The W<sup>5</sup> Framework for Computation Offloading in the Internet of Things

Elazhary, Hanan; Sabbeh, Sahar F.

doi:10.1109/access.2018.2829840

Cited by 12 publications

(5 citation statements)

References 24 publications

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“…Another work in [12] proposed an IoT architecture for executing healthcare applications on clouds, but the optimization for memory usage of MDs in the offloading has been overlooked. In fact, there is close relationship between memory usage and the data task as investigated in the recent work [16]. That is, the higher size of data tasks to be offloaded, the higher device memory required to handle the task.…”

Section: A Health Data Offloadingmentioning

confidence: 83%

BEdgeHealth: A Decentralized Architecture for Edge-Based IoMT Networks Using Blockchain

Nguyen

Pathirana

Ding

et al. 2021

IEEE Internet Things J.

128

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The healthcare industry has witnessed significant transformations in e-health services by using mobile edge computing (MEC) and blockchain to facilitate healthcare operations. Many MEC-blockchain-based schemes have been proposed, but some critical technical challenges still remain, such as low quality of services (QoS), data privacy and system security vulnerabilities. In this paper, we propose a new decentralized health architecture, called BEdgeHealth that integrates MEC and blockchain for data offloading and data sharing in distributed hospital networks. First, a data offloading scheme is proposed where mobile devices can offload health data to a nearby MEC server for efficient computation with privacy awareness. Moreover, we design a data sharing scheme which enables data exchanges among healthcare users by leveraging blockchain and interplanetary file system. Particularly, a smart contractbased authentication mechanism is integrated with MEC to perform decentralized user access verification at the network edge without requiring any central authority. The real-world experiment results and evaluations demonstrate the effectiveness of the proposed BEdgeHealth architecture in terms of improved QoS with data privacy and security guarantees, compared to the existing schemes.

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Section: A Health Data Offloadingmentioning

confidence: 83%

BEdgeHealth: A Decentralized Architecture for Edge-Based IoMT Networks Using Blockchain

Nguyen

Pathirana

Ding

et al. 2021

IEEE Internet Things J.

128

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“…The latency and energy consumption was improved by employing the Gibbs sampling and Lyapunov optimization. In the W5 framework, the authors proposed an agent‐based task offloading approach for the IoT applications like multimedia, smart homes, etc 25 . The authors have considered three situations for offloading decisions and processing the IoT applications at suitable platforms.…”

Section: Related Workmentioning

confidence: 99%

“…In the W5 framework, the authors proposed an agent-based task offloading approach for the IoT applications like multimedia, smart homes, etc. 25 The authors have considered three situations for offloading decisions and processing the IoT applications at suitable platforms. The authors presented an orchestration scheme for task offloading in IoT-mobile edge computing.…”

mentioning

confidence: 99%

Secured data offloading using reinforcement learning and Markov decision process in mobile edge computing

2023

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Mobile Internet services are developing rapidly for several applications based on computational ability such as augmented/virtual reality, vehicular networks, etc. The mobile terminals are enabled using mobile edge computing (MEC) for offloading the task at the edge of the cellular networks, but offloading is still a challenging issue due to the dynamism, and uncertainty of upcoming IoT requests and wireless channel state. Moreover, securing the offloading data enhanced the challenges of computational complexities and required a secure and efficient offloading technique. To tackle the mentioned issues, a reinforcement learning‐based Markov decision process offloading model is proposed that optimized energy efficiency, and mobile users' time by considering the constrained computation of IoT devices, moreover guarantees efficient resource sharing among multiple users. An advanced encryption standard is employed in this work to fulfil the requirements of data security. The simulation outputs reveal that the proposed approach surpasses the existing baseline models for offloading overhead and service cost QoS parameters ensuring secure data offloading.

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“…al. [24] formulated IoT offloading as a decision problem. They proposed a W5 reference framework for future research works.…”

Section: A Related Work and Contributionsmentioning

confidence: 99%

Process Migration-Based Computational Offloading Framework for IoT-Supported Mobile Edge/Cloud Computing

Yousafzai

Yaqoob

Imran

et al. 2020

IEEE Internet Things J.

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Mobile devices have become an indispensable component of Internet of Things (IoT). However, these devices have resource constraints in processing capabilities, battery power, and storage space, thus hindering the execution of computationintensive applications that often require broad bandwidth, stringent response time, long battery life, and heavy computing power. Mobile cloud computing and mobile edge computing (MEC) are emerging technologies that can meet the aforementioned requirements using offloading algorithms. In this paper, we analyze the effect of platform-dependent native applications on computational offloading in edge networks and propose a lightweight process migration-based computational offloading framework. The proposed framework does not require application binaries at edge servers and thus seamlessly migrates native applications. The proposed framework is evaluated using an experimental testbed. Numerical results reveal that the proposed framework saves almost 44% of the execution time and 84% of the energy consumption. Hence, the proposed framework shows profound potential for resource-intensive IoT application processing in MEC.

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The W⁵ Framework for Computation Offloading in the Internet of Things

Cited by 12 publications

References 24 publications

BEdgeHealth: A Decentralized Architecture for Edge-Based IoMT Networks Using Blockchain

BEdgeHealth: A Decentralized Architecture for Edge-Based IoMT Networks Using Blockchain

Secured data offloading using reinforcement learning and Markov decision process in mobile edge computing

Process Migration-Based Computational Offloading Framework for IoT-Supported Mobile Edge/Cloud Computing

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The W5 Framework for Computation Offloading in the Internet of Things

Cited by 12 publications

References 24 publications

BEdgeHealth: A Decentralized Architecture for Edge-Based IoMT Networks Using Blockchain

BEdgeHealth: A Decentralized Architecture for Edge-Based IoMT Networks Using Blockchain

Secured data offloading using reinforcement learning and Markov decision process in mobile edge computing

Process Migration-Based Computational Offloading Framework for IoT-Supported Mobile Edge/Cloud Computing

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Product

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The W⁵ Framework for Computation Offloading in the Internet of Things