Topic
Edge computing
About: Edge computing is a research topic. Over the lifetime, 11657 publications have been published within this topic receiving 148533 citations.
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TL;DR: A deep reinforcement learning (DRL) based solution is proposed to determine an optimal charging scheduling policy for low-battery EVs and the solution with incremental update achieves much higher computation efficiency than conventional game-theoretical method in dynamic EV charging.
Abstract: Smart grid delivers power with two-way flows of electricity and information with the support of information and communication technologies. Electric vehicles (EVs) with rechargeable batteries can be powered by external sources of electricity from the grid, and thus charging scheduling that guides low-battery EVs to charging services is significant for service quality improvement of EV drivers. The revolution of communications and data analytics driven by massive data in smart grid brings many challenges as well as chances for EV charging scheduling, and how to schedule EV charging in a smart and resilient way has inevitably become a crucial problem. Toward this end, we in this paper leverage the techniques of software defined networking and vehicular edge computing to investigate a joint problem of fast charging station selection and EV route planning. Our objective is to minimize the total overhead from users’ perspective, including time and charging fares in the whole process, considering charging availability and electricity price fluctuation. A deep reinforcement learning (DRL) based solution is proposed to determine an optimal charging scheduling policy for low-battery EVs. Besides, in response to dynamic EV charging, we further develop a resilient EV charging strategy based on incremental update, with EV drivers’ user experience being well considered. Extensive simulations demonstrate that our proposed DRL-based solution obtains near-optimal EV charging overhead with good adaptivity, and the solution with incremental update achieves much higher computation efficiency than conventional game-theoretical method in dynamic EV charging.
127 citations
TL;DR: The recent advances in fog radio access network research, hybrid fog-cloud architecture, and system design issues are described, and the opportunities of integrating the GPP platform with F-RAN architecture are discussed.
Abstract: Cloud-based wireless networking system applies centralized resource pooling to improve operation efficiency. Fog-based wireless networking system reduces latency by placing processing units in the network edge. Confluence of fog and cloud design paradigms in 5G radio access network will better support diverse applications. In this article, we describe the recent advances in fog radio access network (F-RAN) research, hybrid fog-cloud architecture, and system design issues. Furthermore, the GPP platform facilitates the confluence of computational and communications processing. Through observations from GPP platform testbed experiments and simulations, we discuss the opportunities of integrating the GPP platform with F-RAN architecture.
127 citations
TL;DR: A voice pathology detection system using deep learning on the mobile healthcare framework using the existing robust CNN models and the VGG-16 and CaffeNet models are investigated in the paper.
Abstract: The feasibility and popularity of mobile healthcare are currently increasing. The advancement of modern technologies, such as wireless communication, data processing, the Internet of Things, cloud, and edge computing, makes mobile healthcare simpler than before. In addition, the deep learning approach brings a revolution in the machine learning domain. In this paper, we investigate a voice pathology detection system using deep learning on the mobile healthcare framework. A mobile multimedia healthcare framework is also designed. In the voice pathology detection system, voices are captured using smart mobile devices. Voice signals are processed before being fed to a convolutional neural network (CNN). We use a transfer learning technique to use the existing robust CNN models. In particular, the VGG-16 and CaffeNet models are investigated in the paper. The Saarbrucken voice disorder database is used in the experiments. Experimental results show that the voice pathology detection accuracy reaches up to 97.5% using the transfer learning of CNN models.
127 citations
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TL;DR: This paper presents a comprehensive literature review on applications of economic and pricing models for resource management in cloud networking, and surveys a variety of incentive mechanisms using the pricing strategies in sharing resources in edge computing.
Abstract: This paper presents a comprehensive literature review on applications of economic and pricing models for resource management in cloud networking. To achieve sustainable profit advantage, cost reduction, and flexibility in provisioning of cloud resources, resource management in cloud networking requires adaptive and robust designs to address many issues, e.g., resource allocation, bandwidth reservation, request allocation, and workload allocation. Economic and pricing models have received a lot of attention as they can lead to desirable performance in terms of social welfare, fairness, truthfulness, profit, user satisfaction, and resource utilization. This paper reviews applications of the economic and pricing models to develop adaptive algorithms and protocols for resource management in cloud networking. Besides, we survey a variety of incentive mechanisms using the pricing strategies in sharing resources in edge computing. In addition, we consider using pricing models in cloud-based Software Defined Wireless Networking (cloud-based SDWN). Finally, we highlight important challenges, open issues and future research directions of applying economic and pricing models to cloud networking
127 citations
TL;DR: In this article, the secrecy outage probability minimization problem was investigated by taking the priority of two users into account, and characterized the optimal secrecy offloading rates and power allocations with closed-form expressions.
Abstract: Mobile edge computing (MEC) has been envisaged as a promising technique in the next-generation wireless networks. In order to improve the security of computation tasks offloading and enhance user connectivity, physical layer security and non-orthogonal multiple access (NOMA) are studied in MEC-aware networks. The secrecy outage probability is adopted to measure the secrecy performance of computation offloading by considering a practically passive eavesdropping scenario. The weighted sum-energy consumption minimization problem is firstly investigated subject to the secrecy offloading rate constraints, the computation latency constraints and the secrecy outage probability constraints. The semi-closed form expression for the optimal solution is derived. We then investigate the secrecy outage probability minimization problem by taking the priority of two users into account, and characterize the optimal secrecy offloading rates and power allocations with closed-form expressions. Numerical results demonstrate that the performance of our proposed design are better than those of the alternative benchmark schemes.
127 citations