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With the increasing popularity of smart user equipments (UEs), numerous exciting wireless big data services are coming into life. However, due to the constraints of physical size, UEs are struggling with limited battery energy and computation capacity. In this paper, we propose a multi-user vehicle-aided multi-access edge computing (MEC) network architecture by deploying parked vehicles as the temporary computation service providers. A dynamic pricing strategy is proposed to minimize the energy consumption of UEs under the constraints on quality of service while maximize mobile service providers (MSPs)’ revenue. A differential evolution (DE) algorithm is proposed to determine UE’s fine-grained offloading decision. Moreover, a Q-learning algorithm is utilized to assist the DE algorithm in selecting the proper unit service price. The results show that the proposed dynamic pricing strategy can achieve better performance than the fixed pricing strategy and the differential pricing strategy regarding the overall cumulative cost. In addition, the proposed approach promises higher resource efficiency in comparison with all local execution and random offloading algorithms. The results obtained in this paper can be applied to design the future healthcare service pricing scheme of the IoT-based intelligent system.
Yangzhe Liao; Xinhui Qiao; Quan Yu; Quan Liu. Intelligent dynamic service pricing strategy for multi-user vehicle-aided MEC networks. Future Generation Computer Systems 2020, 114, 15 -22.
AMA StyleYangzhe Liao, Xinhui Qiao, Quan Yu, Quan Liu. Intelligent dynamic service pricing strategy for multi-user vehicle-aided MEC networks. Future Generation Computer Systems. 2020; 114 ():15-22.
Chicago/Turabian StyleYangzhe Liao; Xinhui Qiao; Quan Yu; Quan Liu. 2020. "Intelligent dynamic service pricing strategy for multi-user vehicle-aided MEC networks." Future Generation Computer Systems 114, no. : 15-22.
Mobile edge computing (MEC) based solutions are essential and of great significance for a wide range of promising 5G wireless big data services such as remote healthcare systems and AR/VR games. Present research in this area focuses on the downlink resource allocation scenarios from MEC servers to user equipments (UEs). This paper considers a multi-user MEC-enabled wireless communication system, where UEs suffer limited communication and computation resources. To achieve higher energy efficiency and the better experience for UEs, we aim to maximize the number of offloaded tasks for all UEs in uplink communication while maintaining the computation resources of MEC at an acceptable level. The formulated problem is an NP-hard mixed-integer nonlinear programming problem and it is a challenge to solve it efficiently. As such, an efficient low-complexity heuristic algorithm is proposed, which provides a near-optimal solution with a low time cost. The results show that the proposed scheme achieves the higher number of successful offloaded tasks than the existing centralized resource allocation algorithm (CRAA) and centralized decision and resource allocation algorithm that UEs with the largest saved energy consumption accepted first (CAR-E) under different scenarios. Moreover, the relationship between the optimal transmission power and the computation resource of MEC is investigated. The results obtained in this paper can be extended to design a novel framework of communication, computation and smart coded caching MEC networks.
Yangzhe Liao; Liqing Shou; Quan Yu; Qingsong Ai; Quan Liu. Joint offloading decision and resource allocation for mobile edge computing enabled networks. Computer Communications 2020, 154, 361 -369.
AMA StyleYangzhe Liao, Liqing Shou, Quan Yu, Qingsong Ai, Quan Liu. Joint offloading decision and resource allocation for mobile edge computing enabled networks. Computer Communications. 2020; 154 ():361-369.
Chicago/Turabian StyleYangzhe Liao; Liqing Shou; Quan Yu; Qingsong Ai; Quan Liu. 2020. "Joint offloading decision and resource allocation for mobile edge computing enabled networks." Computer Communications 154, no. : 361-369.
Inspired by the recent developments of the Internet of Things (IoT) relay and mobile edge computing (MEC), a hospital/home-based medical monitoring framework is proposed, in which the intensive computing tasks from the implanted sensors can be efficiently executed by on-body wearable devices or a coordinator-based MEC (C-MEC). In this paper, we first propose a wireless relay-enabled task offloading mechanism that consists of a network model and a computation model. Moreover, to manage the computation resources among all relays, a task offloading decision model and the best task offloading recipient selection function is given. The performance evaluation considers different computation schemes under the predetermined link quality condition regarding the selected vital quality of service (QoS) metrics. After demonstrating the channel characterization and network topology, the performance evaluation is implemented under different scenarios regarding the network lifetime of all relays, network residual energy status, total number of locally executed packets, path loss (PL), and service delay. The results show that data transmission without the offloading scheme outperforms the offload-based technique regarding network lifetime. Moreover, the high computation capacity scenario achieves better performance regarding PL and the total number of locally executed packets.
Yangzhe Liao; Quan Yu; Yi Han; Mark S. Leeson. Relay-Enabled Task Offloading Management for Wireless Body Area Networks. Applied Sciences 2018, 8, 1409 .
AMA StyleYangzhe Liao, Quan Yu, Yi Han, Mark S. Leeson. Relay-Enabled Task Offloading Management for Wireless Body Area Networks. Applied Sciences. 2018; 8 (8):1409.
Chicago/Turabian StyleYangzhe Liao; Quan Yu; Yi Han; Mark S. Leeson. 2018. "Relay-Enabled Task Offloading Management for Wireless Body Area Networks." Applied Sciences 8, no. 8: 1409.