Decentralized Blockchain-Based and Trust-Aware Task Offloading Strategy for Healthcare IoT

Ren, Junyu; Qin, Tuanfa

doi:10.1109/jiot.2023.3286900

Cited by 5 publications

(2 citation statements)

References 45 publications

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“…Ren et al [24] designed a task-offloading strategy for a centralized low latency, secure, and reliable decision method (LSRDM-EH) with strong emergency handling capabilities and a dual-layer multidimensional comprehensive security strategy based on blockchain. In [25], a task-offloading strategy based on blockchain and trust awareness (BBTAS), through which task-offloading requests are published as blockchain transactions and automatically and securely executed by smart contracts (SCs). A blockchain based on a private WBAN platform to assist wearable IoT devices in healthcare services was proposed in [26].…”

Section: Existing Research Workmentioning

confidence: 99%

“…The novelty of this article comes from the paradigm of blockchain of WBANs and task-offloading with DRL of the proposed work. √ × energy consumption management scheme Zhang et al [10,17] √ × total cost, load balance CTOS-DE, DNN-TOTA Deng et al [11] × √ stability, security CP-ABE Xiao et al [12] × √ security, reliability architecture model Wang et al [13] × √ security safety protocol Xu et al [14] × √ security, energy consumption security scheme Yuan et al [16,18] √ × throughput, overall utilization A3C, DQN-FATOA Yuan et al [19] √ × resource allocation TPOS Liao et al [20,23] √ × computing capacity, latency system modeling Ning et al [21] √ × system average cost system modeling Ren et al [24,25] × √ latency, security, reliability LSRDM-EH, BBTAS Baucas et al [26] × √ security private platform Son et al [27] × √ data integrity CP-ABE Proposed work √ √ system average cost, security, QoE D3QN…”

Section: Research Gapmentioning

confidence: 99%

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Task-Offloading Strategy of Mobile Edge Computing for WBANs

Li,

Zhang

2024

Electronics

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In recent years, mobile edge computing has become one of the popular methods to provide computing resources for the body area network, but existing research only considers the problem of minimizing the cost of offloading when solving the optimization problem of task-offloading, ignoring the trust problem of edge computing nodes, and offloading tasks on edge nodes may cause user information disclosure and reduce the quality of user experience. In response to this situation, this study aims to minimize the average user cost and designs a task-offloading strategy based on the D3QN (dueling double deep Q-network) algorithm in conjunction with the blockchain information security storage model. This strategy uses deep reinforcement learning algorithms to obtain the minimum average offloading cost of the system while considering user latency, energy consumption, and data protection conditions. The experimental simulation results show that compared to traditional schemes and other reinforcement learning-based schemes, this scheme can more effectively reduce the average cost of the system, and the average cost is reduced by 31.25% when reaching convergence. In addition, as the complexity of the model increases, this scheme can provide users with better experience quality, with 53.7% of the 1000 users having a very good experience quality.

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Section: Existing Research Workmentioning

confidence: 99%

Section: Research Gapmentioning

confidence: 99%

Task-Offloading Strategy of Mobile Edge Computing for WBANs

Li,

Zhang

2024

Electronics

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Cost-aware workflow offloading in edge-cloud computing using a genetic algorithm

Abdi,

Ashjaei,

Mubeen

2024

J Supercomput

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The edge-cloud computing continuum effectively uses fog and cloud servers to meet the quality of service (QoS) requirements of tasks when edge devices cannot meet those requirements. This paper focuses on the workflow offloading problem in edge-cloud computing and formulates this problem as a nonlinear mathematical programming model. The objective function is to minimize the monetary cost of executing a workflow while satisfying constraints related to data dependency among tasks and QoS requirements, including security and deadlines. Additionally, it presents a genetic algorithm for the workflow offloading problem to find near-optimal solutions with the cost minimization objective. The performance of the proposed mathematical model and genetic algorithm is evaluated on several real-world workflows. Experimental results demonstrate that the proposed genetic algorithm can find admissible solutions comparable to the mathematical model and outperforms particle swarm optimization, bee life algorithm, and a hybrid heuristic-genetic algorithm in terms of workflow execution costs.

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