@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}
}
@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.}
}
