Development an Intelligent Task Offloading System for Edge-Cloud Computing Paradigm

Atanov, Sabyrzhan; Seitbattalov, Zhexen Y.; Moldabayeva, Zhanar S.

doi:10.1109/icecco53203.2021.9663797

Cited by 6 publications

(4 citation statements)

References 17 publications

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“…In order to overcome these disadvantages, some researches used edge computing [19,20]. The experimental results of the study using edge computing significantly optimised the wireless network bandwidth and improved scalability without compromising the accuracy of the results and latency [21,22].…”

Section: Related Workmentioning

confidence: 99%

Digital model of automated unmanned aerial vehicle with edge computing application for railway reconnaissance

Tulembayeva,

Ashirov,

Makarov

et al. 2024

Acta Polytech

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In the last decade, unmanned aerial vehicles have been widely used in various civil and military field operations. Most modern drones are manually controlled by an operator and require decision making, which requires experience gained through training, which is time consuming and expensive, and even with experienced operators, the human factor can never be excluded. In this paper, an algorithm for an automatic flight mode of unmanned aerial vehicles without an operator’s participation has been developed. The proposed algorithm has been considered in the military reconnaissance of railways using edge computing and computer vision approaches to recognise railroads and moving trains for simulated and real cases as well as send report data to the web platform. As a result, the head of the operation received information about the number of carriages and Global Positioning System coordinates of the recognised train to make the necessary decisions.

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Section: Related Workmentioning

confidence: 99%

Digital model of automated unmanned aerial vehicle with edge computing application for railway reconnaissance

Tulembayeva,

Ashirov,

Makarov

et al. 2024

Acta Polytech

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“…To calculate the RoA value, the mechanism is based on the fuzzy logic data controller that collects context factors from every data request, such as data, quality, and network, which is used as a threshold to determine which data requests should be allocated off-chain or to a cloud database for storage. An offloading system for tasks based on fuzzy logic was developed in [105], allowing the administrator to choose an appropriate computer network for the task while avoiding high latency and the energy consumption. To save energy and time, fuzzy logic systems have chosen the best computing system for a variety of real-world scenarios and factors.…”

Section: Figure 8 Fuzzy Load Balancing Classificationmentioning

confidence: 99%

Fuzzy Theory in Fog Computing: Review, Taxonomy, and Open Issues

et al. 2022

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Geographically dispersed Fog Computing architecture ubiquitously connected to a range of heterogeneous nodes at the edge of the network can provide cooperative flexible, and variable computations, communications, and storage services. Several fog computing methods, models, and techniques have been used to solve cloud issues. The fuzzy theory has also been used in many aspects of fog computing. Objectives: This work presents a systematic literature review of the use of fuzzy theory in Fog Computing, highlighting the main practical motivations, classification types in research approaches, fuzzy methods used, popular evaluation tools, open issues, and future trends. Methods: The investigations were systematically performed using fuzzy theory in fog computing, and four databases which are ScienceDirect, Web of Science (WoS), Scopus, and IEEE Xplore Digital Library from 2015 to 2022, were used to analyse their performance evaluation, architecture, and applications. Results: 94 articles were selected based on fuzzy theory in fog computing using different methods, models, and techniques, based on the proposed exclusion and inclusion criteria. The results of the taxonomy were divided into five major classes: task and resource management, intrusion detection systems, trust management, and healthcare services. Discussion: Applications requiring real-time, low latency, and quick responses are well suited for fog computing. These studies show that resource sharing improves the fog computing architecture by delivering reduced latency, distributed processing, improved scalability, better security, fault tolerance, and privacy. Conclusion: The majority of the time, research areas on fuzzy theory in fog computing are crucially significant. We conclude that this review will enhance research capacity, thereby expanding and creating new research domains.

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“…A single-board computer, Raspberry Pi, has been selected as an edge computing device, or it is briefly called an edge device [7,8]. Our previous studies have shown the excellent results in the application of computer vision and edge computing to number plate recognition system on the Raspberry Pi [9], as well as in the development of an intelligent task offload system [10]. Thus, the Raspberry Pi would be able to complete the task of weapon recognition based on the EfficientDet model and significantly reduce the cost of the technical solution.…”

Section: Introductionmentioning

confidence: 99%

Weapons Detection System Based on Edge Computing and Computer Vision

Burnayev¹,

Toibazarov²,

Atanov³

et al. 2023

IJACSA

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Early detection of armed threats is crucial in reducing accidents and deaths resulting from armed conflicts and terrorist attacks. The most significant application of weapon detection systems would be found in public areas such as airports, stadiums, central squares, and on the battlefield in urban or rural conditions. Modern surveillance and control systems of closed-circuit television cameras apply deep learning and machine learning algorithms for weapons detection on the base of cloud architecture. However, cloud computing is inefficient for network bandwidth, data privacy and slow decision-making. To address these issues, edge computing can be applied, using Raspberry Pi as an edge device with the EfficientDet model for developing the weapons detection system. The image processing results are transmitted as a text report to the cloud platform for further analysis by the operator. Soldiers can equip themselves with the suggested edge node and headphones for armed threat notifications, plugged into augmented reality glasses for visual data output. As a result, the application of edge computing makes it possible to ensure data safety, increase the network bandwidth and provide the device operation without the internet. Thus, an independent weapon detection system was developed that identifies weapons in 1.48 seconds without the Internet.

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Development an Intelligent Task Offloading System for Edge-Cloud Computing Paradigm

Cited by 6 publications

References 17 publications

Digital model of automated unmanned aerial vehicle with edge computing application for railway reconnaissance

Digital model of automated unmanned aerial vehicle with edge computing application for railway reconnaissance

Fuzzy Theory in Fog Computing: Review, Taxonomy, and Open Issues

Weapons Detection System Based on Edge Computing and Computer Vision

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