QoS enhancement with deep learning-based interference prediction in mobile IoT

Zafar, Saniya; Jangsher, Sobia; Bouachir, Ouns; Aloqaily, Moayad; Othman, Jalel Ben

doi:10.1016/j.comcom.2019.09.010

Cited by 48 publications

(24 citation statements)

References 22 publications

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“…These results thus showed that participants who were able to utilize the teaching strategies could enhance learning outcomes and reduce cognitive load. Such findings are consistent with other cooperative learning [14,15,18,28], and mobile learning studies [34].…”

Section: The Cognitive Load Of Using a Cooperative Learningsupporting

confidence: 91%

“…Based on the concept of computer-assisted learning, e-learning has developed the models of mobile and ubiquitous learning [13][14][15]. Mobile learning not only has the characteristics of digital information but also provides the mobility of learning [16][17][18]. Moreover, the mobile learning environment is more convenient than that of e-learning because students can learn at any time and in any location.…”

Section: Mobile Learningmentioning

confidence: 99%

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Cooperative Mobile Learning for the Investigation of Natural Science Courses in Elementary Schools

et al. 2020

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The rapid development of technologies such as tablet PCs and 4G/5G networks has further enhanced the benefits of mobile learning. Although mobile devices are convenient and provide a variety of learning benefits, they are unable to improve students’ learning outcomes without an appropriate learning strategy. Furthermore, little research has been conducted to examine the effects of using collaborative learning on mobile devices. This study proposed a cooperative learning framework using Google Docs to explore the learning outcomes of students of natural science in an elementary curriculum. The study was of a quasi-experimental design with an experimental group (cooperative learning) and a control group (personal learning). The results show that a cooperative learning approach using Google Docs improved learning outcomes, teaching interest, and understanding of campus plants, and reduced cognitive load. One conclusion of the study is that the collaborative learning approach associated with mobile learning is more effective than personal learning. In addition, this paper also provides brief recommendations to expand on the study’s limitations. Future work should investigate the impact of collaborative learning on different environments for mobile learning.

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Section: The Cognitive Load Of Using a Cooperative Learningsupporting

confidence: 91%

Section: Mobile Learningmentioning

confidence: 99%

Cooperative Mobile Learning for the Investigation of Natural Science Courses in Elementary Schools

et al. 2020

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“…Ahmad et al used the K shortest path to calculate the optimal path planning path to reflect dynamics [26]. Literature [27] proposes the dynamic resource allocation problem with improved Quality-of-Service applicable to buses. Long-Short Term Memory (LSTM) based neural networks are considered to predict city buses locations for interference determination between moving small cells.…”

Section: Figure 2: Paper Organization Frameworkmentioning

confidence: 99%

Dynamic Tasks Scheduling Model of UAV Cluster Based on Flexible Network Architecture

Duan¹,

Wang²,

Wang³

et al. 2020

IEEE Access

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With the rapid growth of application demands and the real-time change of environmental situations, the defects of the UAV task network in adaptability, flexibility, and resilience are becoming more and more prominent. The current network architecture that the junction of points and lines is fixed cannot dynamically provide capacity requirements in real-time due to the failure nodes encountered in the Unmanned Aerial Vehicle (UAV) task scheduling process. To address this challenging issue, this paper proposes a flexible network architecture supporting dynamic fault-tolerant task scheduling model (DSM-FNA) for the UAV cluster. To be specific this paper resorts to super network theory, combining the management theory of flexible network and resilience network to carry out the organizational calculation on the model, and also draw upon linear transformation function to weight and stratify the capability value according to the ability requirement required by the task. Then, a flexible network architecture dynamic scheduling algorithm (FDSA) is proposed, and the substitution strategy is designed for the failure point, to realize the capability and dynamically adapt to the task. Finally, compared with the classical Max-Min algorithm and other algorithms, it is verified that the FDSA algorithm performs better dynamic adjustment for quick response in case of UAV cluster emergencies.

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“…Energy efficient stable matching between cellular user and D2D pair based on mutual preference has been explored in [20]. Resource allocation has been done among multiple D2D pairs in [21] through simultaneous harvesting of energy from power beacon and using same spectrum for information dissemination [22] [23]. EH at the relay with SWIPT to prolong the lifetime of energy-constrained network has been studied in [24] where energy harvested at one node can be relayed to other devices in the affected area through multi-hop.…”

Section: Related Workmentioning

confidence: 99%

“…TSW ratio α for our original LF optimization problem in (15) can be obtained using y in (22) through back substitution with constraint on QoS i.e., R min as…”

Section: B Time Switching Ratio Allocation (Tra) Problemmentioning

confidence: 99%

EPS-TRA: Energy Efficient Peer Selection and Time Switching Ratio Allocation for SWIPT-Enabled D2D Communication

Khan

Jangsher

Aloqaily

et al. 2020

IEEE Trans. Sustain. Comput.

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This paper considers device-to-device (D2D) network with Simultaneous Wireless Information and Power Transfer (SWIPT) enabled devices to ensure selfsustained communication in situations like disasters. Such direct link networks can ensure connectivity with devices having drained backup , when trapped in collapsed infrastructure, through mutual sharing of energy on RF link. To guarantee successful execution of SWIPT session for an isolated device in wake of disasters, it is pertinent to select a reliable peer with ultimate aim to maximize link Energy Efficiency (EE). In practice, Energy Harvesting (EH) is not achievable after Information Decoding (ID), however, it has been made possible through splitting the signal in the time domain. Selection of D2D peer for selfsustained communication with an objective to maximize EE through optimum time based splitting of signal has not been extensively studied. In this paper to manifest the aforesaid goal, we worked out a joint problem of peer association and time switching ratio allocation with an objective to maximize the EE for a device contained under collapsed infrastructure. We propose an Energy efficient Peer Selection and Time switching Ratio Allocation (EPS-TRA) algorithm to solve the proposed mixed integer problem. Numerical results validate our proposed approach in acquiring better EE when compared with Uniform Allocation Scheme of time slots for EH & ID. Furthermore, results explain how EE of the link varies with the choice of constrained variables i.e. data rate and harvested energy.

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QoS enhancement with deep learning-based interference prediction in mobile IoT

Cited by 48 publications

References 22 publications

Cooperative Mobile Learning for the Investigation of Natural Science Courses in Elementary Schools

Cooperative Mobile Learning for the Investigation of Natural Science Courses in Elementary Schools

Dynamic Tasks Scheduling Model of UAV Cluster Based on Flexible Network Architecture

EPS-TRA: Energy Efficient Peer Selection and Time Switching Ratio Allocation for SWIPT-Enabled D2D Communication

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