ISOF: Information Scheduling and Optimization Framework for Improving the Performance of Agriculture Systems Aided by Industry 4.0

Manogaran, Gunasekaran; Hsu, Ching‐Hsien; Rawal, Bharat S.; Muthu, BalaAnand; Mavromoustakis, Constandinos X.; Mastorakis, George

doi:10.1109/jiot.2020.3045479

Cited by 52 publications

(21 citation statements)

References 25 publications

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“…Logistics optimization needs to focus on logistics information in addition to the integration of logistics storage and transportation data. Common features also exist between the two, and common features between logistics data need to be shared and screened in an adaptive manner [ 9 ]. For the management of storage and transportation data, it is necessary to start from the supply chain side, with decision making and supporting operations as key aspects to optimize the management application details [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Logistics Optimization Strategy Based on Deep Neural Framework

Liu

Zhu

2022

Computational Intelligence and Neuroscience

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We propose a logistics optimization method based on improved graph convolutional networks to address the current problem of low product delivery rate and untimely product delivery during the peak period of e-commerce activities. Our method can learn excellent planning strategies from previous data and can give the best logistics strategy in time during the peak logistics period, which improves the product delivery rate and delivery time of logistics and greatly enhances the return on investment. First, we add a tensor rotation module to the graph convolution layer to better capture the global features of logistics nodes. Then we add inception structures in the temporal convolution layer to build multiscale temporal convolution filters to obtain temporal information of logistics nodes in different time-aware domains and reduce arithmetic power. Finally, we cooperate with e-commerce platforms to adopt logistics data as the experimental database. The experimental results show that our method greatly accelerates the logistics planning speed, improves the product delivery rate, ensures the timely delivery of products, and increases the return on investment.

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Section: Introductionmentioning

confidence: 99%

Logistics Optimization Strategy Based on Deep Neural Framework

Liu

Zhu

2022

Computational Intelligence and Neuroscience

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“…The use of IoT sensors’ measurements as a novelty is critical to the factory’s performance. Mining, energy, utilities, agriculture, oil & gas, infrastructure, emergency response and life sciences all require intelligent solutions that integrate hardware with automation software to give accurate results at scale (Bigliardi et al , 2020; Manogaran et al , 2020; Javaid and Haleem, 2020).…”

Section: Technological Advances Of Drones For Industry 40mentioning

confidence: 99%

Exploring contributions of drones towards Industry 4.0

Javaid

Khan

Singh

et al. 2021

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Purpose Unmanned aerial vehicles are commonly known as UAVs and drones. Nowadays, industries have begun to realise the operational and economic benefits of drone-enabled tasks. The Internet of Things (IoT), Big Data, drones, etc., represent implementable advanced technologies intended to accomplish Industry 4.0. The purpose of this study is to discuss the significant contributions of drones for Industry 4.0. Design/methodology/approach Nowadays, drones are used for inspections, mapping and surveying in difficult or hazardous locations. For writing this paper, relevant research papers on drone for Industry 4.0 are identified from various research platforms such as Scopus, Google Scholar, ResearchGate and ScienceDirect. Given the enormous extent of the topic, this work analyses many papers, reports and news stories in an attempt to comprehend and clarify Industry 4.0. Findings Drones are being implemented in manufacturing, entertainment industries (cinematography, etc.) and machinery across the world. Thermal-imaging devices attached to drones can detect variable heat levels emanating from a facility, trigger the sprinkler system and inform emergency authorities. Due partly to their utility and adaptability in industrial areas such as energy, transportation, engineering and more, autonomous drones significantly impact Industry 4.0. This paper discusses drones and their types. Several technological advances and primary extents of drones for Industry 4.0 are diagrammatically elaborated. Further, the authors identified and discussed 19 major applications of drones for Industry 4.0. Originality/value This paper’s originality lies in its discussion and exploration of the capabilities of drones for Industry 4.0, especially in manufacturing organisations. In addition to improving efficiency and site productivity, drones can easily undertake routine inspections and check streamlines operations and maintenance procedures. This work contributes to creating a common foundation for comprehending Industry 4.0 outcomes from many disciplinary viewpoints, allowing for more research and development for industrial innovation and technological progress.

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“…The implanted connected IoT sensors across different agricultural provide take the responsibility of collaborative sensing and monitoring different aspects (i.e., weather states, humidity degree, temperature degree, and moisture degree, etc.) of the agriculture environment [50]. The accumulated data is subsequently analyzed to gain insights critical for the development of smart agricultural applications and services including automated irrigation, examining the quality of water, monitoring soil ingredients, infection, and insects, and detecting crop diseases [51].…”

Section: Smart Agriculture Applicationsmentioning

confidence: 99%

Internet of Things, Preliminaries and Foundations

Abdel‐Basset

Moustafa

Hawash

et al. 2021

Deep Learning Techniques for IoT Security and Privacy

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This chapter mainly presents a detailed discussion of the IoT technologies and dependent systems with the main objectives of emphasizing the main attributes of IoT systems that might possibly threaten the security of the system. Firstly, the definition and of the IoT system and the detailed description of its architecture are presented along with a taxonomy for dividing its architecture into layers with different complementary roles. Secondly, the concepts of cloud computing, fog computing, and edge computing are discussed and compared in view of IoT systems. Finally, the learned lessons are summarized and pointed out in the last section of this chapter.

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ISOF: Information Scheduling and Optimization Framework for Improving the Performance of Agriculture Systems Aided by Industry 4.0

Cited by 52 publications

References 25 publications

Logistics Optimization Strategy Based on Deep Neural Framework

Logistics Optimization Strategy Based on Deep Neural Framework

Exploring contributions of drones towards Industry 4.0

Internet of Things, Preliminaries and Foundations

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