mTrust: Call Behavioral Trust Predictive Analytics Using Unsupervised Learning in Mobile Cloud Computing

Bhowmik, Arka; De, Debashis

doi:10.1007/s11277-020-07879-x

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…Then comprehensively consider the application task's QoS target constraints and user expectations and based on a membership function, apply the method described to convert the application task's multiple QoS target constraints into a single-objective constraint optimization problem. Finally, the reconstructed genetic algorithm is applied to approximate the optimal solution of the mentioned single-objective optimization problem [ 21 ]. The calculated result is the final scheduling decision plan, which is returned to the scheduler.…”

Section: Linear Equations Solution Methods and Task Scheduling Methodsmentioning

confidence: 99%

Decision Scheduling for Cloud Computing Tasks Relying on Solving Large Linear Systems of Equations

2022

Computational Intelligence and Neuroscience

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With the continuous reform and innovation of Internet technology and the continuous development and progress of social economy, Big Data cloud computing technology is more and more widely used in people’s work and life. Many parallel algorithms play a very important role in solving large linear equations in various applications. To this end, this article aims to propose and summarize a cloud computing task scheduling model that relies on the solution of large linear equations. The method of this paper is to study the technology of solving large-scale linear equations and propose an M-QoS-OCCSM scheduling model. The function of the experimental method is to solve the problem of efficiently executing N mutually dependent parallel tasks within limited resources, while fully satisfying users’ expectations of task completion time, bandwidth rate, reliability, and cost. In this paper, the application experiment of large-scale linear equations in task scheduling is used to study task scheduling algorithms. The results show that when the task load is 10 and 20, the convergence speed of the MPQGA algorithm is 32 seconds and 95 seconds faster than that of the BGA algorithm, respectively.

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Section: Linear Equations Solution Methods and Task Scheduling Methodsmentioning

confidence: 99%

Decision Scheduling for Cloud Computing Tasks Relying on Solving Large Linear Systems of Equations

2022

Computational Intelligence and Neuroscience

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“…Simulations demonstrate that the proposed approach can achieve better channel utilization and transmit more data packets compared to other conventional approaches. In [145], an unsupervised learning approach is developed to filter spam voice calls by assigning trust scores among users. In particular, the approach extracts data from call logs to determine trust scores using an MLP model.…”

Section: Other Security Issuesmentioning

confidence: 99%

“…4) Transfer a General Model: In many aforementioned TL approaches, a baseline model trained with a large amount of labeled data is transferred to the target task for fine-tuning. For example, the TL approaches in [168] and [145] propose to transfer a model trained with a large amount of generalized data. The obtained results show that this TL strategy helps to improve the performance of the task models to various extents.…”

Section: Challenges Open Issues and Future Research Directionsmentioning

confidence: 99%

Transfer Learning for Future Wireless Networks: A Comprehensive Survey

Nguyen,

Van Huynh,

Chu

et al. 2021

Preprint

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With outstanding features, Machine Learning (ML) has been the backbone of numerous applications in wireless networks. However, the conventional ML approaches have been facing many challenges in practical implementation, such as the lack of labeled data, the constantly changing wireless environments, the long training process, and the limited capacity of wireless devices. These challenges, if not addressed, will impede the effectiveness and applicability of ML in future wireless networks. To address these problems, Transfer Learning (TL) has recently emerged to be a very promising solution. The core idea of TL is to leverage and synthesize distilled knowledge from similar tasks as well as from valuable experiences accumulated from the past to facilitate the learning of new problems. Doing so, TL techniques can reduce the dependence on labeled data, improve the learning speed, and enhance the ML methods' robustness to different wireless environments. This article aims to provide a comprehensive survey on applications of TL in wireless networks. Particularly, we first provide an overview of TL including formal definitions, classification, and various types of TL techniques. We then discuss diverse TL approaches proposed to address emerging issues in wireless networks. The issues include spectrum management, localization, signal recognition, security, human activity recognition and caching, which are all important to nextgeneration networks such as 5G and beyond. Finally, we highlight important challenges, open issues, and future research directions of TL in future wireless networks.

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“…The other node with which the trustor communicates is called the trustee. Digital trust evaluates past behavior or evidence of the behavior of a device concerning its self-defined level of trustworthiness, which helps perceive its upcoming activities [ 17 ]. It is a presupposition to enhance the decision-making for successful cooperation between two agents.…”

Section: Introductionmentioning

confidence: 99%

Multilevel Central Trust Management Approach for Task Scheduling on IoT-Based Mobile Cloud Computing

Ali

Iqbal

Jamil

et al. 2021

Sensors

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With the increasing number of mobile devices and IoT devices across a wide range of real-life applications, our mobile cloud computing devices will not cope with this growing number of audiences soon, which implies and demands the need to shift to fog computing. Task scheduling is one of the most demanding scopes after the trust computation inside the trustable nodes. The mobile devices and IoT devices transfer the resource-intensive tasks towards mobile cloud computing. Some tasks are resource-intensive and not trustable to allocate to the mobile cloud computing resources. This consequently gives rise to trust evaluation and data sync-up of devices joining and leaving the network. The resources are more intensive for cloud computing and mobile cloud computing. Time, energy, and resources are wasted due to the nontrustable nodes. This research article proposes a multilevel trust enhancement approach for efficient task scheduling in mobile cloud environments. We first calculate the trustable tasks needed to offload towards the mobile cloud computing. Then, an efficient and dynamic scheduler is added to enhance the task scheduling after trust computation using social and environmental trust computation techniques. To improve the time and energy efficiency of IoT and mobile devices using the proposed technique, the energy computation and time request computation are compared with the existing methods from literature, which identified improvements in the results. Our proposed approach is centralized to tackle constant SyncUPs of incoming devices’ trust values with mobile cloud computing. With the benefits of mobile cloud computing, the centralized data distribution method is a positive approach.

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mTrust: Call Behavioral Trust Predictive Analytics Using Unsupervised Learning in Mobile Cloud Computing

Cited by 7 publications

References 14 publications

Decision Scheduling for Cloud Computing Tasks Relying on Solving Large Linear Systems of Equations

Decision Scheduling for Cloud Computing Tasks Relying on Solving Large Linear Systems of Equations

Transfer Learning for Future Wireless Networks: A Comprehensive Survey

Multilevel Central Trust Management Approach for Task Scheduling on IoT-Based Mobile Cloud Computing

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