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| 1 | Information-Centric IoT-Based Smart Farming with Dynamic Data Optimization显示文摘Smart farming has become a strategic approach of sustainable agriculture management and monitoring with the infrastructure to exploit modern technologies,including big data,the cloud,and the Internet of Things(IoT).Many researchers try to integrate IoT-based smart farming on cloud platforms effectively.They define various frameworks on smart farming and monitoring system and still lacks to define effective data management schemes.Since IoT-cloud systems involve massive structured and unstructured data,data optimization comes into the picture.Hence,this research designs an Information-Centric IoT-based Smart Farming with Dynamic Data Optimization(ICISF-DDO),which enhances the performance of the smart farming infrastructure with minimal energy consumption and improved lifetime.Here,a conceptual framework of the proposed scheme and statistical design model has beenwell defined.The information storage and management with DDO has been expanded individually to show the effective use of membership parameters in data optimization.The simulation outcomes state that the proposed ICISF-DDO can surpass existing smart farming systems with a data optimization ratio of 97.71%,reliability ratio of 98.63%,a coverage ratio of 99.67%,least sensor error rate of 8.96%,and efficient energy consumption ratio of 4.84%. | Souvik Pal Hannah VijayKumar D.Akila N.Z.Jhanjhi Omar A.Darwish Fathi Amsaad | 2023 | Computers, Materials & Continua2023，，2： | 0 |
| 2 | Optimized Energy Efficient Strategy for Data Reduction Between Edge Devices in Cloud-IoT显示文摘Numerous Internet of Things(IoT)systems produce massive volumes of information that must be handled and answered in a quite short period.The growing energy usage related to the migration of data into the cloud is one of the biggest problems.Edge computation helps users unload the workload again from cloud near the source of the information that must be handled to save time,increase security,and reduce the congestion of networks.Therefore,in this paper,Optimized Energy Efficient Strategy(OEES)has been proposed for extracting,distributing,evaluating the data on the edge devices.In the initial stage of OEES,before the transmission state,the data gathered from edge devices are supported by a fast error like reduction that is regarded as the largest energy user of an IoT system.The initial stage is followed by the reconstructing and the processing state.The processed data is transmitted to the nodes through controlled deep learning techniques.The entire stage of data collection,transmission and data reduction between edge devices uses less energy.The experimental results indicate that the volume of data transferred decreases and does not impact the professional data performance and predictive accuracy.Energy consumption of 7.38 KJ and energy conservation of 55.57 kJ was found in the proposed OEES scheme.Predictive accuracy is 97.5 percent,data performance rate was 97.65 percent,and execution time is 14.49 ms. | Dibyendu Mukherjee Shivnath Ghosh Souvik Pal D.Akila N.Z.Jhanjhi Mehedi Masud Mohammed A.AlZain | 2022 | Computers, Materials & Continua2022，，7： | 0 |
| 3 | A Novel Machine Learning-Based Hand Gesture Recognition Using HCI on IoT Assisted Cloud Platform显示文摘Machine learning is a technique for analyzing data that aids the construction of mathematical models.Because of the growth of the Internet of Things(IoT)and wearable sensor devices,gesture interfaces are becoming a more natural and expedient human-machine interaction method.This type of artificial intelligence that requires minimal or no direct human intervention in decision-making is predicated on the ability of intelligent systems to self-train and detect patterns.The rise of touch-free applications and the number of deaf people have increased the significance of hand gesture recognition.Potential applications of hand gesture recognition research span from online gaming to surgical robotics.The location of the hands,the alignment of the fingers,and the hand-to-body posture are the fundamental components of hierarchical emotions in gestures.Linguistic gestures may be difficult to distinguish from nonsensical motions in the field of gesture recognition.Linguistic gestures may be difficult to distinguish from nonsensical motions in the field of gesture recognition.In this scenario,it may be difficult to overcome segmentation uncertainty caused by accidental hand motions or trembling.When a user performs the same dynamic gesture,the hand shapes and speeds of each user,as well as those often generated by the same user,vary.A machine-learning-based Gesture Recognition Framework(ML-GRF)for recognizing the beginning and end of a gesture sequence in a continuous stream of data is suggested to solve the problem of distinguishing between meaningful dynamic gestures and scattered generation.We have recommended using a similarity matching-based gesture classification approach to reduce the overall computing cost associated with identifying actions,and we have shown how an efficient feature extraction method can be used to reduce the thousands of single gesture information to four binary digit gesture codes.The findings from the simulation support the accuracy,precision,gesture recognition,sensitivity,and efficiency rates.The Machine Learning-based Gesture Recognition Framework(ML-GRF)had an accuracy rate of 98.97%,a precision rate of 97.65%,a gesture recognition rate of 98.04%,a sensitivity rate of 96.99%,and an efficiency rate of 95.12%. | Saurabh Adhikari Tushar Kanti Gangopadhayay Souvik Pal D.Akila Mamoona Humayun Majed Alfayad N.Z.Jhanjhi | 2023 | Computer Systems Science & Engineering2023，46，8： | 0 |
| 4 | Dynamic Data Optimization in IoT-Assisted Sensor Networks on Cloud Platform显示文摘This article presents a new scheme for dynamic data optimization in IoT(Internet of Things)-assisted sensor networks.The various components of IoT assisted cloud platform are discussed.In addition,a new architecture for IoT assisted sensor networks is presented.Further,a model for data optimization in IoT assisted sensor networks is proposed.A novel Membership inducing Dynamic Data Optimization Membership inducing Dynamic Data Optimization(MIDDO)algorithm for IoT assisted sensor network is proposed in this research.The proposed algorithm considers every node data and utilized membership function for the optimized data allocation.The proposed framework is compared with two stage optimization,dynamic stochastic optimization and sparsity inducing optimization and evaluated in terms of reliability ratio,coverage ratio and sensing error.Data optimization was performed based on the availability of cloud resource,sensor energy,data flow volume and the centroid of each state.It was inferred that the proposed MIDDO algorithm achieves an average performance ratio of 76.55%,reliability ratio of 94.74%,coverage ratio of 85.75%and sensing error of 0.154. | Nguyen A.Tuan D.Akila Souvik Pal Bikramjit Sarkar Thien Khai Tran G.Mothilal Nehru Dac-Nhuong Le | 2022 | Computers, Materials & Continua2022，，7： | 0 |
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