks.},
	author = {Jiang, L. and Tian, H. and Xing, Z. and Wang, K. and Zhang, K. and Maharjan, Sabita and Gjessing, Stein and Zhang, Yan}
}
