Robotic Edge Resource Allocation for Agricultural Cyber-Physical System

Afrin, Mahbuba; Jin, Jiong; Rahman, Ashfaqur; Gasparri, Andrea; Tian, Yu-Chu; Kulkarni, Ambarish

doi:10.1109/tnse.2021.3103602

Cited by 12 publications

(4 citation statements)

References 32 publications

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“…The evaluation conducted on a robot navigation test bed reveals superior performance compared to the baseline long shortterm memory (LSTM) algorithm. In another seminal work [24], authors develop a congestion game-theoretic robotic edge resource allocation mechanism for CPS which minimizes task latency by satisfying the energy constraints of the resources applying to agriculture 4.0 as a use case scenario.…”

Section: A Related Workmentioning

confidence: 99%

A smart collaborative framework for dynamic multi-task offloading in IIoT-MEC networks

Zhang

et al. 2023

Peer-to-Peer Netw. Appl.

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To circumvent persistent connectivity to the cloud infrastructure, the current emphasis on computing at network edge devices in the multi-robot domain is a promising enabler for delay-sensitive jobs, yet its adoption is rife with challenges. This paper proposes a novel utility-aware dynamic task offloading strategy based on a multi-edge-robot system that takes into account computation, communication, and task execution load to minimize the overall service time for delay-sensitive applications. Prior to task offloading, continuous device, network, and task profiling are performed, and for each task assigned, an edge with maximum utility is derived using a weighted utility maximization technique, and a system reward assignment for task connectivity or sensitivity is performed. A scheduler is in charge of task assignment, whereas an executor is responsible for task offloading on edge devices. Experimental comparisons of the proposed approach with conventional offloading methods indicate better performance in terms of optimizing resource utilization and minimizing task latency.

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Section: A Related Workmentioning

confidence: 99%

A smart collaborative framework for dynamic multi-task offloading in IIoT-MEC networks

Zhang

et al. 2023

Peer-to-Peer Netw. Appl.

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“…StkH-2, the semiconductor industry, is the enduser of StkH-1. Therefore, an interface to use the developed algorithms needs to be provided (4). At StkH-2, this then also needs an adjustment of existing requirements (1) and related training activities (8) for the end-users.…”

Section: The Engineering Process Model For ML Applications In the Sem...mentioning

confidence: 99%

“…The main areas of research that have gained increased attention in recent years are reliability [3], performance [4] and cyber security [5]. Systems using IoT frameworks produce a lot of data [6] due to the presence of many sensors and actuators, for instance, enabling predictive maintenance with minor manual interaction [7].…”

Section: Introductionmentioning

confidence: 99%

Improving wafer map classification in Industry 4.0

Scheiber¹,

Nartey²,

Zernig³

et al. 2022

2022 IEEE 5th International Conference on Industrial Cyber-Physical Systems (ICPS)

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At the heart of Industry 4.0. lies the automation of manufacturing processes and interoperability of corresponding applications, in most cases utilizing Cyber-Physical Systems and Internet of Things. Used within diverse industrial areas across the world, these concepts gained more importance in recent years. In the semiconductor industry, continuous improvement of all involved processes is achieved by utilizing these concepts in many different stages of the manufacturing process. This work proposes a machine learning framework that on one hand enhances wafer map classification in the testing stage of semiconductor devices, and on the other hand fulfills the requirements and demands of all involved stakeholders in adopted engineering processes. The core of the proposed system is the Wafer Health Factor, a machine learning framework that detects process deviations in analog wafer test data through pattern recognition. Additional integration of Radon-based features, as well as the use of an ensemble classification framework, boosts model performances in the presented real-world application scenario significantly. Moreover, we also show that the proposed framework performs well on two benchmark data sets from literature, even on small training data sets. We conclude, that the presented framework improves the performance of pattern recognition tasks in realworld applications and thus, enables the automatic and early detection of deviations within semiconductor manufacturing processes.

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“…Finally, the professionals have explained the complexity attained in reliable communication of the 6G network. Further, knowledge in the communication area is improved effectively with machine learning networks and cross-layer optimization techniques (Afrin et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Adaptive resource allocation for time-critical applications in industrial edge computing using Heuristic-HCMDO approach

Udayakumar

Ramamoorthy

2023

Preprint

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An intensive computation source has become increasingly important in recent years to meet the time-critical and low-latency needs of Industrial Internet of Things (IIoT) systems. Existing IIoT-based devices are built with limited computational resources, delivering results in a limited fashion when used in highly resource-intensive applications. Since then, a novel concept known as Edge Computing (EC) has been introduced to reduce network latency and alleviate strain on cloud data centers using an EC server located at the network's periphery. The EC server only managed to gather a small number of resources compared to the resource cloud. Without prior context about task deadline and load, an EC server could not optimally handle latency-sensitive and computation-intensive tasks. Additionally, the EC server did not significantly improve overhead minimization when sending data to and from the remote cloud and the user's device. Parallel to the development of EC, nonorthogonal multiple access (NOMA) has been identified as a technique with the potential to substantially increase spectrum efficiency. In this paper, a NOMA-based EC framework for IIoT system is examined, in which multiple task nodes transfer their task via NOMA to multiple edge servers in proximity for execution. As such, this paper aims to develop a joint optimization model for making decisions about task offloading and allocating resources in Industrial edge computing. An adaptive resource allocation decision model (ARADM) based on deep reinforcement learning (DRL) and heuristically modified long short-term memory (H-LSTM) using hybrid Cat and Mouse Dingo Optimization (HCMDO) is proposed to allocate the task optimally. We formulate joint optimization by considering multi-constraint objective function with communication, computation, and cache parameters using HCMDO. Further, these optimal parameters are used in training an H-LSTM along with benchmark dataset. The outcome of the H-LSTM network utilized in DRL to improve convergence speed, accuracy and stability by predicting optimal cost and load. The goal is to minimize service delay, energy consumption, balance load and maximize resource utilization. The experimental results validated the developed model and its ability to improve the quality of resource allocation in Industrial edge computing.

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Robotic Edge Resource Allocation for Agricultural Cyber-Physical System

Cited by 12 publications

References 32 publications

A smart collaborative framework for dynamic multi-task offloading in IIoT-MEC networks

A smart collaborative framework for dynamic multi-task offloading in IIoT-MEC networks

Improving wafer map classification in Industry 4.0

Adaptive resource allocation for time-critical applications in industrial edge computing using Heuristic-HCMDO approach

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