Delay-aware concurrent data management method for IoT collaborative mobile edge computing environment

Kavitha, B. C.; Vallikannu, R.; Sankaran, Kris

doi:10.1016/j.micpro.2020.103021

Cited by 28 publications

(10 citation statements)

References 24 publications

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“…e increasing popularity of IoT applications demands the computing power of IoTsystems, and edge computing is one of the main methods to enhance the computing speed of IoT applications [32]. In future research, we will try to combine this recognition system with edge computing as well as [33][34][35][36], in which authors used edge computing in combination with IoT devices to increase the computational speed of the system and thus reduce its response time.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

An IoT-Based Motion Tracking System for Next-Generation Foot-Related Sports Training and Talent Selection

Zhang

Wang

et al. 2021

Journal of Healthcare Engineering

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Motion tracking in different fields (medical, military, film, etc.) based on microelectromechanical systems (MEMS) sensing technology has been attracted by world's leading researchers and engineers in recent years; however, there is still a lack of research covering the sports field. In this study, we propose a new AIoT (AI + IoT) paradigm for next-generation foot-driven sports (soccer, football, takraw, etc.) training and talent selection. The system built is cost-effective and easy-to-use and requires much fewer computational resources than traditional video-based analysis on monitoring motions of players during training. The system built includes a customized wireless wearable sensing device (WWSDs), a mobile application, and a data processing interface-based cloud with an ankle attitude angle analysis model. Eleven right-foot male participators wore the WWSD on their ankle while each performed 20 instances of different actions in a formal soccer field. The experimental outcome demonstrates the proposed motion tracking system based on AIoT and MEMS sensing technologies capable of recognizing different motions and assessing the players’ skills. The talent selection function can partition the elite and amateur players at an accuracy of 93%. This intelligent system can be an emerging technology based on wearable sensors and attain the experience-driven to data-driven transition in the field of sports training and talent selection and can be easily extended to analyze other foot-related sports motions (e.g., taekwondo, tumble, and gymnastics) and skill levels.

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Section: Conclusion and Discussionmentioning

confidence: 99%

An IoT-Based Motion Tracking System for Next-Generation Foot-Related Sports Training and Talent Selection

Zhang

Wang

et al. 2021

Journal of Healthcare Engineering

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“…Further, the privacy concerns involved in edge computing was discussed along with the future research directions. Kavitha et al [21] presented a scheme for data management based on concurrent data. This scheme was designed for the mobile environment.…”

Section: Related Workmentioning

confidence: 99%

Optimized Energy Efficient Strategy for Data Reduction Between Edge Devices in Cloud-IoT

Mukherjee¹,

Ghosh²,

Pal³

et al. 2022

Computers, Materials &Amp; Continua

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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.

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“…Zhang [18] considered application data processing in both local execution and cloud execution modes, and optimized the CPU clock frequency and data transmission with the goal of minimizing power consumption, and finally determined that the application adopts the energyefficient application execution mode in processing data. In addition to optimizing the energy consumption of mobile application execution, many studies also pay attention to the time constraints of mobile application execution [19], e.g., the constraints of application deadline [20]. Muñoz [21] studied the mobile application offload problem with delay constraints and came up with solutions to optimize utilization of communication resources and computing resources.…”

Section: Related Work and Motivationmentioning

confidence: 99%

“…Then, since SD, SR, RD, SN R are random variables, and λ t changes over time, we propose the optimization model for V2V transmission scheduling based on (5) and ( 9). The goal is to maximize the estimated success probability of computation offloading, with the derivation process and final expression shown in (19).…”

Section: A Optimization Algorithm For Joint Reliabilitymentioning

confidence: 99%

Reliability-Aware Joint Optimization for Cooperative Vehicular Communication and Computing

Han

Sheng

Duan

et al. 2021

IEEE Trans. Intell. Transport. Syst.

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This paper comprehensively discusses the cooperative communication and computation of vehicular system. Based on the cooperative transmission, an stochastic model of vehicleto-vehicle (V2V) communication reliability is established using probability theory. Furthermore, the computation reliability is defined as a new metric for computation offloading, and a vehicle computational performance evaluation model is also established. In order to effectively compute the required data, we combine V2V communication and vehicle computing to further characterize the coupling reliability of cooperative communications and computation systems. In addition, we propose a virtual queue model that combines queue length and vehicle privacy entropy to optimize partitioning. Finally, considering the amount of processing data and cut-off time of vehicle applications, we establish the optimal partition model of vehicle computing with the goal of maximizing the coupling reliability, and propose the coupling-oriented reliability calculation for vehicle collaboration using dynamic programming methods. Simulations show that the proposed scheme outperforms traditional approaches in terms of coupling reliability and completion rate. In addition, the allocation between local computing and data offloading is controlled by the server's privacy perception of collaboration events.Index Terms-Cooperative vehicle infrastructure system (CVIS), vehicular communication, mobile edge computing (MEC), partial offloading, dynamic programming.

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Delay-aware concurrent data management method for IoT collaborative mobile edge computing environment

Cited by 28 publications

References 24 publications

An IoT-Based Motion Tracking System for Next-Generation Foot-Related Sports Training and Talent Selection

An IoT-Based Motion Tracking System for Next-Generation Foot-Related Sports Training and Talent Selection

Optimized Energy Efficient Strategy for Data Reduction Between Edge Devices in Cloud-IoT

Reliability-Aware Joint Optimization for Cooperative Vehicular Communication and Computing

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