EEDTO: An Energy-Efficient Dynamic Task Offloading Algorithm for Blockchain-Enabled IoT-Edge-Cloud Orchestrated Computing

Wu, Huaming; Wolter, Katinka; Jiao, Pengfei; Deng, Yingjun; Zhao, Y. B.; Xu, Minxian

doi:10.1109/jiot.2020.3033521

Cited by 202 publications

(88 citation statements)

References 50 publications

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“…MEC servers are much closer to mobile devices and thus have lower latency, while MCC servers can provide flexible and scalable computing capability to support complicated applications [34]. For simplicity, we do not explicitly differentiate between edge servers and remote cloud servers in our formulations.…”

Section: Discussionmentioning

confidence: 99%

Cloudroid Swarm: A QoS‐Aware Framework for Multirobot Cooperation Offloading

Zhai

Ding

Zhang

et al. 2021

Wireless Communications and Mobile Computing

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Computation offloading has been widely recognized as an effective way to promote the capabilities of resource-constrained mobile devices. Recent years have seen a renewal of the importance of this technology in the emerging field of mobile robots, supporting resource-intensive robot applications. However, cooperating to solve complex tasks in the physical world, which is a significant feature of a robot swarm compared to traditional mobile computing devices, has not received in-depth attention in research concerned with traditional computation offloading. In this study, we propose an approach named cooperation offloading, which offloads the intensive communication among robots as well as the computation for compute-intensive and data-intensive tasks. We analyze the performance gain of cooperation offloading by formalizing multirobot cooperative models; in addition, we study offloading decisions. Based on this approach, we design a cloud robotic framework named Cloudroid Swarm and develop several QoS-aware mechanisms to provide a general solution to cooperation offloading with QoS assurance in multirobot cooperative scenes. We implement Cloudroid Swarm to transparently migrate multirobot applications to cloud servers without any code modification. We evaluate our framework using three different multirobot cooperative applications. Our results show that Cloudroid Swarm can be applied to various robotic applications and real-world environments and bring significant benefits in terms of both network optimization and task performance. Besides, our framework has good scalability and can do support as many as 256 robot entities simultaneously.

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Section: Discussionmentioning

confidence: 99%

Cloudroid Swarm: A QoS‐Aware Framework for Multirobot Cooperation Offloading

Zhai

Ding

Zhang

et al. 2021

Wireless Communications and Mobile Computing

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“…Edge Computing [61] Propose an energy-efficient IoT task offloading algorithm for blockchain-enabled edge computing.…”

Section: Ref Contributionsmentioning

confidence: 99%

“…Edge Computing: The tradeoff between limited resources and required latency is a major challenge for edge computing. To deal with this issue, Wu et al [61] considered the collaboration of edge and cloud. They proposed an energy-efficient IoT task offloading algorithm for blockchain-enabled edge computing.…”

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confidence: 99%

Blockchain-Enabled Edge Intelligence for IoT: Background, Emerging Trends and Open Issues

Wang

Leung

2021

Future Internet

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Blockchain, a distributed ledger technology (DLT), refers to a list of records with consecutive time stamps. This decentralization technology has become a powerful model to establish trust among trustless entities, in a verifiable manner. Motivated by the recent advancement of multi-access edge computing (MEC) and artificial intelligence (AI), blockchain-enabled edge intelligence has become an emerging technology for the Internet of Things (IoT). We review how blockchain-enabled edge intelligence works in the IoT domain, identify the emerging trends, and suggest open issues for further research. To be specific: (1) we first offer some basic knowledge of DLT, MEC, and AI; (2) a comprehensive review of current peer-reviewed literature is given to identify emerging trends in this research area; and (3) we discuss some open issues and research gaps for future investigations. We expect that blockchain-enabled edge intelligence will become an important enabler of future IoT, providing trust and intelligence to satisfy the sophisticated needs of industries and society.

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“…e third one is task offloading with the goal of optimizing the weighted sum of the mobile device's energy consumption and the task processing delay. Wu et al [24] propose a Lyapunov optimization-based energy-efficient task offloading scheme to control the computational and communication overheads and further choose optimal computational location for the application to minimize energy consumption and task processing time. However, all above works mainly focus on the independent task scheduling in MEC.…”

Section: Related Workmentioning

confidence: 99%

Reinforcement Learning for Security-Aware Workflow Application Scheduling in Mobile Edge Computing

Huang

Xiang

et al. 2021

Security and Communication Networks

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Mobile edge computing as a novel computing paradigm brings remote cloud resource to the edge servers nearby mobile users. Within one-hop communication range of mobile users, a number of edge servers equipped with enormous computation and storage resources are deployed. Mobile users can offload their partial or all computation tasks of a workflow application to the edge servers, thereby significantly reducing the completion time of the workflow application. However, due to the open nature of mobile edge computing environment, these tasks, offloaded to the edge servers, are susceptible to be intentionally overheard or tampered by malicious attackers. In addition, the edge computing environment is dynamical and time-variant, which results in the fact that the existing quasistatic workflow application scheduling scheme cannot be applied to the workflow scheduling problem in dynamical mobile edge computing with malicious attacks. To address these two problems, this paper formulates the workflow scheduling problem with risk probability constraint in the dynamic edge computing environment with malicious attacks to be a Markov Decision Process (MDP). To solve this problem, this paper designs a reinforcement learning-based security-aware workflow scheduling (SAWS) scheme. To demonstrate the effectiveness of our proposed SAWS scheme, this paper compares SAWS with MSAWS, AWM, Greedy, and HEFT baseline algorithms in terms of different performance parameters including risk probability, security service, and risk coefficient. The extensive experiments results show that, compared with the four baseline algorithms in workflows of different scales, the SAWS strategy can achieve better execution efficiency while satisfying the risk probability constraints.

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EEDTO: An Energy-Efficient Dynamic Task Offloading Algorithm for Blockchain-Enabled IoT-Edge-Cloud Orchestrated Computing

Cited by 202 publications

References 50 publications

Cloudroid Swarm: A QoS‐Aware Framework for Multirobot Cooperation Offloading

Cloudroid Swarm: A QoS‐Aware Framework for Multirobot Cooperation Offloading

Blockchain-Enabled Edge Intelligence for IoT: Background, Emerging Trends and Open Issues

Reinforcement Learning for Security-Aware Workflow Application Scheduling in Mobile Edge Computing

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