BeCome: Blockchain-Enabled Computation Offloading for IoT in Mobile Edge Computing

Xu, Xiaolong; Zhang, Xuyun; Gao, Honghao; Xue, Yuan; Qi, Lianyong; Dou, Wanchun

doi:10.1109/tii.2019.2936869

Cited by 260 publications

(136 citation statements)

References 27 publications

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“…Both of these heuristic algorithms can effectively solve the task assignment problem. Xu et al [64] proposed an offloading method using block chain technology. It can guarantee the loss of data in offloading tasks under edge computing.…”

Section: Mecmentioning

confidence: 99%

Study QoS Optimization and Energy Saving Techniques in Cloud, Fog, Edge, and IoT

Wang

Sun

et al. 2020

Complexity

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With an increase of service users’ demands on high quality of services (QoS), more and more efficient service computing models are proposed. The development of cloud computing, fog computing, and edge computing brings a number of challenges, e.g., QoS optimization and energy saving. We do a comprehensive survey on QoS optimization and energy saving in cloud computing, fog computing, edge computing, and IoT environments. We summarize the main challenges and analyze corresponding solutions proposed by existing works. This survey aims to help readers have a deeper understanding on the concepts of different computing models and study the techniques of QoS optimization and energy saving in these models.

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Section: Mecmentioning

confidence: 99%

Study QoS Optimization and Energy Saving Techniques in Cloud, Fog, Edge, and IoT

Wang

Sun

et al. 2020

Complexity

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“…Our further work will develop effective techniques to evaluate the temporal performance and to resolve the resource conflict problem. Additionally, edge computing and the Internet of ings (IOT) are two areas closely related to cloud computing [5,[39][40][41][42][43][44]. Based on the cloud service, our further work will extend the proposed approach to the two areas.…”

Section: Resultsmentioning

confidence: 99%

A Correctness Checking Approach for Collaborative Business Processes in the Cloud

Wang

Xiang

et al. 2020

Complexity

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With the increasing popularity of cloud computing, especially the emergence of Business Process as a Service (BPaaS), more and more enterprises construct their process collaborations based on BPaaS services. Indeed, the collaborative business process built by BPaaS services can be seen as a complex system, as it covers multiple business processes (i.e., BPaaS services) and they act independently. Since business processes corresponding to BPaaS services are usually provided by different cloud service providers, and their interactions are unforeseen in advance, in actual execution, some behavioral anomalies (e.g., deadlocks) may occur. To this end, based on BPaaS services, we propose an approach to build process collaborations in the cloud. In this approach, we first model collaborative business processes using open nets. Then, we check their correctness based on stubborn sets. Finally, in case they are partially correct, we generate reliable paths for the coordination execution between business processes. Our approach is implemented in the PIPE (an open tool for Petri nets) and evaluated on actual cases that show its effectiveness and efficiency.

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“…We highlight the intuitions and key technologies of deep learning-driven wireless communication from the aspects of end-to-end communication, signal detection, channel estimation and compression sensing, encoding and decoding, and security and privacy. Main challenges, potential opportunities and future trends in incorporating deep learning schemes in wireless communications environments are further illustrated.further fulfill the requirements of future wireless communication systems, e.g., beyond the fifth-generation (B5G) networks.Along with the fast convergence of communication and computing in popular paradigms of edge computing and cloud computing [7,8], intelligent communication is considered to be one of the mainstream directions for the extensive development of future 5G and beyond wireless networks, since it can optimize wireless communication systems performance. In addition, with tremendous progress in artificial intelligence (AI) technology, it offers alternative options for addressing these challenges and replacing the design concepts of conventional wireless communications.…”

mentioning

confidence: 99%

“…Along with the fast convergence of communication and computing in popular paradigms of edge computing and cloud computing [7,8], intelligent communication is considered to be one of the mainstream directions for the extensive development of future 5G and beyond wireless networks, since it can optimize wireless communication systems performance. In addition, with tremendous progress in artificial intelligence (AI) technology, it offers alternative options for addressing these challenges and replacing the design concepts of conventional wireless communications.…”

mentioning

confidence: 99%

Deep learning-driven wireless communication for edge-cloud computing: opportunities and challenges

Deng

2020

J Cloud Comp

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Future wireless communications are becoming increasingly complex with different radio access technologies, transmission backhauls, and network slices, and they play an important role in the emerging edge computing paradigm, which aims to reduce the wireless transmission latency between end-users and edge clouds. Deep learning techniques, which have already demonstrated overwhelming advantages in a wide range of internet of things (IoT) applications, show significant promise for solving such complicated real-world scenarios. Although the convergence of radio access networks and deep learning is still in the preliminary exploration stage, it has already attracted tremendous concern from both academia and industry. To address emerging theoretical and practical issues, ranging from basic concepts to research directions in future wireless networking applications and architectures, this paper mainly reviews the latest research progress and major technological deployment of deep learning in the development of wireless communications. We highlight the intuitions and key technologies of deep learning-driven wireless communication from the aspects of end-to-end communication, signal detection, channel estimation and compression sensing, encoding and decoding, and security and privacy. Main challenges, potential opportunities and future trends in incorporating deep learning schemes in wireless communications environments are further illustrated.further fulfill the requirements of future wireless communication systems, e.g., beyond the fifth-generation (B5G) networks.Along with the fast convergence of communication and computing in popular paradigms of edge computing and cloud computing [7,8], intelligent communication is considered to be one of the mainstream directions for the extensive development of future 5G and beyond wireless networks, since it can optimize wireless communication systems performance. In addition, with tremendous progress in artificial intelligence (AI) technology, it offers alternative options for addressing these challenges and replacing the design concepts of conventional wireless communications. Deep learning (DL) is playing an increasingly crucial role in the field of wireless communications due to its high efficiency in dealing with tremendous complex calculations, and is regarded as one of the effective tools for dealing with communication issues. Although deep learning has performed well in some IoT applications, "no free lunch" theorem [9] shows that a model cannot solve all problems once and for all, and we cannot learn a general model for a wide range of communication scenarios. This means that for any particular mobile and wireless network issue, we still need to adopt different deep learning architectures such as convolutional neural networks (CNN), deep neural networks (DNN) and recurrent neural networks (RNN), in order to achieve better performance of the communication systems.As a classic model of deep learning, autoencoder is widely used in the design paradigms of communication system ...

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BeCome: Blockchain-Enabled Computation Offloading for IoT in Mobile Edge Computing

Cited by 260 publications

References 27 publications

Study QoS Optimization and Energy Saving Techniques in Cloud, Fog, Edge, and IoT

Study QoS Optimization and Energy Saving Techniques in Cloud, Fog, Edge, and IoT

A Correctness Checking Approach for Collaborative Business Processes in the Cloud

Deep learning-driven wireless communication for edge-cloud computing: opportunities and challenges

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