@article {28071,
	title = {Federated Learning empowered End-Edge-Cloud Cooperation for 5G HetNet Security},
	journal = {IEEE Network},
	volume = {35},
	year = {2021},
	month = {03/2021},
	pages = {88-94},
	publisher = { IEEE},
	abstract = {The distributed and heterogeneous framework in the 5G heterogeneous network (Het-Net) makes it vulnerable to attacks of different kinds. Nodes for improving the network security are therefore important to eliminate such critical threats. Without cooperation or with limited cooperation, these nodes are substantially restricted in their protecting capacity due to specific characteristics such as heterogeneity, hierarchy, and wide range in the 5G HetNet. In this article, we propose a federated learning empowered end-edge-cloud cooperation-based framework for enhancing 5G HetNet security. In this framework, nodes equipped with attack detection mechanisms are distributed in the end, edge, and cloud of the 5G HetNet. We then design cooperative training schemes to realize the full potential of these nodes in detecting attacks. Illustrative results demonstrate the superior performance of our proposed scheme compared to three different benchmark schemes.},
	author = {Wei, Yunkai and Zhou, Sipei and Leng, Supeng and Maharjan, Sabita and Zhang, Yan}
}
@article {28079,
	title = {Multi-Agent Deep Reinforcement Learning for Computation Offloading and Interference Coordination in Small Cell Networks},
	journal = {IEEE Transactions on Vehicular Technology},
	volume = {70},
	year = {2021},
	month = {09/2021},
	pages = {9282-9293},
	publisher = { IEEE},
	abstract = {Integrating mobile edge computing (MEC) with small cell networks has been conceived as a promising solution to provide pervasive computing services. However, the interactions among small cells due to inter-cell interference, the diverse application-specific requirements, as well as the highly dynamic wireless environment make it challenging to design an optimal computation offloading scheme. In this paper, we focus on the joint design of computation offloading and interference coordination for edge intelligence empowered small cell networks. To this end, we propose a distributed multi-agent deep reinforcement learning (DRL) scheme with the objective of minimizing the overall energy consumption while ensuring the latency requirements. Specifically, we exploit the collaboration among small cell base station (SBS) agents to adaptively adjust their strategies, considering computation offloading, channel allocation, power control, and computation resource allocation. Further, to decrease the computation complexity and signaling overhead of the training process, we design a federated DRL scheme which only requires SBS agents to share their model parameters instead of local training data. Numerical results demonstrate that our proposed schemes can significantly reduce the energy consumption and effectively guarantee the latency requirements compared with the benchmark schemes.},
	doi = {10.1109/TVT.2021.3096928},
	author = {Huang, Xiaoyan and Leng, Supeng and Maharjan, Sabita and Zhang, Yan}
}
@article {27378,
	title = {Deep Reinforcement Learning for Cooperative Content Caching in Vehicular Edge Computing and Networks},
	journal = {IEEE Internet of Things Journal},
	volume = {7},
	year = {2020},
	month = {01/2020},
	pages = {247-257},
	publisher = { IEEE},
	abstract = {In this article, we propose a cooperative edge caching scheme, a new paradigm to jointly optimize the content placement and content delivery in the vehicular edge computing and networks, with the aid of the flexible trilateral cooperations among a macro-cell station, roadside units, and smart vehicles. We formulate the joint optimization problem as a double time-scale Markov decision process (DTS-MDP), based on the fact that the time-scale of content timeliness changes less frequently as compared to the vehicle mobility and network states during the content delivery process. At the beginning of the large time-scale, the content placement/updating decision can be obtained according to the content popularity, vehicle driving paths, and resource availability. On the small time-scale, the joint vehicle scheduling and bandwidth allocation scheme is designed to minimize the content access cost while satisfying the constraint on content delivery latency. To solve the long-term mixed integer linear programming (LT-MILP) problem, we propose a nature-inspired method based on the deep deterministic policy gradient (DDPG) framework to obtain a suboptimal solution with a low computation complexity. The simulation results demonstrate that the proposed cooperative caching system can reduce the system cost, as well as the content delivery latency, and improve content hit ratio, as compared to the noncooperative and random edge caching schemes.},
	doi = { 10.1109/JIOT.2019.2945640},
	url = {https://ieeexplore.ieee.org/abstract/document/8879573},
	author = {Qiao, G. and Leng, Supeng and Maharjan, Sabita and Zhang, Yan and Ansari, Nirwan}
}
@article {27383,
	title = {Energy Efficiency and Delay Tradeoff for Wireless Powered Mobile-Edge Computing Systems with Multi-Access Schemes},
	journal = {IEEE Transactions on Wireless Communications},
	volume = {9},
	year = {2020},
	pages = {1855 - 1867},
	publisher = { IEEE},
