Joint Task Offloading and Resource Allocation for Accuracy-Aware Machine-Learning-Based IIoT Applications

Fan, Wenhao; Li, Shenmeng; Liu, Jie; Su, Yi; Wu, Fan; Liu, Yuanan

doi:10.1109/jiot.2022.3181990

Cited by 22 publications

(5 citation statements)

References 38 publications

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“…While it shows promising energy efficiency and EED reduction results, its primary focus on MEC and D2D might limit its general applicability. [99] develop a joint TO and RA system, targeting accuracy-aware ML-based IIoT applications in edge-cloud network architectures. This model is adept at minimizing the long-term average system cost, considering the inference accuracy of ML models.…”

Section: Related Workmentioning

confidence: 99%

ODTRA-Based Task Offload Optimization for Industrial Internet of Things: Improving Efficiency and Performance With Digital Twins and Metaheuristic Optimization

Swaminathan,

Rajagopalan,

Nidumolu

et al. 2024

IEEE Access

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The Industrial Internet of Things (IIoT) has revolutionized several industries by improving the communication of sensor data among interconnected machines and systems. IIoT frameworks, on the other hand, can be challenging to set up and keep functioning several times. This paper introducing Task Offloading (TO) into the Internet of Things (IoT) brings about plenty of challenges, which will be addressed throughout this study. Tasks that use an enormous number of resources are allocated to remote server locations in the cloud in order to be performed. As a result of the objective of optimizing decisions involving Optimize Task Offloading (OTO), it should be considered that Digital Twins (DT) be developed. DTs are automated backups of physical objects or systems that can be used to perform data collection in real-time, management's decisions, and optimization. DTs are also identified as digital copies of things. Using DT, constantly tracked in real-time, and Metaheuristic Optimization (MO) computational approaches, this research recommends a Task Offloading Model (TOM) for the IIoT. In the model, the Task Execution Time (TET) is reduced to a minimum by limited factors such as the server's computing power, the constraints on bandwidth, and the Energy Consumption (EC) of the device. In order to efficiently increase OTO results, the Offloading with Digital Twins and Raindrop Algorithm (ODTRA) method that has been industrialized makes use of the Water Cycle Metaphor (WCM) and the Probabilistic Recursive Local Search (PRLS) technique. It is feasible to implement the algorithm in real-time IIoT environments through the presence of DT in order to progress decision-making and provide real-time monitoring. This work reviews the assumptions of a research study that analyzes the performance of OTO in IIoT environments, with a detailed emphasis on the potential for real-time deployment.

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Section: Related Workmentioning

confidence: 99%

ODTRA-Based Task Offload Optimization for Industrial Internet of Things: Improving Efficiency and Performance With Digital Twins and Metaheuristic Optimization

Swaminathan,

Rajagopalan,

Nidumolu

et al. 2024

IEEE Access

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“…These solutions are typically complexly designed to address the limits imposed by data constraints and the dynamic nature of the MEC system [17]. This imposes significant pressure on MEC server resources and makes its practical implementation unfeasible [17,18]. In contrast to the current distributed techniques, the proposed strategy enables MEC servers to make independent decisions by using local data at each server and aggregated global knowledge.…”

Section: Distributed Service Cachingmentioning

confidence: 99%

“…The curse of dimensionality may result in delayed learning and increased computational demands. Moreover, using these techniques in a distributed approach increases the complexity of the training process while simultaneously raising challenges in terms of convergence and stability [18].…”

Section: Introductionmentioning

confidence: 99%

Federated Learning-Based Service Caching in Multi-Access Edge Computing System

Tran,

Nguyen

et al. 2024

Applied Sciences

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Multi-access edge computing (MEC) brings computations closer to mobile users, thereby decreasing service latency and providing location-aware services. Nevertheless, given the constrained resources of the MEC server, it is crucial to provide a limited number of services that properly fulfill the demands of users. Several static service caching approaches have been proposed. However, the effectiveness of these strategies is constrained by the dynamic nature of the system states and user demand patterns. To mitigate this problem, several investigations have been conducted on dynamic service caching techniques that can be categorized as centralized and distributed. However, centralized approaches typically require gathering comprehensive data from the entire system. This increases the burden on resources and raises concerns regarding data security and privacy. By contrast, distributed strategies require the formulation of complicated optimization problems without leveraging the inherent characteristics of the data. This paper proposes a distributed service caching strategy based on federated learning (SCFL) that works efficiently in a distributed system with user mobility. An autoencoder model is utilized to extract features regarding the service request distribution of individual MEC servers. The global model is then generated using federated learning, which is utilized to make service-caching decisions. Extensive experiments are conducted to demonstrate that the performance of the proposed method is superior to that of other methods.

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“…Wang et al [25] conceived a revenue-maximizing framework for cellular networks by jointly considering the computation offloading, resource allocation and content caching. Focusing on accuracy-aware machine learning (ML) tasks in the Internet of Industrial Things, Fan et al [26] constructed a longterm average system cost optimization framework by jointly considering the resources of sensors, edge server and cloud server, as well as the inference accuracy of the ML tasks. However, when the application scenario changes from cellular networks to AUV-aided underwater networks, the research mentioned above is no longer applicable.…”

Section: Related Workmentioning

confidence: 99%

Environment-Aware AUV Trajectory Design and Resource Management for Multi-Tier Underwater Computing

Hou

Wang

Bai

et al. 2023

IEEE J. Select. Areas Commun.

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The Internet of underwater things (IoUT) is envisioned to be an essential part of maritime activities. Given the IoUT devices' wide-area distribution and constrained transmit power, autonomous underwater vehicles (AUVs) have been widely adopted for collecting and forwarding the data sensed by IoUT devices to the surface-stations. In order to accommodate the diverse requirements of IoUT applications, it is imperative to conceive a multi-tier underwater computing (MTUC) framework by carefully harnessing both the computing and the communications as well as the storage resources of both the surface-station and of the AUVs as well as of the IoUT devices. Furthermore, to meet the stringent energy constraints of the IoUT devices and to reduce the operating cost of the MTUC framework, a joint environment-aware AUV trajectory design and resource management problem is formulated, which is a high-dimensional NP-hard problem. To tackle this challenge, we first transform the problem into a Markov decision process (MDP) and solve it with the aid of the asynchronous advantage actor-critic (A3C) algorithm. Our simulation results demonstrate the superiority of our scheme.

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Joint Task Offloading and Resource Allocation for Accuracy-Aware Machine-Learning-Based IIoT Applications

Cited by 22 publications

References 38 publications

ODTRA-Based Task Offload Optimization for Industrial Internet of Things: Improving Efficiency and Performance With Digital Twins and Metaheuristic Optimization

ODTRA-Based Task Offload Optimization for Industrial Internet of Things: Improving Efficiency and Performance With Digital Twins and Metaheuristic Optimization

Federated Learning-Based Service Caching in Multi-Access Edge Computing System

Environment-Aware AUV Trajectory Design and Resource Management for Multi-Tier Underwater Computing

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