A new service-oriented grid-based method for AIoT application and implementation

Zou, Yiqin; Quan, Li

doi:10.1142/s0217984917400644

Cited by 11 publications

(7 citation statements)

References 9 publications

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“…The IoT grid was introduced to integrate the grid services with IoT architecture [ 10 ]. The IoT grid benefits from deploying grid service architecture to IoT technology [ 10 , 12 ].…”

Section: Related Workmentioning

confidence: 99%

“…The IoT comprises things such as sensors, computing devices, embedded systems, objects, and animals with unique identifiers and that have the capacity to transmit data over a specified network [ 4 , 5 , 7 , 8 , 9 ]. The IoT grid was introduced to integrate the grid services with IoT architecture [ 10 , 11 ]. The IoT grid benefits from deploying grid service architecture for IoT technology [ 10 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

“…The IoT grid was introduced to integrate the grid services with IoT architecture [ 10 , 11 ]. The IoT grid benefits from deploying grid service architecture for IoT technology [ 10 , 12 ]. The grid sites are clusters, super computers, or multiprocessor computing networks [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

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Greedy Firefly Algorithm for Optimizing Job Scheduling in IoT Grid Computing

Yousif

Alqhtani

Bashir

et al. 2022

Sensors

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The Internet of Things (IoT) is defined as interconnected digital and mechanical devices with intelligent and interactive data transmission features over a defined network. The ability of the IoT to collect, analyze and mine data into information and knowledge motivates the integration of IoT with grid and cloud computing. New job scheduling techniques are crucial for the effective integration and management of IoT with grid computing as they provide optimal computational solutions. The computational grid is a modern technology that enables distributed computing to take advantage of a organization’s resources in order to handle complex computational problems. However, the scheduling process is considered an NP-hard problem due to the heterogeneity of resources and management systems in the IoT grid. This paper proposed a Greedy Firefly Algorithm (GFA) for jobs scheduling in the grid environment. In the proposed greedy firefly algorithm, a greedy method is utilized as a local search mechanism to enhance the rate of convergence and efficiency of schedules produced by the standard firefly algorithm. Several experiments were conducted using the GridSim toolkit to evaluate the proposed greedy firefly algorithm’s performance. The study measured several sizes of real grid computing workload traces, starting with lightweight traces with only 500 jobs, then typical with 3000 to 7000 jobs, and finally heavy load containing 8000 to 10,000 jobs. The experiment results revealed that the greedy firefly algorithm could insignificantly reduce the makespan makespan and execution times of the IoT grid scheduling process as compared to other evaluated scheduling methods. Furthermore, the proposed greedy firefly algorithm converges on large search spacefaster , making it suitable for large-scale IoT grid environments.

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“…The IoT grid was introduced to integrate the grid services with IoT architecture [ 10 ]. The IoT grid benefits from deploying grid service architecture to IoT technology [ 10 , 12 ].…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Greedy Firefly Algorithm for Optimizing Job Scheduling in IoT Grid Computing

Yousif

Alqhtani

Bashir

et al. 2022

Sensors

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“…The climate prediction has high reference value. Small climate stations in agricultural areas monitoring and Sensors 2020, 20 predicting climate changes in real-time can help farmers quickly adjust a planting plan to minimize losses. For the prevention of, and resistance to, risks in the agricultural industry, the support of climate prediction technology is indispensable.…”

Section: Introductionmentioning

confidence: 99%

“…Hao et al [19] studied the relationship between farmland environmental factors and crop growth cycle and crop yield based on agricultural big data. Zou et al [20] proposed a new service-based, grid-based approach to building agricultural IoT systems. Agricultural material information can be used in agricultural engineering and has obvious advantages.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Deep Learning Predictor for Smart Agriculture Sensing Based on Empirical Mode Decomposition and Gated Recurrent Unit Group Model

Jin

Yang

Wang

et al. 2020

Sensors

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Smart agricultural sensing has enabled great advantages in practical applications recently, making it one of the most important and valuable systems. For outdoor plantation farms, the prediction of climate data, such as temperature, wind speed, and humidity, enables the planning and control of agricultural production to improve the yield and quality of crops. However, it is not easy to accurately predict climate trends because the sensing data are complex, nonlinear, and contain multiple components. This study proposes a hybrid deep learning predictor, in which an empirical mode decomposition (EMD) method is used to decompose the climate data into fixed component groups with different frequency characteristics, then a gated recurrent unit (GRU) network is trained for each group as the sub-predictor, and finally the results from the GRU are added to obtain the prediction result. Experiments based on climate data from an agricultural Internet of Things (IoT) system verify the development of the proposed model. The prediction results show that the proposed predictor can obtain more accurate predictions of temperature, wind speed, and humidity data to meet the needs of precision agricultural production.

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