	abstract = {The integration of Mobile-edge Computing (MEC) and Wireless Energy Transfer (WET) has been recognized as a promising technique to enhance computation capability and to prolong battery lifetime of resource-constrained wireless devices in the Internet of Things (IoT) era. However, it is challenging to jointly schedule energy, radio, and computational resources for coordinating heterogeneous performance requirements in wireless powered MEC systems. To fill this gap, this paper investigates the fundamental tradeoff between Energy Efficiency (EE) and delay in a multi-user wireless powered MEC system. Considering the random channel conditions and task arrivals, we formulate a stochastic optimization problem to study the EE-delay tradeoff, which optimizes network EE subject to network stability, maximum central processing unit frequency, peak transmission power, available communication resource, and energy causality constraints. Further, we propose the online computation offloading and resource allocation algorithm by transforming the original problem into a series of deterministic optimization problems in each time block based on Lyapunov optimization theory. In addition, theoretical analysis shows that the algorithm achieves the EE-delay tradeoff as [O(1/V), O(V)] and introduces a control parameter V to balance the EE-delay performance. Numerical results verify the theoretical analysis and reveal the impact of various parameters to the system performance.},
	author = {Mao, S. and Leng, Supeng and Maharjan, Sabita and Zhang, Yan}
}
@inproceedings {25273,
	title = {Optimal Delay Constrained Offloading for Vehicular Edge Computing Networks},
	journal = {2017 IEEE International Conference on Communications (ICC)},
	year = {2017},
	publisher = {IEEE},
	abstract = {The increasing number of smart vehicles and their resource hungry applications pose new challenges in terms of computation and processing for providing reliable and efficient vehicular services. Mobile Edge Computing (MEC) is a new paradigm with potential to improve vehicular services through computation offloading in close proximity to mobile vehicles. However, in the road with dense traffic flow, the computation limitation of these MEC servers may endanger the quality of offloading service. To address the problem, we propose a hierarchical cloud-based Vehicular Edge Computing (VEC) offloading framework, where a backup computing server in the neighborhood is introduced to make up for the deficit computing resources of MEC servers. Based on this framework, we adopt a Stackelberg game theoretic approach to design an optimal multilevel offloading scheme, which maximizes the utilities of both the vehicles and the computing servers. Furthermore, to obtain the optimal offloading strategies, we present an iterative distributed algorithm and prove its convergence. Numerical results indicate that our proposed scheme greatly enhances the utility of the offloading service providers.},
	isbn = {978-1-4673-8999-0},
	issn = {1938-1883},
	doi = {10.1109/ICC.2017.7997360},
	author = {Zhang, Ke and Mao, Yuming and Leng, Supeng and Maharjan, Sabita and Zhang, Yan},
	editor = {IEEE, }
}
@article {24611,
	title = {Energy-efficient Offloading for Mobile Edge Computing in 5G Heterogeneous Networks},
	journal = {IEEE Access},
	volume = {4},
	year = {2016},
	pages = {5896-5907},
	publisher = {IEEE},
	abstract = {Mobile Edge Computing (MEC) is a promising paradigm to provide cloud-computing capabilities in close proximity to mobile devices in fifth-generation (5G) networks. In this paper, we study energy-efficient computation offloading mechanisms for MEC in 5G heterogenous networks. We formulate an optimization problem to minimize the energy consumption of the offloading system, where the energy cost of both task computing and file transmission are taken into consideration. Incorporating the multi-access characteristics of the 5G heterogenous network, we then design an Energy-Efficient Computation Offloading (EECO) scheme, which jointly optimizes offloading and radio resource allocation to obtain the minimal energy consumption under the latency constraints. Numerical results demonstrate energy efficiency improvement of our proposed EECO scheme.},
	doi = {10.1109/ACCESS.2016.2597169},
	author = {Zhang, Ke and Mao, Yuming and Leng, Supeng and Zhao, Quanxin and Li, Longjiang and Peng, Xin and Pan, Li and Zhang, Guoyun and Maharjan, Sabita and Zhang, Yan}
}
@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 {23691,
	title = {An Incentivized Auction Based Group-Selling Approach for Demand Response Management in V2G Systems},
	journal = {IEEE Transactions on Industrial Informatics},
	volume = {11},
	year = {2015},
	month = {12/2015},
	pages = {1554-1563},
	publisher = {IEEE},
	abstract = {Vehicle-to-Grid (V2G) system with efficient Demand Response Management (DRM) is critical to solve the problem of supplying electricity by utilizing surplus electricity available at EVs. An incentivilized DRM approach is studied to reduce the system cost and maintain the system stability. EVs are motivated with dynamic pricing determined by the group-selling based auction. In the proposed approach, a number of aggregators sit on the first level auction responsible to communicate with a group of EVs. EVs as bidders consider Quality of Energy (QoE) requirements and report interests and decisions on the bidding process coordinated by the associated aggregator. Auction winners are determined based on the bidding prices and the amount of electricity sold by the EV bidders. We investigate the impact of the proposed mechanism on the system performance with maximum feedback power constraints of aggregators. The designed mechanism is proven to have essential economic properties. Simulation results indicate the proposed mechanism can reduce the system cost and offer EVs significant incentives to participate in the V2G DRM operation.},
	keywords = {auction, Demand response management, group-selling, Vehicle-to-grid},
	doi = {10.1109/TII.2015.2482948},
	author = {Zeng, Ming and Leng, Supeng and Maharjan, Sabita and Gjessing, Stein and He, Jianhua}
}
@inproceedings {24520,
	title = {Analysis of Connectivity Probability in Platoon-based Vehicular Ad Hoc Networks},
	journal = {IWCMC 2014 (Best Paper Award)},
	year = {2014},
	author = {Shao, C. and Leng, Supeng and Zhang, Yan and Vinel, A. and Jonsson, Magnus}
}
@article {24496,
	title = {A multi-priority supported medium access control in Vehicular Ad Hoc Networks},
	journal = {Computer Communications},
	volume = {39},
	year = {2014},
	pages = {367-374},
	publisher = {Elsevier},
	author = {Shao, Caixing and Leng, Supeng and Zhang, Yan and Fu, Huirong}
}
