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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.
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.
Network lifetime maximization of wireless biomedical implant systems is one of the major research challenges of wireless body area networks (WBANs). In this paper, a mutual information (MI)-based incremental relaying communication protocol is presented where several on-body relay nodes and one coordinator are attached to the clothes of a patient. Firstly, a comprehensive analysis of a system model is investigated in terms of channel path loss, energy consumption, and the outage probability from the network perspective. Secondly, only when the MI value becomes smaller than the predetermined threshold is data transmission allowed. The communication path selection can be either from the implanted sensor to the on-body relay then forwards to the coordinator or from the implanted sensor to the coordinator directly, depending on the communication distance. Moreover, mathematical models of quality of service (QoS) metrics are derived along with the related subjective functions. The results show that the MI-based incremental relaying technique achieves better performance in comparison to our previous proposed protocol techniques regarding several selected performance metrics. The outcome of this paper can be applied to intra-body continuous physiological signal monitoring, artificial biofeedback-oriented WBANs, and telemedicine system design.
Yangzhe Liao; Mark S. Leeson; Qing Cai; Qingsong Ai; Quan Liu. Mutual-Information-Based Incremental Relaying Communications for Wireless Biomedical Implant Systems. Sensors 2018, 18, 515 .
AMA StyleYangzhe Liao, Mark S. Leeson, Qing Cai, Qingsong Ai, Quan Liu. Mutual-Information-Based Incremental Relaying Communications for Wireless Biomedical Implant Systems. Sensors. 2018; 18 (2):515.
Chicago/Turabian StyleYangzhe Liao; Mark S. Leeson; Qing Cai; Qingsong Ai; Quan Liu. 2018. "Mutual-Information-Based Incremental Relaying Communications for Wireless Biomedical Implant Systems." Sensors 18, no. 2: 515.